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Popovici GC, Georgescu CV, Plesea AC, Arbune AA, Cristian G, Arbune M. Prognostic Value of the Brixia Radiological Score in COVID-19 Patients: A Retrospective Study from Romania. Trop Med Infect Dis 2025; 10:130. [PMID: 40423360 DOI: 10.3390/tropicalmed10050130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2025] [Revised: 04/12/2025] [Accepted: 05/08/2025] [Indexed: 05/28/2025] Open
Abstract
The novel coronavirus pandemic, SARS-CoV-2, has a variable clinical spectrum, ranging from asymptomatic to critical forms. High mortality and morbidity rates have been associated with risk factors such as comorbidities, age, sex, and virulence factors specific to viral variants. Material and Methods: We retrospectively evaluated imaging characteristics using the Brixia radiological score in relation to favorable or unfavorable outcomes in adult patients. We included COVID-19 cases, admitted between 2020 and 2022, in a specialized pulmonology hospital with no intensive care unit. We analyzed 380 virologically confirmed COVID-19 cases, with a mean age of 52.8 ± 13.02 years. The mean Brixia radiological score at admission was 5.13 ± 3.56, reflecting predominantly mild-to-moderate pulmonary involvement. Multivariate analysis highlighted the utility of this score as a predictive marker for COVID-19 prognosis, with values >5 correlating with other severity biomarkers, NEWS-2 scores, and a lack of vaccination and hospitalization delay of more than 6 days from symptom onset. Summarizing, the Brixia score is itself an effective tool for screening COVID-19 cases at risk of death for early recognition of clinical deterioration and for decisions regarding appropriate care settings. Promoting vaccination can reduce the severity of radiological lesions, thereby decreasing the risk of death. Technologies based on artificial intelligence could optimize diagnosis and management decisions.
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Affiliation(s)
- George-Cosmin Popovici
- School for Doctoral Studies in Biomedical Sciences, "Dunarea de Jos" University from Galati, 800008 Galati, Romania
- Pneumophtiziology Hospital Galati, 800189 Galati, Romania
| | - Costinela-Valerica Georgescu
- Pharmaceutical Sciences Department, "Dunarea de Jos" University from Galati, 800008 Galati, Romania
- Gynecology and Obstetrics Clinic Hospital Galati, 544886 Galati, Romania
| | | | - Anca-Adriana Arbune
- Neurology Department Clinic Institute Fundeni Bucharest, 022328 Bucharest, Romania
- Multidisciplinary Integrated Center for Dermatological Interface Research, 800010 Galati, Romania
| | - Gutu Cristian
- "Dr. Aristide Serfioti" Military Emergency Hospital, 800008 Galati, Romania
- Medical Clinic Department, "Dunarea de Jos" University from Galati, 800008 Galati, Romania
| | - Manuela Arbune
- Medical Clinic Department, "Dunarea de Jos" University from Galati, 800008 Galati, Romania
- Infectious Diseases Clinic I, Infectious Diseases Clinic Hospital Galati, 800179 Galati, Romania
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Ostrowska M, Kasprzak M, Fabiszak T, Gajda J, Jaje-Rykowska N, Michalski P, Moczulska B, Nowek P, Piasecki M, Pilaczyńska-Cemel M, Podhajski P, Prudzic P, Stępniak D, Świątkowski D, Żechowicz M, Gajda R, Gromadziński L, Kryś J, Kubica A, Przybylski G, Szymański P, Kubica J. The 123 COVID SCORE: A simple and reliable diagnostic tool to predict in-hospital death in COVID-19 patients on hospital admission. PLoS One 2024; 19:e0309922. [PMID: 39436870 PMCID: PMC11495612 DOI: 10.1371/journal.pone.0309922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 08/20/2024] [Indexed: 10/25/2024] Open
Abstract
BACKGROUND Patients hospitalized due to Coronavirus disease 2019 (COVID-19) are still burdened with high risk of death. The aim of this study was to create a risk score predicting in-hospital mortality in COVID-19 patients on hospital admission. METHODS Independent mortality predictors identified in multivariate logistic regression analysis were used to build the 123 COVID SCORE. Diagnostic performance of the score was evaluated using the area under the receiver-operating characteristic curve (AUROC). RESULTS Data from 673 COVID-19 patients with median age of 70 years were used to build the score. In-hospital death occurred in 124 study participants (18.4%). The final score is composed of 3 variables that were found predictive of mortality in multivariate logistic regression analysis: (1) age, (2) oxygen saturation on hospital admission without oxygen supplementation and (3) percentage of lung involvement in chest computed tomography (CT). Four point ranges have been identified: 0-5, 6-8, 9-11 and 12-17, respectively corresponding to low (1.5%), moderate (13.4%), high (28.4%) and very high (57.3%) risk of in-hospital death. The 123 COVID SCORE accuracy measured with the AUROC was 0.797 (95% CI 0.757-0.838; p<0.0001) in the study population and 0.774 (95% CI 0.728-0.821; p<0.0001) in an external validation cohort consisting of 558 COVID-19 patients. CONCLUSIONS The 123 COVID SCORE containing merely 3 variables: age, oxygen saturation, and percentage of lung involvement assessed with chest CT is a simple and reliable tool to predict in-hospital death in COVID-19 patients upon hospital admission.
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Affiliation(s)
| | - Michał Kasprzak
- Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Tomasz Fabiszak
- Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Jacek Gajda
- Gajda-Med District Hospital in Pultusk, Pultusk, Poland
| | - Natalia Jaje-Rykowska
- Department of Cardiology and Internal Medicine, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Piotr Michalski
- Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Beata Moczulska
- Department of Cardiology and Internal Medicine, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Paulina Nowek
- Department of Cardiology and Internal Medicine, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Maciej Piasecki
- Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Marta Pilaczyńska-Cemel
- Department of Lung Diseases, Neoplasms and Tuberculosis, Faculty of Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland
| | | | - Paulina Prudzic
- Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Dominika Stępniak
- Department of Cardiology and Internal Medicine, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | | | - Maciej Żechowicz
- Department of Cardiology and Internal Medicine, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Robert Gajda
- Gajda-Med District Hospital in Pultusk, Pultusk, Poland
| | - Leszek Gromadziński
- Department of Cardiology and Internal Medicine, School of Medicine, Collegium Medicum, University of Warmia and Mazury, Olsztyn, Poland
| | - Jacek Kryś
- Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Aldona Kubica
- Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Grzegorz Przybylski
- Department of Lung Diseases, Neoplasms and Tuberculosis, Faculty of Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland
| | - Paweł Szymański
- Department of Cardiology, Interventional Cardiology and Electrophysiology with Cardiac Intensive Care Unit, Tertiary Care Hospital, Grudziądz, Poland
| | - Jacek Kubica
- Collegium Medicum, Nicolaus Copernicus University, Bydgoszcz, Poland
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Pranskunas A, Zaveckiene J, Baranauskas T, Zakarauskaite B, Zykute D, Tamosuitis T. Early association between respiratory mechanics and radiological changes in mechanically ventilated critically ill patients with COVID-19. Intern Emerg Med 2024; 19:1081-1088. [PMID: 38105407 DOI: 10.1007/s11739-023-03500-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023]
Abstract
The chest X-ray (CXR) Brixia scoring system was developed exclusively for COVID-19 severity assessment. However, no association between the score and respiratory mechanics during mechanical ventilation has been examined. Our aim was to evaluate the association between the CXR Brixia score and respiratory mechanics on the first day of mechanical ventilation in critically ill COVID-19 patients. A total of 77 COVID-19 patients who underwent mechanical ventilation and CXR in the ICU setting were retrospectively included. The CXR Brixia scoring system was applied, and respiratory mechanics data were recorded on the first day of invasive mechanical ventilation. Median Simplified Acute Physiologic Score II (SAPSII) and Sequential Organ Failure Assessment (SOFA) scores were 40 (31-54) and 6 (4-8), respectively. The median Brixia score was 14 (11-16). The correlation between the Brixia score and static compliance or driving pressure was significant, at r = -0.38, p < 0.001 and r = 0.33, p = 0.003, respectively. Using multivariable linear regression, the model with the B zone was significantly better associated with static compliance (F = 11.5, R2 = 0.14, p = 0.001) and driving pressure (F = 11.3, R2 = 0.13, p = 0.001). In logistic regression analysis, the Brixia score (OR 1.24; 95% CI 1.07, 1.45; p = 0.005), B zone (OR 2.60; 95% CI 1.30, 5.20; p = 0.007), C zone (OR 2.50; 95% CI 1.23, 5.11; p = 0.012), A zone (OR 2.01; 95% CI 1.16, 3.44; p = 0.012), and D zone (OR 1.84; 95% CI 1.07, 3.17; p = 0.027) significantly predicted a driving pressure > 14 cmH2O. There is a relationship between changes in Brixia-scored chest X-ray images and compliance and driving pressure on the first day of invasive mechanical ventilation. We identified some CXR areas using the Brixia score, and evaluation of the Brixia score may provide additional information for predicting respiratory mechanics.
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Affiliation(s)
- Andrius Pranskunas
- Department of Intensive Care Medicine, Lithuanian University of Health Sciences, Eiveniu G.2, 50161, Kaunas, Lithuania.
| | - Jurgita Zaveckiene
- Department of Radiology, Lithuanian University of Health Sciences, Eiveniu G.2, 50161, Kaunas, Lithuania
| | - Tautvydas Baranauskas
- Department of Intensive Care Medicine, Lithuanian University of Health Sciences, Eiveniu G.2, 50161, Kaunas, Lithuania
| | - Beatrice Zakarauskaite
- Department of Intensive Care Medicine, Lithuanian University of Health Sciences, Eiveniu G.2, 50161, Kaunas, Lithuania
| | - Dalia Zykute
- Department of Intensive Care Medicine, Lithuanian University of Health Sciences, Eiveniu G.2, 50161, Kaunas, Lithuania
| | - Tomas Tamosuitis
- Department of Intensive Care Medicine, Lithuanian University of Health Sciences, Eiveniu G.2, 50161, Kaunas, Lithuania
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Limratana P, Maisat W, Tsai A, Yuki K. Perioperative Factors and Radiographic Severity Scores for Predicting the Duration of Mechanical Ventilation After Arterial Switch Surgery. J Cardiothorac Vasc Anesth 2024; 38:992-1005. [PMID: 38365467 PMCID: PMC10947876 DOI: 10.1053/j.jvca.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/26/2023] [Accepted: 01/14/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVES Cardiac surgery on cardiopulmonary bypass (CPB) during the neonatal period can cause perioperative organ injuries. The primary aim of this study was to determine the incidence and risk factors associated with postoperative mechanical ventilation duration and acute lung injury after the arterial switch operation (ASO). The secondary aim was to examine the utility of the Brixia score for characterizing postoperative acute lung injury (ALI). DESIGN A retrospective study. SETTING A single-center university hospital. PARTICIPANTS A total of 93 neonates with transposition of great arteries with intact ventricular septum (dTGA IVS) underwent ASO. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS From January 2015 to December 2022, 93 neonates with dTGA IVS were included in the study. The cohort had a median age of 4.0 (3.0-5.0) days and a mean weight of 3.3 ± 0.5 kg. About 63% of patients had ≥48 hours of postoperative mechanical ventilation after ASO. Risk factors included prematurity, post-CPB transfusion of salvaged red cells, platelets and cryoprecipitate, and postoperative fluid balance by univariate analysis. The larger transfused platelet volume was associated with the risk of ALI by multivariate analysis. The median baseline Brixia scores were 11.0 (9.0-12.0) and increased significantly in the postoperative day 1 in patients who developed moderate ALI 24 hours after admission to the intensive care unit (15.0 [13.0-16.0] v 12.0 [10.0-14.0], p = 0.046). CONCLUSIONS Arterial switch operation results in a high incidence of ≥48-hour postoperative mechanical ventilation. Blood component transfusion is a potentially modifiable risk factor. The Brixia scores also may be used to characterize postoperative acute lung injury.
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Affiliation(s)
- Panop Limratana
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, USA
- Department of Anaesthesia, Harvard Medical School, Boston, MA, USA
- Department of Anesthesiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Wiriya Maisat
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, USA
- Department of Anaesthesia, Harvard Medical School, Boston, MA, USA
- Department of Anesthesiology, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Andy Tsai
- Department of Radiology, Boston Children’s Hospital, Boston, MA, USA
| | - Koichi Yuki
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, USA
- Department of Anaesthesia, Harvard Medical School, Boston, MA, USA
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Li T, Li W, Chen F, Xu Q, Du G, Fu Y, Yuan L, Zhang S, Wu W, He P, Xia M. The chest X-ray score baseline in predicting continuous oxygen therapy failure in low-risk aged patients after thoracic surgery. J Thorac Dis 2024; 16:1885-1899. [PMID: 38617782 PMCID: PMC11009605 DOI: 10.21037/jtd-23-1786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Accepted: 02/02/2024] [Indexed: 04/16/2024]
Abstract
Background Radiographic severity assessment can be instrumental in diagnosing postoperative pulmonary complications (PPCs) and guiding oxygen therapy. The radiographic assessment of lung edema (RALE) and Brixia scores correlate with disease severity, but research on low-risk elderly patients is lacking. This study aimed to assess the efficacy of two chest X-ray scores in predicting continuous oxygen therapy (COT) treatment failure in patients over 70 years of age after thoracic surgery. Methods From January 2019 to December 2021, we searched for patients aged 70 years and above who underwent thoracic surgery and received COT treatment, with a focus on those at low risk of respiratory complications. Bedside chest X-rays, RALE, Brixia scores, and patient data were collected. Univariate, multivariate analyses, and 1:2 matching identified risk factors. Receiver operating characteristic (ROC) curves determined score sensitivity, specificity, and predictive values. Results Among the 242 patients surviving to discharge, 19 (7.9%) patients experienced COT failure. COT failure correlated with esophageal cancer surgeries, thoracotomies (36.8% vs. 9%, P=0.003; 26.3% vs. 9.4%, P=0.004), and longer operation time (3.4 vs. 2.8 h, P=0.003). Surgical approach and RALE score were independent risk factors. The prediction model had an area under the curve (AUC) of 0.839 [95% confidence interval (CI), 0.740-0.938]. Brixia and RALE scores predicted COT failure with AUCs of 0.764 (95% CI, 0.650-0.878) with a cut-off value of 6.027 and 0.710 (95% CI, 0.588-0.832) with a cut-off value of 17.134, respectively, after 1:2 matching. Conclusions The RALE score predict the risk of COT failure in elderly, low-risk thoracic patients better than the Brixia score. This simple, cheap, and noninvasive method helps evaluate postoperative lung damage, monitor treatment response, and provide early warning for oxygen therapy escalation. Further studies are required to confirm the validity and applicability of this model in different settings and populations.
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Affiliation(s)
- Tongxin Li
- Department of Thoracic Surgery, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
- Department of Digital Medicine, College of Biomedical Engineering and Medical Imaging, Army Medical University (Third Military Medical University), Chongqing, China
| | - Weina Li
- Department of Thoracic Surgery, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
| | - Fengxi Chen
- Department of Radiology, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
| | - Qianfeng Xu
- Department of Thoracic Surgery, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
| | - Gaoli Du
- Department of Thoracic Surgery, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
| | - Yong Fu
- Department of Cardiothoracic Surgery, Dianjiang People’s Hospital of Chongqing, Chongqing, China
| | - Lihui Yuan
- Department of Thoracic Surgery, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
| | - Sha Zhang
- Department of Thoracic Surgery, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
| | - Wei Wu
- Department of Thoracic Surgery, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
| | - Ping He
- Department of Cardiac Surgery, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
| | - Mei Xia
- Department of Thoracic Surgery, First Affiliated Hospital of the Army Medical University, Army Medical University (Third Military Medical University), Chongqing, China
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Kusumoto T, Chubachi S, Namkoong H, Tanaka H, Lee H, Otake S, Nakagawara K, Fukushima T, Morita A, Watase M, Asakura T, Masaki K, Kamata H, Ishii M, Hasegawa N, Harada N, Ueda T, Ueda S, Ishiguro T, Arimura K, Saito F, Yoshiyama T, Nakano Y, Mutoh Y, Suzuki Y, Edahiro R, Murakami K, Sato Y, Okada Y, Koike R, Kitagawa Y, Tokunaga K, Kimura A, Imoto S, Miyano S, Ogawa S, Kanai T, Fukunaga K. Characteristics of patients with COVID-19 who have deteriorating chest X-ray findings within 48 h: a retrospective cohort study. Sci Rep 2023; 13:22054. [PMID: 38086863 PMCID: PMC10716517 DOI: 10.1038/s41598-023-49340-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 12/07/2023] [Indexed: 12/18/2023] Open
Abstract
The severity of chest X-ray (CXR) findings is a prognostic factor in patients with coronavirus disease 2019 (COVID-19). We investigated the clinical and genetic characteristics and prognosis of patients with worsening CXR findings during early hospitalization. We retrospectively included 1656 consecutive Japanese patients with COVID-19 recruited through the Japan COVID-19 Task Force. Rapid deterioration of CXR findings was defined as increased pulmonary infiltrates in ≥ 50% of the lung fields within 48 h of admission. Rapid deterioration of CXR findings was an independent risk factor for death, most severe illness, tracheal intubation, and intensive care unit admission. The presence of consolidation on CXR, comorbid cardiovascular and chronic obstructive pulmonary diseases, high body temperature, and increased serum aspartate aminotransferase, potassium, and C-reactive protein levels were independent risk factors for rapid deterioration of CXR findings. Risk variant at the ABO locus (rs529565-C) was associated with rapid deterioration of CXR findings in all patients. This study revealed the clinical features, genetic features, and risk factors associated with rapid deterioration of CXR findings, a poor prognostic factor in patients with COVID-19.
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Affiliation(s)
- Tatsuya Kusumoto
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
| | - Ho Namkoong
- Department of Infectious Diseases, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan.
| | - Hiromu Tanaka
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Ho Lee
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Shiro Otake
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Kensuke Nakagawara
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Takahiro Fukushima
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Atsuho Morita
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Mayuko Watase
- Department of Respiratory Medicine, National Hospital Organization Tokyo Medical Center, Tokyo, Japan
| | - Takanori Asakura
- Department of Pulmonary Medicine, Saitama City Hospital, Saitama, Japan
| | - Katsunori Masaki
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Hirofumi Kamata
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Makoto Ishii
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Naoki Hasegawa
- Department of Infectious Diseases, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Norihiro Harada
- Department of Respiratory Medicine, Juntendo University Faculty of Medicine and Graduate School of Medicine, Tokyo, Japan
| | - Tetsuya Ueda
- Department of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan
| | - Soichiro Ueda
- Department of Internal Medicine, Japan Community Health Care Organization (JCHO), Saitama Medical Center, Saitama, Japan
| | - Takashi Ishiguro
- Department of Respiratory Medicine, Saitama Cardiovascular and Respiratory Center, Kumagaya, Japan
| | - Ken Arimura
- Department of Respiratory Medicine, Tokyo Women's Medical University, Tokyo, Japan
| | - Fukuki Saito
- Department of Emergency and Critical Care Medicine, Kansai Medical University General Medical Center, Moriguchi, Japan
| | - Takashi Yoshiyama
- Respiratory Disease Center, Fukujuji Hospital, Japan Anti-Tuberculosis Association, Tokyo, Japan
| | - Yasushi Nakano
- Department of Internal Medicine, Kawasaki Municipal Ida Hospital, Kawasaki, Japan
| | - Yoshikazu Mutoh
- Department of Infectious Diseases, Tosei General Hospital, Seto, Japan
| | - Yusuke Suzuki
- Department of Respiratory Medicine, Kitasato University Kitasato Institute Hospital, Tokyo, Japan
| | - Ryuya Edahiro
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Koji Murakami
- Department of Respiratory Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Yasunori Sato
- Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- The Center for Infectious Disease Education and Research (CiDER), Osaka University, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
| | - Ryuji Koike
- Medical Innovation Promotion Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yuko Kitagawa
- Department of Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project (Toyama), National Center for Global Health and Medicine, Tokyo, Japan
| | - Akinori Kimura
- Institute of Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan
| | - Seishi Ogawa
- Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institute, Stockholm, Sweden
| | - Takanori Kanai
- Division of Gastroenterology and Hepatology, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Koichi Fukunaga
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-8582, Japan
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Benatti SV, Venturelli S, Crotti G, Ghirardi A, Binda F, Savardi M, Previtali G, Seghezzi M, Marozzi R, Corsi A, Bonaffini PA, Gori M, Falanga A, Signoroni A, Alessio MG, Zucchi A, Barbui T, Rizzi M. Clinical variables associated with late-onset thrombotic and cardiovascular events, after SARS-CoV-2 infection, in a cohort of patients from the first epidemic wave: an 18-month analysis on the "Surviving-COVID" cohort from Bergamo, Italy. Front Cardiovasc Med 2023; 10:1280584. [PMID: 38099229 PMCID: PMC10720075 DOI: 10.3389/fcvm.2023.1280584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 11/08/2023] [Indexed: 12/17/2023] Open
Abstract
Importance Population studies have recorded an increased, unexplained risk of post-acute cardiovascular and thrombotic events, up to 1 year after acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Objectives To search for clinical variables and biomarkers associated with late post-acute thrombotic and cardiovascular events after SARS-CoV-2 infection. Design Retrospective cohort study. Setting Third-level referral hospital in Bergamo (Italy). Participants Analysis of an existing database of adult patients, who received care for SARS-CoV-2 infection at our institution between 20 February and 30 September 2020, followed up on a single date ("entry date") at 3-6 months. Exposure Initial infection by SARS-CoV-2. Main outcomes and measures Primary outcome: occurrence, in the 18 months after entry date, of a composite endpoint, defined by the International Classification of Diseases-9th edition (ICD-9) codes for at least one of: cerebral/cardiac ischemia, venous/arterial thrombosis (any site), pulmonary embolism, cardiac arrhythmia, heart failure. Measures (as recorded on entry date): history of initial infection, symptoms, current medications, pulmonary function test, blood tests results, and semi-quantitative radiographic lung damage (BRIXIA score). Individual clinical data were matched to hospitalizations, voluntary vaccination against SARS-CoV-2 (according to regulations and product availability), and documented reinfections in the following 18 months, as recorded in the provincial Health Authority database. A multivariable Cox proportional hazard model (including vaccine doses as a time-dependent variable) was fitted, adjusting for potential confounders. We report associations as hazard ratios (HR) and 95% confidence intervals (CI). Results Among 1,515 patients (948 men, 62.6%, median age 59; interquartile range: 50-69), we identified 84 endpoint events, occurring to 75 patients (5%): 30 arterial thromboses, 11 venous thromboses, 28 arrhythmic and 24 heart failure events. From a multivariable Cox model, we found the following significant associations with the outcome: previous occurrence of any outcome event, in the 18 months before infection (HR: 2.38; 95% CI: 1.23-4.62); BRIXIA score ≥ 3 (HR: 2.43; 95% CI: 1.30-4.55); neutrophils-to-lymphocytes ratio ≥ 3.3 (HR: 2.60; 95% CI: 1.43-4.72), and estimated glomerular filtration rate < 45 ml/min/1.73 m2 (HR: 3.84; 95% CI: 1.49-9.91). Conclusions and relevance We identified four clinical variables, associated with the occurrence of post-acute thrombotic and cardiovascular events, after SARS-CoV-2 infection. Further research is needed, to confirm these results.
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Affiliation(s)
- S. V. Benatti
- Infectious Diseases Unit, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - S. Venturelli
- Infectious Diseases Unit, ASST Papa Giovanni XXIII, Bergamo, Italy
- Scuola di Medicina, Università degli Studi di Milano-Bicocca, Milano, Italy
| | - G. Crotti
- ATS Bergamo, Ufficio Epidemiologico, Bergamo, Italy
| | - A. Ghirardi
- Fondazione per la Ricerca Ospedale di Bergamo (FROM)—ETS, Bergamo, Italy
| | - F. Binda
- Infectious Diseases Unit, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - M. Savardi
- Dipartimento di Specialità Medico Chirurgiche, Scienze Radiologiche e Sanità Pubblica, Università Degli Studi di Brescia, Brescia, Italy
| | - G. Previtali
- Central Laboratory, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - M. Seghezzi
- Central Laboratory, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - R. Marozzi
- Central Laboratory, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - A. Corsi
- Scuola di Medicina, Università degli Studi di Milano-Bicocca, Milano, Italy
- Scuola di Specializzazione in Radiologia, Università Degli Studi di Milano-Bicocca, Milano, Italy
| | - P. A. Bonaffini
- Scuola di Medicina, Università degli Studi di Milano-Bicocca, Milano, Italy
- Radiology Unit, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - M. Gori
- Cardiology Unit, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - A. Falanga
- Scuola di Medicina, Università degli Studi di Milano-Bicocca, Milano, Italy
- Immunohematology and Transfusion Medicine, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - A. Signoroni
- Dipartimento di Specialità Medico Chirurgiche, Scienze Radiologiche e Sanità Pubblica, Università Degli Studi di Brescia, Brescia, Italy
| | - M. G. Alessio
- Central Laboratory, ASST Papa Giovanni XXIII, Bergamo, Italy
| | - A. Zucchi
- ATS Bergamo, Ufficio Epidemiologico, Bergamo, Italy
| | - T. Barbui
- Fondazione per la Ricerca Ospedale di Bergamo (FROM)—ETS, Bergamo, Italy
| | - M. Rizzi
- Infectious Diseases Unit, ASST Papa Giovanni XXIII, Bergamo, Italy
- Scuola di Medicina, Università degli Studi di Milano-Bicocca, Milano, Italy
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Paraskevas T, Dimopoulos PM, Kantanis A, Garatzioti AS, Karalis I, Michailides C, Chourpiliadi C, Matthaiakaki E, Kalogeropoulou C, Velissaris D. Evaluation of Reliability and Validity of the RALE and BRIXIA Chest-X Ray Scores in Patients Hospitalized with COVID-19 Pneumonia. ROMANIAN JOURNAL OF INTERNAL MEDICINE = REVUE ROUMAINE DE MEDECINE INTERNE 2023; 61:141-146. [PMID: 37249556 DOI: 10.2478/rjim-2023-0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Indexed: 05/31/2023]
Abstract
INTRODUCTION Chest X-rays are commonly used to assess the severity in patients that present in the emergency department with suspected COVID-19 pneumonia, but in clinical practice quantitative scales are rarely employed. AIMS To evaluate the reliability and validity of two semi-quantitative radiological scales in patients hospitalized for COVID-19 pneumonia (BRIXIA score and RALE score). METHODS Patients hospitalized between October 2021 and March 2022 with confirmed COVID-19 pneumonia diagnosis were eligible for inclusion. All included patients had a chest X-ray taken in the ED before admission. Three raters that participated in the treatment and management of patients with COVID-19 during the pandemic independently assessed chest X-rays. RESULTS Intraclass coefficients for BRIXΙA and RALES was 0.781 (0.729-0.826) and 0.825 (0.781-0.862) respectively, showing good to excellent reliability overall. Pairwise analysis was performed using quadratic weighted kappa showing significant variability in the inter-rater agreement. The prognostic accuracy of the two scores for in-hospital mortality for all raters was between 0.753 and 0.763 for BRIXIA and 0.737 and 0.790 for RALES, demonstrating good to excellent prognostic value. Both radiological scores were significantly associated with inhospital mortality after adjustment for 4C Mortality score. We found a consistent upwards trend with significant differences between severity groups in both radiological scores. CONCLUSION Our findings suggest that BRIXIA and RALES are reliable and can be used to assess the prognosis of patients with COVID-19 requiring hospitalization. However, the inherent subjectivity of radiological scores might make it difficult to set a cut-off value suitable for all assessors.
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Affiliation(s)
| | - Platon M Dimopoulos
- 2Department of Radiology, General University Hospital of Patras, Patras, Greece
| | - Anastasios Kantanis
- 3Department of General Practice and Family Medicine, General University Hospital of Patras, Patras, Greece
| | | | - Iosif Karalis
- 1Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
| | - Christos Michailides
- 1Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
| | | | - Evgenia Matthaiakaki
- 1Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
| | | | - Dimitrios Velissaris
- 1Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
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9
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Plasencia-Martínez JM, Moreno-Pastor A, Lozano-Ros M, Jiménez-Pulido C, Herves-Escobedo I, Pérez-Hernández G, García-Santos JM. Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis. Emerg Radiol 2023; 30:465-474. [PMID: 37358654 DOI: 10.1007/s10140-023-02153-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/19/2023] [Indexed: 06/27/2023]
Abstract
PURPOSE Diagnosing pneumonia by radiograph is improvable. We aimed (a) to compare radiograph and digital thoracic tomosynthesis (DTT) performances and agreement for COVID-19 pneumonia diagnosis, and (b) to assess the DTT ability for COVID-19 diagnosis when polymerase chain reaction (PCR) and radiograph are negative. METHODS Two emergency radiologists with 11 (ER1) and 14 experience-years (ER2) retrospectively evaluated radiograph and DTT images acquired simultaneously in consecutively clinically suspected COVID-19 pneumonia patients in March 2020-January 2021. Considering PCR and/or serology as reference standard, DTT and radiograph diagnostic performance and interobserver agreement, and DTT contributions in unequivocal, equivocal, and absent radiograph opacities were analysed by the area under the curve (AUC), Cohen's Kappa, Mc-Nemar's and Wilcoxon tests. RESULTS We recruited 480 patients (49 ± 15 years, 277 female). DTT increased ER1 (from 0.76, CI95% 0.7-0.8 to 0.79, CI95% 0.7-0.8; P=.04) and ER2 (from 0.77 CI95% 0.7-0.8 to 0.80 CI95% 0.8-0.8, P=.02) radiograph-AUCs, sensitivity, specificity, predictive values, and positive likelihood ratio. In false negative microbiological cases, DTT suggested COVID-19 pneumonia in 13% (4/30; P=.052, ER1) and 20% (6/30; P=.020, ER2) more than radiograph. DTT showed new or larger opacities in 33-47% of cases with unequivocal opacities in radiograph, new opacities in 2-6% of normal radiographs and reduced equivocal opacities by 13-16%. Kappa increased from 0.64 (CI95% 0.6-0.8) to 0.7 (CI95% 0.7-0.8) for COVID-19 pneumonia probability, and from 0.69 (CI95% 0.6-0.7) to 0.76 (CI95% 0.7-0.8) for pneumonic extension. CONCLUSION DTT improves radiograph performance and agreement for COVID-19 pneumonia diagnosis and reduces PCR false negatives.
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Affiliation(s)
| | | | | | | | | | - Gloria Pérez-Hernández
- Hospital Universitario Morales Meseguer, 30008, Murcia, ZC, Spain
- Current affiliation: Hospital Clínico, 50009, Zaragoza, ZC, Spain
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10
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Sun Y, Salerno S, He X, Pan Z, Yang E, Sujimongkol C, Song J, Wang X, Han P, Kang J, Sjoding MW, Jolly S, Christiani DC, Li Y. Use of machine learning to assess the prognostic utility of radiomic features for in-hospital COVID-19 mortality. Sci Rep 2023; 13:7318. [PMID: 37147440 PMCID: PMC10161188 DOI: 10.1038/s41598-023-34559-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 05/03/2023] [Indexed: 05/07/2023] Open
Abstract
As portable chest X-rays are an efficient means of triaging emergent cases, their use has raised the question as to whether imaging carries additional prognostic utility for survival among patients with COVID-19. This study assessed the importance of known risk factors on in-hospital mortality and investigated the predictive utility of radiomic texture features using various machine learning approaches. We detected incremental improvements in survival prognostication utilizing texture features derived from emergent chest X-rays, particularly among older patients or those with a higher comorbidity burden. Important features included age, oxygen saturation, blood pressure, and certain comorbid conditions, as well as image features related to the intensity and variability of pixel distribution. Thus, widely available chest X-rays, in conjunction with clinical information, may be predictive of survival outcomes of patients with COVID-19, especially older, sicker patients, and can aid in disease management by providing additional information.
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Affiliation(s)
- Yuming Sun
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Stephen Salerno
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Xinwei He
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Ziyang Pan
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Eileen Yang
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Chinakorn Sujimongkol
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jiyeon Song
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Xinan Wang
- Department of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
| | - Peisong Han
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA
| | - Michael W Sjoding
- Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan Medical School, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Shruti Jolly
- Department of Radiation Oncology, University of Michigan Rogel Cancer Center, 1500 East Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - David C Christiani
- Department of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care, Department of Internal Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Yi Li
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI, 48109, USA.
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11
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Dua R, Malik S, Kumari R, Naithani M, Panda PK, Saroha A, Omar B, Pathania M, Saxena S. The Role of Yoga in Hospitalized COVID-19 Patients: An Exploratory Randomized Controlled Trial. Cureus 2023; 15:e39320. [PMID: 37351243 PMCID: PMC10282501 DOI: 10.7759/cureus.39320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 06/24/2023] Open
Abstract
Introduction The unpredictable course and sheer magnitude of coronavirus disease 2019 (COVID-19) have sparked a search for novel and repurposed pharmacological interventions. Non-pharmacological interventions may also play a role in the management of this multifaceted disease. This study aimed to evaluate the safety, feasibility, and effect of yoga in hospitalized patients with moderate COVID-19. Methods Twenty patients satisfying the inclusion criterion were randomized (1:1 ratio) into Intervention and Control groups. Patients in the intervention arm performed a one-hour yoga session that included pranayama and Gayatri mantra (GM) chant for up to 14 days. Sessions were fully supervised by a trained yoga trainer via an online platform. Patients in both groups received the normal treatment as per national guidelines. Outcome parameters were recorded on the 14th day/end of the hospital stay. Results Yoga is safe and feasible in hospitalized patients with COVID-19. The decline of high-sensitivity C-reactive protein (hs-CRP) levels was significantly greater in the Intervention Group. Quality of life (QOL), depression, anxiety, and fatigue severity scale (FSS) showed a decline in both groups with a significant decline observed in FSS scores of the Intervention Group. Median chest X-ray score values, duration of hospital stay, and reverse transcription-polymerase chain reaction (RT-PCR) conversion days were observed to be lower in the Intervention Group but were not significant (p>0.05). Conclusion The study found that incorporating pranayama and GM practices in hospitalized patients with moderate COVID-19 pneumonia was safe and feasible. It showed a notable reduction in hs-CRP levels and FSS scores in the Intervention Group, but the study was not powered to detect statistically significant results. Further research with larger sample sizes is needed for conclusive findings.
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Affiliation(s)
- Ruchi Dua
- Pulmonary Medicine, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Saloni Malik
- Pulmonary Medicine, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Ranjeeta Kumari
- Community and Family Medicine, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Manisha Naithani
- Biochemistry, Advanced Center of Continuous Professional Development, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Prasan K Panda
- Internal Medicine, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Amit Saroha
- Radiology, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Balram Omar
- Microbiology, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Monika Pathania
- Medicine, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
| | - Sudhir Saxena
- Radiodiagnosis, All India Institute of Medical Sciences, Rishikesh, Rishikesh, IND
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12
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Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability. Sci Rep 2023; 13:4226. [PMID: 36918593 PMCID: PMC10012307 DOI: 10.1038/s41598-023-30505-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
In the past few years COVID-19 posed a huge threat to healthcare systems around the world. One of the first waves of the pandemic hit Northern Italy severely resulting in high casualties and in the near breakdown of primary care. Due to these facts, the Covid CXR Hackathon-Artificial Intelligence for Covid-19 prognosis: aiming at accuracy and explainability challenge had been launched at the beginning of February 2022, releasing a new imaging dataset with additional clinical metadata for each accompanying chest X-ray (CXR). In this article we summarize our techniques at correctly diagnosing chest X-ray images collected upon admission for severity of COVID-19 outcome. In addition to X-ray imagery, clinical metadata was provided and the challenge also aimed at creating an explainable model. We created a best-performing, as well as, an explainable model that makes an effort to map clinical metadata to image features whilst predicting the prognosis. We also did many ablation studies in order to identify crucial parts of the models and the predictive power of each feature in the datasets. We conclude that CXRs at admission do not help the predicting power of the metadata significantly by itself and contain mostly information that is also mutually present in the blood samples and other clinical factors collected at admission.
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13
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Venugopalan Nair A, Kumar D, McInnes M, Hadi AA, Valiyakath Subair HS, Khyatt OA, Almashhadani MA, Jacob B, Vasudevan A, Ashruf MZ, Al-Heidous M, Kuttikatt Soman D. Utility of chest radiograph severity scoring in emergency department for predicting outcomes in COVID-19: A study of 1275 patients. Clin Imaging 2023; 95:65-70. [PMID: 36623355 PMCID: PMC9794386 DOI: 10.1016/j.clinimag.2022.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/07/2022] [Accepted: 12/07/2022] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To measure the reliability and reproducibility of a chest radiograph severity score (CSS) in prognosticating patient's severity of disease and outcomes at the time of disease presentation in the emergency department (ED) with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS We retrospectively studied 1275 consecutive RT-PCR confirmed COVID-19 adult patients presenting to ED from March 2020 through June 2020. Chest radiograph severity score was assessed for each patient by two blinded radiologists. Clinical and laboratory parameters were collected. The rate of admission to intensive care unit, mechanical ventilation or death up to 60 days after the baseline chest radiograph were collected. Primary outcome was defined as occurrence of ICU admission or death. Multivariate logistic regression was performed to evaluate the relationship between clinical parameters, chest radiograph severity score, and primary outcome. RESULTS CSS of 3 or more was associated with ICU admission (78 % sensitivity; 73.1 % specificity; area under curve 0.81). CSS and pre-existing diabetes were independent predictors of primary outcome (odds ratio, 7; 95 % CI: 3.87, 11.73; p < 0.001 & odds ratio, 2; 95 % CI: 1-3.4, p 0.02 respectively). No significant difference in primary outcome was observed for those with history of hypertension, asthma, chronic kidney disease or coronary artery disease. CONCLUSION Semi-quantitative assessment of CSS at the time of disease presentation in the ED predicted outcomes in adults of all age with COVID-19.
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Affiliation(s)
- Anirudh Venugopalan Nair
- Dept of Clinical Radiology, NHS Salisbury Foundation Trust, Wiltshire, United Kingdom; Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar.
| | - Devendra Kumar
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
| | - Matthew McInnes
- The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Ahmed Akram Hadi
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
| | | | - Omar Ammar Khyatt
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
| | | | - Bamil Jacob
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
| | | | | | - Mahmoud Al-Heidous
- Dept of Clinical Imaging, Al Wakra hospital, Hamad Medical Corporation, Qatar
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14
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Lee HW, Yang HJ, Kim H, Kim UH, Kim DH, Yoon SH, Ham SY, Nam BD, Chae KJ, Lee D, Yoo JY, Bak SH, Kim JY, Kim JH, Kim KB, Jung JI, Lim JK, Lee JE, Chung MJ, Lee YK, Kim YS, Lee SM, Kwon W, Park CM, Kim YH, Jeong YJ, Jin KN, Goo JM. Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study. J Med Internet Res 2023; 25:e42717. [PMID: 36795468 PMCID: PMC9937110 DOI: 10.2196/42717] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/12/2022] [Accepted: 01/11/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19. OBJECTIVE We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19. METHODS This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration. RESULTS The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859). CONCLUSIONS The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.
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Affiliation(s)
- Hyun Woo Lee
- Division of Respiratory and Critical Care, Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Hyun Jun Yang
- College of Medicine, Seoul National University, Seoul, Republic of Korea
| | - Hyungjin Kim
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Ue-Hwan Kim
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
| | - Dong Hyun Kim
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Soon Ho Yoon
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Soo-Youn Ham
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Bo Da Nam
- Department of Radiology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Republic of Korea
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine, Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Dabee Lee
- Department of Radiology, Dankook University Hospital, Cheonan, Republic of Korea
| | - Jin Young Yoo
- Department of Radiology, Chungbuk National University Hospital, Cheongju, Republic of Korea
| | - So Hyeon Bak
- Department of Radiology, Kangwon National University Hospital, Kangwon National University School of Medicine, Chuncheon, Republic of Korea
| | - Jin Young Kim
- Department of Radiology, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Republic of Korea
| | - Jin Hwan Kim
- Department of Radiology, Chungnam National University Hospital, College of Medicine, Daejeon, Republic of Korea
| | - Ki Beom Kim
- Department of Radiology, Daegu Fatima Hospital, Daegu, Republic of Korea
| | - Jung Im Jung
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Jae-Kwang Lim
- Department of Radiology, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - Jong Eun Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Myung Jin Chung
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Young Kyung Lee
- Department of Radiology, Seoul Medical Center, Seoul, Republic of Korea
| | - Young Seon Kim
- Department of Radiology, Yeungnam University Hospital, Yeungnam University College of Medicine, Daegu, Republic of Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woocheol Kwon
- Department of Radiology, Ewha Womans University Seoul Hospital, Seoul, Republic of Korea
| | - Chang Min Park
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Yun-Hyeon Kim
- Department of Radiology, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Yeon Joo Jeong
- Department of Radiology, Pusan National University Hospital, Pusan National University School of Medicine and Biomedical Research Institute, Busan, Republic of Korea
| | - Kwang Nam Jin
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Jin Mo Goo
- College of Medicine, Seoul National University, Seoul, Republic of Korea
- Department of Radiology, Seoul National University Medical Research Center, Seoul, Republic of Korea
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15
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Labuschagne HC, Venturas J, Moodley H. Risk stratification of hospital admissions for COVID-19 pneumonia by chest radiographic scoring in a Johannesburg tertiary hospital. S Afr Med J 2023; 113:75-83. [PMID: 36757072 DOI: 10.7196/samj.2023.v113i2.16681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Chest radiographic scoring systems for COVID-19 pneumonia have been developed. However, little is published on the utilityof these scoring systems in low- and middle-income countries. OBJECTIVES To perform risk stratification of COVID-19 pneumonia in Johannesburg, South Africa (SA), by comparing the Brixia score withclinical parameters, disease course and clinical outcomes. To assess inter-rater reliability and developing predictive models of the clinicaloutcome using the Brixia score and clinical parameters. METHODS Retrospective investigation was conducted of adult participants with established COVID-19 pneumonia admitted at a tertiaryinstitution from 1 May to 30 June 2020. Two radiologists, blinded to clinical data, assigned Brixia scores. Brixia scores were compared withclinical parameters, length of stay and clinical outcomes (discharge/death). Inter-rater agreement was determined. Multivariable logisticregression extracted variables predictive of in-hospital demise. RESULTS The cohort consisted of 263 patients, 51% male, with a median age of 47 years (interquartile range (IQR) = 20; 95% confidenceinterval (CI) 46.5 - 49.9). Hypertension (38.4%), diabetes (25.1%), obesity (19.4%) and HIV (15.6%) were the most common comorbidities.The median length of stay for 258 patients was 7.5 days (IQR = 7; 95% CI 8.2 - 9.7) and 6.5 days (IQR = 8; 95% CI 6.5 - 12.5) for intensivecare unit stay. Fifty (19%) patients died, with a median age of 55 years (IQR = 23; 95% CI 50.5 - 58.7) compared with survivors, of medianage 46 years (IQR = 20; 95% CI 45 - 48.6) (p=0.01). The presence of one or more comorbidities resulted in a higher death rate (23% v. 9.2%;p=0.01) than without comorbidities. The median Brixia score for the deceased was higher (14.5) than for the discharged patients (9.0)(p<0.001). Inter-rater agreement for Brixia scores was good (intraclass correlation coefficient 0.77; 95% CI 0.6 - 0.85; p<0.001). A modelcombining Brixia score, age, male gender and obesity (sensitivity 84%; specificity 63%) as well as a model with Brixia score and C-reactiveprotein (CRP) count (sensitivity 81%; specificity 63%) conferred the highest risk for in-hospital mortality. CONCLUSION We have demonstrated the utility of the Brixia scoring system in a middle-income country setting and developed the first SArisk stratification models incorporating comorbidities and a serological marker. When used in conjunction with age, male gender, obesityand CRP, the Brixia scoring system is a promising and reliable risk stratification tool. This may help inform the clinical decision pathway inresource-limited settings like ours during future waves of COVID-19.
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Affiliation(s)
- H C Labuschagne
- Department of Radiology, Charlotte Maxeke Johannesburg Academic Hospital, and Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - J Venturas
- Department of Internal Medicine, Charlotte Maxeke Johannesburg Academic Hospital, and Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Department of Respiratory Medicine, Waikato District Health Board, Hamilton, New Zealand.
| | - H Moodley
- Department of Radiology, Charlotte Maxeke Johannesburg Academic Hospital, and Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
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16
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Rao Y, Cao W, Qu J, Zhang X, Wang J, Wang J, Li G, Li D, Pei Y, Xu W, Gai X, Sun Y. More severe lung lesions in smoker patients with active pulmonary tuberculosis were associated with peripheral NK cell subsets. Tuberculosis (Edinb) 2023; 138:102293. [PMID: 36549189 DOI: 10.1016/j.tube.2022.102293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 12/01/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND Both pulmonary tuberculosis (PTB) and cigarette smoke (CS) exposure may lead to lung damage. The potential impact of CS exposure on tuberculosis-associated lung damage and the disturbance of immune cells and mediators involved, need to be further elucidated. METHODS We firstly evaluated the chest X-ray (CXR) scores of a retrospective cohort of male patients with active PTB, followed for 6 months, and compared the scores between smoker (≥10 pack-years) and non-smoker patients. In a cross-sectional study, we measured the peripheral blood NK cell subsets and plasma inflammatory cytokines in male smoker and non-smoker patients with active PTB before anti-tuberculosis therapy, and the proportions of NK cell subsets and the levels of cytokines were analyzed for correlation with the CXR scores. RESULTS In the retrospective cohort, male smoker patients with active PTB showed a higher CXR score, characterized by more cavitary lesions, enlarged lymph nodes and emphysema, as compared to non-smokers. The cross-sectional study revealed that the CXR score in smoker patients was correlated inversely with the percentages of blood CD107a+, NKP46+, and TIGIT+ NK cells. CONCLUSION In patients with active PTB, CS exposure was associated with more severe lung lesions, which were correlated with peripheral NK cell subsets.
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Affiliation(s)
- Yafei Rao
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Research Center for Chronic Airway Diseases, Peking University Health Science Center, Beijing, China
| | - Wenli Cao
- Beijing Geriatric Hospital, Beijing, China
| | - Jingge Qu
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Research Center for Chronic Airway Diseases, Peking University Health Science Center, Beijing, China
| | - Xueyang Zhang
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Research Center for Chronic Airway Diseases, Peking University Health Science Center, Beijing, China
| | - Jun Wang
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Research Center for Chronic Airway Diseases, Peking University Health Science Center, Beijing, China
| | | | - Gen Li
- Beijing Geriatric Hospital, Beijing, China
| | - Danyang Li
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Research Center for Chronic Airway Diseases, Peking University Health Science Center, Beijing, China
| | - Yuqiang Pei
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Research Center for Chronic Airway Diseases, Peking University Health Science Center, Beijing, China
| | - Wei Xu
- Beijing Geriatric Hospital, Beijing, China
| | - Xiaoyan Gai
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Research Center for Chronic Airway Diseases, Peking University Health Science Center, Beijing, China.
| | - Yongchang Sun
- Department of Respiratory and Critical Care Medicine, Peking University Third Hospital, Research Center for Chronic Airway Diseases, Peking University Health Science Center, Beijing, China.
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17
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Leidi F, Boari GEM, Scarano O, Mangili B, Gorla G, Corbani A, Accordini B, Napoli F, Ghidelli C, Archenti G, Turini D, Saottini M, Guarinoni V, Ferrari-Toninelli G, Manzoni F, Bonetti S, Chiarini G, Malerba P, Braglia-Orlandini F, Bianco G, Faustini C, Agabiti-Rosei C, De Ciuceis C, Rizzoni D. Comparison of the characteristics, morbidity and mortality of COVID-19 between first and second/third wave in a hospital setting in Lombardy: a retrospective cohort study. Intern Emerg Med 2022; 17:1941-1949. [PMID: 35809152 PMCID: PMC9521559 DOI: 10.1007/s11739-022-03034-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 06/12/2022] [Indexed: 01/08/2023]
Abstract
Coronavirus disease 2019 (COVID-19) represents a major health problem in terms of deaths and long-term sequelae. We conducted a retrospective cohort study at Montichiari Hospital (Brescia, Italy) to better understand the determinants of outcome in two different COVID-19 outbreaks. A total of 634 unvaccinated patients admitted from local emergency room to the Internal Medicine ward with a confirmed diagnosis of SARS-CoV-2 infection and a moderate-to-severe COVID-19 were included in the study. A group of 260 consecutive patients during SARS-CoV-2 first wave (from February to May 2020) and 374 consecutive patients during SARS-CoV-2 2nd/3rd wave (from October 2020 to May 2021) were considered. Demographic data were not significantly different between waves, except a lower prevalence of female sex during first wave. Mortality was significantly higher during the 1st wave than in the following periods (24.2% vs. 11%; p < 0.001). Time from symptoms onset to hospital admission was longer during first wave (8 ± 6 vs. 6 ± 4 days; p < 0.001), while in-hospital staying was significantly shorter (10 ± 14 vs. 15 ± 11 days; p < 0.001). Other significant differences were a larger use of corticosteroids and low-molecular weight heparin as well less antibiotic prescription during the second wave. Respiratory, bio-humoral and X-ray scores were significantly poorer at the time of admission in first-wave patients. After a multivariate regression analysis, C-reactive protein and procalcitonin values, % fraction of inspired oxygen on admission to the Internal Medicine ward and length of hospital stay and duration of symptoms were the strongest predictors of outcome. Concomitant anti-hypertensive treatment (including ACE-inhibitors and angiotensin-receptor blockers) did not affect the outcome. In conclusion, our data suggest that earlier diagnosis, timely hospital admission and rational use of the therapeutic options reduced the systemic inflammatory response and were associated to a better outcome during the 2nd/3rd wave.
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Affiliation(s)
- Francesca Leidi
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | | | - Ottavio Scarano
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Benedetta Mangili
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Giulia Gorla
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Andrea Corbani
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Beatrice Accordini
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Federico Napoli
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Chiara Ghidelli
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Giulia Archenti
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Daniele Turini
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
| | - Michele Saottini
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
| | - Vittoria Guarinoni
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
| | | | - Francesca Manzoni
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
| | - Silvia Bonetti
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Giulia Chiarini
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Paolo Malerba
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Federico Braglia-Orlandini
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Gianluca Bianco
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Cristina Faustini
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Claudia Agabiti-Rosei
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Carolina De Ciuceis
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy
| | - Damiano Rizzoni
- Division of Medicine, Spedali Civili di Brescia, Montichiari, Brescia, Italy.
- Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili Di Brescia, Piazza Spedali Civili 1, 25100, Brescia, Italy.
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18
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Elia D, Mozzanica F, Caminati A, Giana I, Carli L, Ambrogi F, Zompatori M, Harari S. Prognostic value of radiological index and clinical data in patients with COVID-19 infection. Intern Emerg Med 2022; 17:1679-1687. [PMID: 35596103 PMCID: PMC9122253 DOI: 10.1007/s11739-022-02985-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 04/05/2022] [Indexed: 01/08/2023]
Abstract
During the Coronavirus-19 pandemic, chest X-ray scoring system have been validated by Al-Smadi and Toussie in this group of patients and even RALE score, previously designed for ARDS, have been used to estimate correlation with mortality. The aim of this study was to evaluate the prognostic value of As-Smadi, Tuossie and RALE scores in predicting death in the same population of patients when associated to clinical data. In this retrospective clinical study, data of patients with COVID-19, admitted to our hospital from 1st October 2020 to 31st December 2020 were collected. CXR images of each patient were analyzed with the three different scores above mentioned. 144 patients (male 96 aged 68.5 years) were included in the study. 93 patients reported a least 1 comorbidity and 36 died. The association with increasing age, presence of comorbidities, and lower hemoglobin was significantly associated with risk of death for all the regression models. When considering the radiological score, a significant effect was found for the Al Smadi and RALE scores, while no evidence of association was found for the Toussie score. The fraction of new information is 16.7% for the Al Smadi score, 12.9% for the RALE and 5.1% for the Toussie score. The improvement in the prognostic usefulness with respect to the base model is particularly interesting for the Al Smadi score. The highest c-index was also obtained by the model with the Al Smadi score.
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Affiliation(s)
- Davide Elia
- Unità Di Pneumologia E Terapia Semi-Intensiva Respiratoria, Servizio Di Fisiopatologia Respiratoria Ed Emodinamica Polmonare, Ospedale San Giuseppe, MultiMedica IRCCS, Via San Vittore 12, 20123, Milano, Italy
| | - Francesco Mozzanica
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
- Department of Otorhinolaryngology, IRCCS Multimedica, Milan, Italy
| | - Antonella Caminati
- Unità Di Pneumologia E Terapia Semi-Intensiva Respiratoria, Servizio Di Fisiopatologia Respiratoria Ed Emodinamica Polmonare, Ospedale San Giuseppe, MultiMedica IRCCS, Via San Vittore 12, 20123, Milano, Italy.
| | - Ilaria Giana
- Unità Di Pneumologia E Terapia Semi-Intensiva Respiratoria, Servizio Di Fisiopatologia Respiratoria Ed Emodinamica Polmonare, Ospedale San Giuseppe, MultiMedica IRCCS, Via San Vittore 12, 20123, Milano, Italy
| | - Leonardo Carli
- Unità Di Pneumologia E Terapia Semi-Intensiva Respiratoria, Servizio Di Fisiopatologia Respiratoria Ed Emodinamica Polmonare, Ospedale San Giuseppe, MultiMedica IRCCS, Via San Vittore 12, 20123, Milano, Italy
| | - Federico Ambrogi
- Department of Otorhinolaryngology, IRCCS Multimedica, Milan, Italy
| | - Maurizio Zompatori
- U.O. Di Radiologia Ospedale San Giuseppe, MultiMedica IRCCS, Milan, Italy
| | - Sergio Harari
- Unità Di Pneumologia E Terapia Semi-Intensiva Respiratoria, Servizio Di Fisiopatologia Respiratoria Ed Emodinamica Polmonare, Ospedale San Giuseppe, MultiMedica IRCCS, Via San Vittore 12, 20123, Milano, Italy
- Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
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19
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Matsunaga F, Kono Y, Kitamura H, Terashima M. The role of radiologic technologists during the COVID-19 pandemic. Glob Health Med 2022; 4:237-241. [PMID: 36119782 PMCID: PMC9420333 DOI: 10.35772/ghm.2022.01011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 06/20/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
During the pandemic, stress of coronavirus disease 2019 (COVID-19) on a radiology department has caused major change in the workflow and protocol, which can inflame unnecessary anxiety among the staff. We have adapted and responded quickly however, to the volatile clinical situations owing to a close consultant in infection control. Our repeatedly revised procedures since the 2014 Ebola outbreak possess the expertise and were very useful. In-house training sessions have been held and updated accordingly. In-house networking service has now become more common in our department instead of the emergency contact network relaying the message to the person on the phone tree. Up until January 2022, we examined 10,861 chest X-rays with no in-hospital infection. We sincerely hope our chest X-ray strategies comply with infection prevention and control standards and minimize use of personal protective equipment will be embraced as a positive initiative by frontline radiologic technologists and relieve their anxiety.
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Affiliation(s)
- Futoshi Matsunaga
- Address correspondence to:Futoshi Matsunaga, Department of Radiology, National Center for Global Health and Medicine, 1-21-1 Toyama, Shinjuku-ku, Tokyo 162-8655, Japan. E-mail:
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20
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Filip C, Covali R, Socolov D, Akad M, Carauleanu A, Vasilache IA, Scripcariu IS, Pavaleanu I, Butureanu T, Ciuhodaru M, Boiculese LV, Socolov R. Brixia and qSOFA Scores, Coagulation Factors and Blood Values in Spring versus Autumn 2021 Infection in Pregnant Critical COVID-19 Patients: A Preliminary Study. Healthcare (Basel) 2022; 10:1423. [PMID: 36011083 PMCID: PMC9408262 DOI: 10.3390/healthcare10081423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/25/2022] [Accepted: 07/26/2022] [Indexed: 11/17/2022] Open
Abstract
(1) Background: From the recent variants of concern of the SARS-CoV-2 virus, in which the delta variant generated more negative outcomes than the alpha, we hypothesized that lung involvement, clinical condition deterioration and blood alterations were also more severe in autumn infection, when the delta variant dominated (compared with spring infections, when the alpha variant dominated), in severely infected pregnant patients. (2) Methods: In a prospective study, all pregnant patients admitted to the ICU of the Elena Doamna Obstetrics and Gynecology Hospital with a critical form of COVID-19 infection-spring group (n = 11) and autumn group (n = 7)-between 1 January 2021 and 1 December 2021 were included. Brixia scores were calculated for every patient: A score, upon admittance; H score, the highest score throughout hospitalization; and E score, at the end of hospitalization. For each day of Brixia A, H or E score, the qSOFA (quick sepsis-related organ failure assessment) score was calculated, and the blood values were also considered. (3) Results: Brixia E score, C-reactive protein, GGT and LDH were much higher, while neutrophil count was much lower in autumn compared with spring critical-form pregnant patients. (4) Conclusions: the autumn infection generated more dramatic alterations than the spring infection in pregnant patients with critical forms of COVID-19. Larger studies with more numerous participants are required to confirm these results.
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Affiliation(s)
- Catalina Filip
- Department of Vascular Surgery, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania;
| | - Roxana Covali
- Department of Radiology, Elena Doamna Obstetrics and Gynecology University Hospital, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania
| | - Demetra Socolov
- Department of Obstetrics and Gynecology, Cuza Voda Obstetrics and Gynecology University Hospital, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania; (D.S.); (A.C.); (I.A.V.); (I.S.S.)
| | - Mona Akad
- Department of Obstetrics and Gynecology, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania
| | - Alexandru Carauleanu
- Department of Obstetrics and Gynecology, Cuza Voda Obstetrics and Gynecology University Hospital, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania; (D.S.); (A.C.); (I.A.V.); (I.S.S.)
| | - Ingrid Andrada Vasilache
- Department of Obstetrics and Gynecology, Cuza Voda Obstetrics and Gynecology University Hospital, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania; (D.S.); (A.C.); (I.A.V.); (I.S.S.)
| | - Ioana Sadiye Scripcariu
- Department of Obstetrics and Gynecology, Cuza Voda Obstetrics and Gynecology University Hospital, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania; (D.S.); (A.C.); (I.A.V.); (I.S.S.)
| | - Ioana Pavaleanu
- Department of Obstetrics and Gynecology, Elena Doamna Obstetrics and Gynecology University Hospital, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania; (I.P.); (T.B.); (M.C.); (R.S.)
| | - Tudor Butureanu
- Department of Obstetrics and Gynecology, Elena Doamna Obstetrics and Gynecology University Hospital, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania; (I.P.); (T.B.); (M.C.); (R.S.)
| | - Madalina Ciuhodaru
- Department of Obstetrics and Gynecology, Elena Doamna Obstetrics and Gynecology University Hospital, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania; (I.P.); (T.B.); (M.C.); (R.S.)
| | - Lucian Vasile Boiculese
- Department of Statistics, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania;
| | - Razvan Socolov
- Department of Obstetrics and Gynecology, Elena Doamna Obstetrics and Gynecology University Hospital, Grigore T. Popa University of Medicine and Pharmacy Iasi, 700115 Iasi, Romania; (I.P.); (T.B.); (M.C.); (R.S.)
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Chandra TB, Singh BK, Jain D. Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 222:106947. [PMID: 35749885 PMCID: PMC9403875 DOI: 10.1016/j.cmpb.2022.106947] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 05/25/2022] [Accepted: 06/08/2022] [Indexed: 05/13/2023]
Abstract
BACKGROUND AND OBJECTIVES Chest X-ray (CXR) is a non-invasive imaging modality used in the prognosis and management of chronic lung disorders like tuberculosis (TB), pneumonia, coronavirus disease (COVID-19), etc. The radiomic features associated with different disease manifestations assist in detection, localization, and grading the severity of infected lung regions. The majority of the existing computer-aided diagnosis (CAD) system used these features for the classification task, and only a few works have been dedicated to disease-localization and severity scoring. Moreover, the existing deep learning approaches use class activation map and Saliency map, which generate a rough localization. This study aims to generate a compact disease boundary, infection map, and grade the infection severity using proposed multistage superpixel classification-based disease localization and severity assessment framework. METHODS The proposed method uses a simple linear iterative clustering (SLIC) technique to subdivide the lung field into small superpixels. Initially, the different radiomic texture and proposed shape features are extracted and combined to train different benchmark classifiers in a multistage framework. Subsequently, the predicted class labels are used to generate an infection map, mark disease boundary, and grade the infection severity. The performance is evaluated using a publicly available Montgomery dataset and validated using Friedman average ranking and Holm and Nemenyi post-hoc procedures. RESULTS The proposed multistage classification approach achieved accuracy (ACC)= 95.52%, F-Measure (FM)= 95.48%, area under the curve (AUC)= 0.955 for Stage-I and ACC=85.35%, FM=85.20%, AUC=0.853 for Stage-II using calibration dataset and ACC = 93.41%, FM = 95.32%, AUC = 0.936 for Stage-I and ACC = 84.02%, FM = 71.01%, AUC = 0.795 for Stage-II using validation dataset. Also, the model has demonstrated the average Jaccard Index (JI) of 0.82 and Pearson's correlation coefficient (r) of 0.9589. CONCLUSIONS The obtained classification results using calibration and validation dataset confirms the promising performance of the proposed framework. Also, the average JI shows promising potential to localize the disease, and better agreement between radiologist score and predicted severity score (r) confirms the robustness of the method. Finally, the statistical test justified the significance of the obtained results.
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Affiliation(s)
- Tej Bahadur Chandra
- Department of Computer Applications, National Institute of Technology Raipur, Chhattisgarh, India.
| | - Bikesh Kumar Singh
- Department of Biomedical Engineering, National Institute of Technology Raipur, Chhattisgarh, India
| | - Deepak Jain
- Department of Radiodiagnosis, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, Chhattisgarh, India
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22
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Christanto AG, Komala Dewi D, Nugraha HG, Hikmat IH. Chest X-Ray pattern and lung severity score in COVID-19 patients with diabetes mellitus: A cross sectional study. CLINICAL EPIDEMIOLOGY AND GLOBAL HEALTH 2022; 16:101107. [PMID: 35781928 PMCID: PMC9239701 DOI: 10.1016/j.cegh.2022.101107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/21/2022] [Accepted: 06/26/2022] [Indexed: 01/08/2023] Open
Abstract
Diabetes mellitus is a chronic hyperglycemic condition that can affect the body's immune response to SARS-CoV-2 This study aimed to determine the relationship between diabetes mellitus and lung severity in COVID-19 patients. METHODS A cross-sectional design was conducted at Hasan Sadikin General Hospital during the January-May 2021 period. Data were based on medical records of patients aged 18 years and over with COVID-19. The chi-square test was performed to assess the relationship between diabetes mellitus and lung severity based on the BRIXIA score. RESULTS This study included 538 subjects, mostly aged <60 years (71.9%) and female (60.2%). A total of 125 subjects had abnormal blood glucose levels with an average HbA1c of 9.00 ± 1.77% in patients with diabetes mellitus and a median HbA1c of 5.85% (4.5-6.4%) in patients with reactive hyperglycemia. Lung abnormalities were found in 357 subjects (66.4%). The results of the BRIXIA score to assess lung severity found as many as 77 subjects (14.3%) had a score of 11-18 with 14 people with diabetes mellitus, five people with reactive hyperglycemia. In the population aged ≥60 years, as many as 32 people had a score of 11-18 with three people with diabetes mellitus, two with reactive hyperglycemia and 27 with normal blood glucose. A significant relationship was found between diabetes mellitus and lung severity (p = 0.024). CONCLUSION There is a significant relationship between diabetes mellitus and lung severity in COVID-19 patients aged ≥60 years.
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23
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Cheng J, Sollee J, Hsieh C, Yue H, Vandal N, Shanahan J, Choi JW, Tran TML, Halsey K, Iheanacho F, Warren J, Ahmed A, Eickhoff C, Feldman M, Mortani Barbosa E, Kamel I, Lin CT, Yi T, Healey T, Zhang P, Wu J, Atalay M, Bai HX, Jiao Z, Wang J. COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data. Eur Radiol 2022; 32:4446-4456. [PMID: 35184218 PMCID: PMC8857913 DOI: 10.1007/s00330-022-08588-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/14/2021] [Accepted: 01/22/2022] [Indexed: 01/19/2023]
Abstract
OBJECTIVES We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.
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Affiliation(s)
- Jianhong Cheng
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, Hunan, China
| | - John Sollee
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Celina Hsieh
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Hailin Yue
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, Hunan, China
| | - Nicholas Vandal
- Department of Diagnostic Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Justin Shanahan
- Department of Diagnostic Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ji Whae Choi
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Thi My Linh Tran
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Kasey Halsey
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Franklin Iheanacho
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - James Warren
- Department of Data Science, University of London, London, UK
| | - Abdullah Ahmed
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Carsten Eickhoff
- Center for Biomedical Informatics, Brown University, Providence, RI, 02912, USA
| | - Michael Feldman
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Eduardo Mortani Barbosa
- Department of Diagnostic Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ihab Kamel
- Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD, 21205, USA
| | - Cheng Ting Lin
- Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD, 21205, USA
| | - Thomas Yi
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Terrance Healey
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Paul Zhang
- Department of Diagnostic Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jing Wu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, Hunan, China
| | - Michael Atalay
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA
| | - Harrison X Bai
- Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 601 N Caroline St, Baltimore, MD, 21205, USA.
| | - Zhicheng Jiao
- Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI, 02903, USA.
- Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA.
| | - Jianxin Wang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, Hunan, China.
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Sofic A, Cizmic M, Beslagic E, Becirevic M, Mujakovic A, Husic-Selimovic A, Granov LA. Brixia Chest X-ray Severity Scoring System is in Relation with C-reactive Protein and D-dimer Values in Patients with COVID-19. Mater Sociomed 2022; 34:95-99. [PMID: 36199845 PMCID: PMC9478522 DOI: 10.5455/msm.2022.34.95-99] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 06/14/2022] [Indexed: 01/08/2023] Open
Abstract
Background The Brixia scoring system interpreted chest X-ray changes, serves as an indicator of the extent of changes in the lung parenchyma. Objective To indicate the effect of D-dimer and C-reactive protein (CRP) on Brixia score in patients with positive polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods The research had prospective, descriptive and analytical character, and included patients (n=104) with Coronavirus disease 2019 (COVID-19) diagnosis. Chest X-ray, as well as calculation of Brixia score was done on admission, in the first week of hospitalization, on discharge, and 10 days after discharge (the patient was considered a post-COVID patient. Maximum CRP and D-dimer values were taken into account, along with data about dependence of mechanical ventilation and oxygen therapy. Results Initial Brixia score was significantly associated with the values of CRP (r = .23, p <.05). Higher level of CRP affected the higher result on the Brixia score after the initial X-ray. High CRP and D-dimer were significantly associated with oxygen use in patients, while high D-dimer was also statistically significantly associated with comorbidity. The mean value of Brixia score (during four time points) was significantly related to the values of CRP, D-dimer, the use of mechanical ventilation and oxygen therapy, but also with the existence of comorbidities. The largest statistically significant positive correlation of Brixia scora is with the values of D-dimer (r = .45, p <.000), but also with the values of CRP (r = .36, p <.000). Conclusion Values of CRP have an impact on Brixia score. Investigation of clinical characteristics and outcomes of severe clinical presentation of COVID-19 along with CXR scoring system will contribute to early prediction, accurate diagnosis and treatment as well as to improve the prognosis of patients with severe illness.
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Affiliation(s)
- Amela Sofic
- General Hospital "Prim. dr. Abdulah Nakas", Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Midhat Cizmic
- General Hospital "Prim. dr. Abdulah Nakas", Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Eldina Beslagic
- General Hospital "Prim. dr. Abdulah Nakas", Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Muhidin Becirevic
- General Hospital "Prim. dr. Abdulah Nakas", Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Aida Mujakovic
- General Hospital "Prim. dr. Abdulah Nakas", Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Azra Husic-Selimovic
- General Hospital "Prim. dr. Abdulah Nakas", Sarajevo, Sarajevo, Bosnia and Herzegovina
| | - Lejla Aladjuz Granov
- General Hospital "Prim. dr. Abdulah Nakas", Sarajevo, Sarajevo, Bosnia and Herzegovina
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25
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Gharaibeh M, Elheis M, Khasawneh R, Al-Omari M, Jibril M, Dilki K, El-Obeid E, Altalhi M, Abualigah L. Chest Radiograph Severity Scores, Comorbidity Prevalence, and Outcomes of Patients with Coronavirus Disease Treated at the King Abdullah University Hospital in Jordan: A Retrospective Study. Int J Gen Med 2022; 15:5103-5110. [PMID: 35620646 PMCID: PMC9128829 DOI: 10.2147/ijgm.s360851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 05/12/2022] [Indexed: 12/15/2022] Open
Abstract
Introduction Hospitalized patients with coronavirus disease (COVID-19) often undergo chest x-ray (CXR). Utilizing CXR findings could reduce the cost of COVID-19 treatment and the resultant pressure on the Jordanian healthcare system. Methods We evaluated the association between the CXR severity score, based on the Radiographic Assessment of Lung Edema (RALE) scoring system, and outcomes of patients with COVID-19. The main objective of this work is to assess the role of the RALE scoring system in predicting in-hospital mortality and clinical outcomes of patients with COVID-19. Adults with a positive severe acute respiratory syndrome COVID-19 two reverse-transcription polymerase chain reaction test results and a baseline CXR image, obtained in November 2020, were included. The RALE severity scores were calculated by expert radiologists and categorized as normal, mild, moderate, and severe. Chi-square tests and multivariable logistic regression were used to assess the association between the severity category and admission location and clinical characteristics. Results Based on the multivariable regression analysis, it has been found that male sex, hypertension, and the RALE severity score were significantly associated with in-hospital mortality. The baseline RALE severity score was associated with the need for critical care (P<0.001), in-hospital mortality (P<0.001), and the admission location (P=0.002). Discussion The utilization of RALE severity scores helps to predict clinical outcomes and promote prudent use of resources during the COVID-19 pandemic.
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Affiliation(s)
- Maha Gharaibeh
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Mwaffaq Elheis
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Ruba Khasawneh
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Mamoon Al-Omari
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Mohammad Jibril
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Khalid Dilki
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Eyhab El-Obeid
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 2210, Jordan
| | - Maryam Altalhi
- Department of Management Information System, College of Business Administration, Taif University, Taif, 21944, Saudi Arabia
| | - Laith Abualigah
- Faculty of Computer Sciences and Informatics, Amman Arab University, Amman, 11953, Jordan
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26
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O'Shea A, Li MD, Mercaldo ND, Balthazar P, Som A, Yeung T, Succi MD, Little BP, Kalpathy-Cramer J, Lee SI. Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data. BJR Open 2022; 4:20210062. [PMID: 36105420 PMCID: PMC9459864 DOI: 10.1259/bjro.20210062] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 03/06/2022] [Accepted: 03/09/2022] [Indexed: 12/04/2022] Open
Abstract
Objective To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis. Methods A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model. Results 801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes. Conclusion Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome. Advances in knowledge Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.
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Affiliation(s)
- Aileen O'Shea
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Matthew D Li
- Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, United States
| | - Nathaniel D Mercaldo
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Patricia Balthazar
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Avik Som
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | | | - Marc D Succi
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Brent P Little
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, MGH and BWH Center for Clinical Data Science, Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Susanna I Lee
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
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27
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Hoang S, Nguyen K, Huynh TM, Huynh K, Nguyen P, Tran H. Chest X-ray Severity Score as a Putative Predictor of Clinical Outcome in Hospitalized Patients: An Experience From a Vietnamese COVID-19 Field Hospital. Cureus 2022; 14:e23323. [PMID: 35464539 PMCID: PMC9015876 DOI: 10.7759/cureus.23323] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/19/2022] [Indexed: 12/26/2022] Open
Abstract
Background Through the coronavirus disease 2019 (COVID-19) pandemic, portable radiography was particularly useful for assessing and monitoring the COVID-19 disease in Vietnamese field hospitals. It provides a convenient and precise picture of the progression of the disease. The purpose of this study was to evaluate the predictive value of chest radiograph reporting systems (Brixia and total severity score (TSS)) and the National Early Warning Score (NEWS) clinical score in a group of hospitalized patients with COVID-19. Methods This retrospective cohort study used routinely collected clinical data from polymerase chain reaction (PCR)-positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients admitted to Field Hospital District 8, Ho Chi Minh City, Vietnam, from August 2021 to September 2021. The initial chest radiographs were scored based on the TSS and Brixia scoring systems to quantify the extent of lung involvement. After the chest radiograph score was reported, two residents calculated the rate of all-cause in-hospital mortality with the consultation of expert radiologists. In this study, NEWS2 scores on hospital admission were calculated. The gradient boosting machines (GBMs) and Shapley additive exPlanations (SHAP) were applied to access the important variable and improve the accuracy of mortality prediction. The adjusted odds ratio for predictor was presented by univariate analysis and multivariate analysis. Results The chest X-rays (CXRs) at the admission of 273 patients (mean age 59 years +/-16, 42.1% were male) were scored. In the univariate analysis, age, vaccination status, previous disease, NEWS2, a saturation of peripheral oxygen (Sp02), the Brixia and TSS scores were significant predictors of mortality (p-value < 0.05). In multivariate analysis, there were statistically significant differences in mortality between age, Sp02, Brixia score, and patients with previous diseases were independent predictors of mortality and hospitalization. A gradient boosting machine was performed in the train data set, which showed that the best hyperparameters for predicting the mortality of patients are the Brixia score (exclude TSS score). In the top five predictors, an increase in Brixia, age, and BMI increased the logarithmic number of probability clarifying as death status. Although the TSS and Brixia scores evaluated chest imaging, the TSS score was not essential as the Brixia score (rank 6/11). It was clear that the BMI and NEWS2 score was positively correlated with the Brixia score, and age did not affect this correlation. Meanwhile, we did not find any trend between the TSS score versus BMI and NEWS2. Conclusion When integrated with the BMI and NEWS2 clinical classification systems, the severity score of COVID-19 chest radiographs, particularly the Brixia score, was an excellent predictor of all-cause in-hospital mortality.
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Castelli G, Semenzato U, Lococo S, Cocconcelli E, Bernardinello N, Fichera G, Giraudo C, Spagnolo P, Cattelan A, Balestro E. Brief communication: Chest radiography score in young COVID-19 patients: Does one size fit all? PLoS One 2022; 17:e0264172. [PMID: 35196335 PMCID: PMC8865641 DOI: 10.1371/journal.pone.0264172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 02/04/2022] [Indexed: 11/18/2022] Open
Abstract
During the SARS-CoV-2 pandemic, chest X-Ray (CXR) scores are essential to rapidly assess patients’ prognoses. This study evaluates a published CXR score in a different national healthcare system. In our study, this CXR score maintains a prognostic role in predicting length of hospital stay, but not disease severity. However, our results show that the predictive role of CXR score could be influenced by socioeconomic status and healthcare system.
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Affiliation(s)
- Gioele Castelli
- Respiratory Disease Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova and Padova City Hospital, Padova, Italy
| | - Umberto Semenzato
- Respiratory Disease Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova and Padova City Hospital, Padova, Italy
| | - Sara Lococo
- Respiratory Disease Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova and Padova City Hospital, Padova, Italy
| | - Elisabetta Cocconcelli
- Respiratory Disease Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova and Padova City Hospital, Padova, Italy
| | - Nicol Bernardinello
- Respiratory Disease Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova and Padova City Hospital, Padova, Italy
| | - Giulia Fichera
- Institute of Radiology, Department of Medicine, University of Padova, Padova, Italy
| | - Chiara Giraudo
- Institute of Radiology, Department of Medicine, University of Padova, Padova, Italy
| | - Paolo Spagnolo
- Respiratory Disease Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova and Padova City Hospital, Padova, Italy
| | - Annamaria Cattelan
- Division of Infectious and Tropical Diseases, Azienda Ospedaliera and University of Padova, Padova, Italy
| | - Elisabetta Balestro
- Respiratory Disease Unit, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova and Padova City Hospital, Padova, Italy
- * E-mail:
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29
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Borghesi A, Golemi S, Scrimieri A, Nicosia CMC, Zigliani A, Farina D, Maroldi R. Chest X-ray versus chest computed tomography for outcome prediction in hospitalized patients with COVID-19. Radiol Med 2022; 127:305-308. [PMID: 35083642 PMCID: PMC8791092 DOI: 10.1007/s11547-022-01456-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/11/2022] [Indexed: 12/19/2022]
Abstract
The purpose of this study was to compare the prognostic value of chest X-ray (CXR) and chest computed tomography (CT) in a group of hospitalized patients with COVID-19. For this study, we retrospectively selected a cohort of 106 hospitalized patients with COVID-19 who underwent both CXR and chest CT at admission. For each patient, the pulmonary involvement was ranked by applying the Brixia score for CXR and the percentage of well-aerated lung (WAL) for CT. The Brixia score was assigned at admission (A-Brixia score) and during hospitalization. During hospitalization, only the highest score (H-Brixia score) was considered. At admission, the percentage of WAL (A-CT%WAL) was quantified using a dedicated software. On logistic regression analyses, H-Brixia score was the most effective radiological marker for predicting in-hospital mortality and invasive mechanical ventilation. Additionally, A-CT%WAL did not provide substantial advantages in the risk stratification of hospitalized patients with COVID-19 compared to A-Brixia score.
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Affiliation(s)
- Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy.
| | - Salvatore Golemi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Alessandra Scrimieri
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Costanza Maria Carlotta Nicosia
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Angelo Zigliani
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
| | - Roberto Maroldi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, ASST Spedali Civili of Brescia, Piazzale Spedali Civili, 1, 25123, Brescia, Italy
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Plasencia-Martínez JM, Carrillo-Alcaraz A, Martín-Cascón M, Pérez-Costa R, Ballesta-Ruiz M, Blanco-Barrio A, Herves-Escobedo I, Gómez-Verdú JM, Alcaraz-Martínez J, Alemán-Belando S, Carrillo-Burgos MJ. Early radiological worsening of SARS-CoV-2 pneumonia predicts the need for ventilatory support. Eur Radiol 2022; 32:3490-3500. [PMID: 35034140 PMCID: PMC8761087 DOI: 10.1007/s00330-021-08418-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 01/19/2023]
Abstract
Objectives Identifying early markers of poor prognosis of coronavirus disease 2019 (COVID-19) is mandatory. Our purpose is to analyze by chest radiography if rapid worsening of COVID-19 pneumonia in the initial days has predictive value for ventilatory support (VS) need. Methods Ambispective observational ethically approved study in COVID-19 pneumonia inpatients, validated in a second outpatient sample. Brixia score (BS) was applied to the first and second chest radiography required for suspected COVID-19 pneumonia to determine the predictive capacity of BS worsening for VS need. Intraclass correlation coefficient (ICC) was previously analyzed among three radiologists. Sensitivity, specificity, likelihood ratios, AUC, and odds ratio were calculated using ROC curves and binary logistic regression analysis. A value of p < .05 was considered statistically significant. Results A total of 120 inpatients (55 ± 14 years, 68 men) and 112 outpatients (56 ± 13 years, 61 men) were recruited. The average ICC of the BS was between 0.812 (95% confidence interval 0.745–0.878) and 0.906 (95% confidence interval 0.844–0.940). According to the multivariate analysis, a BS worsening per day > 1.3 points within 10 days of the onset of symptoms doubles the risk for requiring VS in inpatients and 5 times in outpatients (p < .001). The findings from the second chest radiography were always better predictors of VS requirement than those from the first one. Conclusion The early radiological worsening of SARS-CoV-2 pneumonia after symptoms onset is a determining factor of the final prognosis. In elderly patients with some comorbidity and pneumonia, a 48–72-h follow-up radiograph is recommended. Key Points • An early worsening on chest X-ray in patients with SARS-CoV-2 pneumonia is highly predictive of the need for ventilatory support. • This radiological worsening rate can be easily assessed by comparing the first and the second chest X-ray. • In elderly patients with some comorbidity and SARS-CoV-2 pneumonia, close early radiological follow-up is recommended. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08418-3.
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Affiliation(s)
- Juana María Plasencia-Martínez
- Radiology Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain.
| | - Andrés Carrillo-Alcaraz
- Intensive Care Medicine Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain
| | - Miguel Martín-Cascón
- Internal Medicine Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain
| | - Rafael Pérez-Costa
- Emergency Medicine Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain
| | - Mónica Ballesta-Ruiz
- Epidemology and Public Health Regional Health Council, IMIB-Arrixaca, Universidad de Murcia, Murcia, Spain
| | - Ana Blanco-Barrio
- Radiology Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain
| | - Ignacio Herves-Escobedo
- Radiology Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain
| | - José-Miguel Gómez-Verdú
- Internal Medicine Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain
| | - Julián Alcaraz-Martínez
- Emergency Medicine Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain
| | - Sergio Alemán-Belando
- Internal Medicine Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain
| | - María José Carrillo-Burgos
- Emergency Medicine Department, Hospital General Universitario JM Morales Meseguer, Avenida Marqués de los Vélez, s/n, 30008, Murcia, Spain
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Malla S, Alphonse PJA. Fake or real news about COVID-19? Pretrained transformer model to detect potential misleading news. THE EUROPEAN PHYSICAL JOURNAL. SPECIAL TOPICS 2022; 231:3347-3356. [PMID: 35039760 PMCID: PMC8756170 DOI: 10.1140/epjs/s11734-022-00436-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/18/2021] [Indexed: 06/14/2023]
Abstract
The World Health Organization declared the novel coronavirus disease 2019 a pandemic on March 11, 2020. Along with the coronavirus pandemic, a new crisis has emerged, characterized by widespread fear and panic caused by a lack of information or, in some cases, outright fake messages. In these circumstances, Twitter is one of the most eminent and trusted social media platforms. Fake tweets, on the other hand, are challenging to detect and differentiate. The primary goal of this paper is to educate society about the importance of accurate information and prevent the spread of fake information. This paper has investigated COVID-19 fake data from various social media platforms such as Twitter, Facebook, and Instagram. The objective of this paper is to categorize given tweets as either fake or real news. The authors have tested various deep learning models on the COVID-19 fake dataset. Finally, the CT-BERT and RoBERTa deep learning models outperformed other deep learning models like BERT, BERTweet, AlBERT, and DistlBERT. The proposed ensemble deep learning architecture outperformed CT-BERT and RoBERTa on the COVID-19 fake news dataset using the multiplicative fusion technique. The proposed model's performance in this technique was determined by the multiplicative product of the final predictive values of CT-BERT and RoBERTa. This technique overcomes the disadvantage of these CT-BERT and RoBERTa models' incorrect predictive nature. The proposed architecture outperforms both well-known ML and DL models, with 98.88% accuracy and a 98.93% F1-score.
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Affiliation(s)
- SreeJagadeesh Malla
- Department of Computer Applications, National Institute of Technology, Thuvakudi, Tiruchirappalli, Tamil Nadu 620015 India
| | - P. J. A. Alphonse
- Department of Computer Applications, National Institute of Technology, Thuvakudi, Tiruchirappalli, Tamil Nadu 620015 India
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Brixia-Score sagt Verlauf von COVID-19 voraus. ROFO-FORTSCHR RONTG 2021. [DOI: 10.1055/a-1556-5066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Laursen CB, Prosch H, Harders SM, Falster C, Davidsen JR, Tárnoki ÁD. COVID-19: imaging. COVID-19 2021. [DOI: 10.1183/2312508x.10012421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
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Garrafa E, Vezzoli M, Ravanelli M, Farina D, Borghesi A, Calza S, Maroldi R. Early prediction of in-hospital death of COVID-19 patients: a machine-learning model based on age, blood analyses, and chest x-ray score. eLife 2021; 10:e70640. [PMID: 34661530 PMCID: PMC8550757 DOI: 10.7554/elife.70640] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 10/17/2021] [Indexed: 12/15/2022] Open
Abstract
An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.
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Affiliation(s)
- Emirena Garrafa
- Department of Molecular and Translational Medicine, University of BresciaBresciaItaly
- ASST Spedali Civili di Brescia, Department of LaboratoryBresciaItaly
| | - Marika Vezzoli
- Department of Molecular and Translational Medicine, University of BresciaBresciaItaly
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of BresciaBresciaItaly
- ASST Spedali Civili di Brescia, Department of RadiologyBresciaItaly
| | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of BresciaBresciaItaly
- ASST Spedali Civili di Brescia, Department of RadiologyBresciaItaly
| | - Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of BresciaBresciaItaly
- ASST Spedali Civili di Brescia, Department of RadiologyBresciaItaly
| | - Stefano Calza
- Department of Molecular and Translational Medicine, University of BresciaBresciaItaly
| | - Roberto Maroldi
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of BresciaBresciaItaly
- ASST Spedali Civili di Brescia, Department of RadiologyBresciaItaly
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Varghese BA, Shin H, Desai B, Gholamrezanezhad A, Lei X, Perkins M, Oberai A, Nanda N, Cen S, Duddalwar V. Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs. Br J Radiol 2021; 94:20210221. [PMID: 34520246 PMCID: PMC9328073 DOI: 10.1259/bjr.20210221] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Objectives For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor prognosis as defined by the need for intensive unit care and mechanical ventilation. We tested the feasibility of chest X-ray (CXR)-based radiomics metrics to develop machine-learning algorithms for predicting patients with poor outcomes. Methods In this Institutional Review Board (IRB) approved, Health Insurance Portability and Accountability Act (HIPAA) compliant, retrospective study, we evaluated CXRs performed around the time of admission from 167 COVID-19 patients. Of the 167 patients, 68 (40.72%) required intensive care during their stay, 45 (26.95%) required intubation, and 25 (14.97%) died. Lung opacities were manually segmented using ITK-SNAP (open-source software). CaPTk (open-source software) was used to perform 2D radiomics analysis. Results Of all the algorithms considered, the AdaBoost classifier performed the best with AUC = 0.72 to predict the need for intubation, AUC = 0.71 to predict death, and AUC = 0.61 to predict the need for admission to the intensive care unit (ICU). AdaBoost had similar performance with ElasticNet in predicting the need for admission to ICU. Analysis of the key radiomic metrics that drive model prediction and performance showed the importance of first-order texture metrics compared to other radiomics panel metrics. Using a Venn-diagram analysis, two first-order texture metrics and one second-order texture metric that consistently played an important role in driving model performance in all three outcome predictions were identified. Conclusions: Considering the quantitative nature and reliability of radiomic metrics, they can be used prospectively as prognostic markers to individualize treatment plans for COVID-19 patients and also assist with healthcare resource management. Advances in knowledge We report on the performance of CXR-based imaging metrics extracted from RT-PCR positive COVID-19 patients at admission to develop machine-learning algorithms for predicting the need for ICU, the need for intubation, and mortality, respectively.
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Affiliation(s)
| | - Heeseop Shin
- Keck School of Medicine, University of Southern California, CA, USA
| | - Bhushan Desai
- Keck School of Medicine, University of Southern California, CA, USA
| | | | - Xiaomeng Lei
- Keck School of Medicine, University of Southern California, CA, USA
| | - Melissa Perkins
- Keck School of Medicine, University of Southern California, CA, USA
| | - Assad Oberai
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Neha Nanda
- Keck School of Medicine, University of Southern California, CA, USA
| | - Steven Cen
- Keck School of Medicine, University of Southern California, CA, USA
| | - Vinay Duddalwar
- Keck School of Medicine, University of Southern California, CA, USA
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Pal A, Ali A, Young TR, Oostenbrink J, Prabhakar A, Prabhakar A, Deacon N, Arnold A, Eltayeb A, Yap C, Young DM, Tang A, Lakshmanan S, Lim YY, Pokarowski M, Kakodkar P. Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic. World J Radiol 2021; 13:258-282. [PMID: 34630913 PMCID: PMC8473437 DOI: 10.4329/wjr.v13.i9.258] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 03/28/2021] [Accepted: 08/04/2021] [Indexed: 02/06/2023] Open
Abstract
Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, over 103214008 cases have been reported, with more than 2231158 deaths as of January 31, 2021. Although the gold standard for diagnosis of this disease remains the reverse-transcription polymerase chain reaction of nasopharyngeal and oropharyngeal swabs, its false-negative rates have ignited the use of medical imaging as an important adjunct or alternative. Medical imaging assists in identifying the pathogenesis, the degree of pulmonary damage, and the characteristic features in each imaging modality. This literature review collates the characteristic radiographic findings of COVID-19 in various imaging modalities while keeping the preliminary focus on chest radiography, computed tomography (CT), and ultrasound scans. Given the higher sensitivity and greater proficiency in detecting characteristic findings during the early stages, CT scans are more reliable in diagnosis and serve as a practical method in following up the disease time course. As research rapidly expands, we have emphasized the CO-RADS classification system as a tool to aid in communicating the likelihood of COVID-19 suspicion among healthcare workers. Additionally, the utilization of other scoring systems such as MuLBSTA, Radiological Assessment of Lung Edema, and Brixia in this pandemic are reviewed as they integrate the radiographic findings into an objective scoring system to risk stratify the patients and predict the severity of disease. Furthermore, current progress in the utilization of artificial intelligence via radiomics is evaluated. Lastly, the lesson from the first wave and preparation for the second wave from the point of view of radiology are summarized.
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Affiliation(s)
- Aman Pal
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Abulhassan Ali
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Timothy R Young
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Juan Oostenbrink
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Akul Prabhakar
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Amogh Prabhakar
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Nina Deacon
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Amar Arnold
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Ahmed Eltayeb
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Charles Yap
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - David M Young
- Department of Computer Science, Yale University, New Haven, CO 06520, United States
| | - Alan Tang
- Department of Health Science, Duke University, Durham, NC 27708, United States
| | - Subramanian Lakshmanan
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Ying Yi Lim
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
| | - Martha Pokarowski
- The Hospital for Sick Kids, University of Toronto, Toronto M5S, Ontario, Canada
| | - Pramath Kakodkar
- School of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland
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Au-Yong I, Higashi Y, Giannotti E, Fogarty A, Morling JR, Grainge M, Race A, Juurlink I, Simmonds M, Briggs S, Cruikshank S, Hammond-Pears S, West J, Crooks CJ, Card T. Chest Radiograph Scoring Alone or Combined with Other Risk Scores for Predicting Outcomes in COVID-19. Radiology 2021; 302:460-469. [PMID: 34519573 PMCID: PMC8475750 DOI: 10.1148/radiol.2021210986] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background Radiographic severity may help predict patient deterioration and
outcomes from COVID-19 pneumonia. Purpose To assess the reliability and reproducibility of three chest radiograph
reporting systems (radiographic assessment of lung edema [RALE], Brixia,
and percentage opacification) in patients with proven SARS-CoV-2
infection and examine the ability of these scores to predict adverse
outcomes both alone and in conjunction with two clinical scoring
systems, National Early Warning Score 2 (NEWS2) and International Severe
Acute Respiratory and Emerging Infection Consortium: Coronavirus
Clinical Characterization Consortium (ISARIC-4C) mortality. Materials and Methods This retrospective cohort study used routinely collected clinical data
of patients with polymerase chain reaction–positive SARS-CoV-2
infection admitted to a single center from February 2020 through July
2020. Initial chest radiographs were scored for RALE, Brixia, and
percentage opacification by one of three radiologists. Intra- and
interreader agreement were assessed with intraclass correlation
coefficients. The rate of admission to the intensive care unit (ICU) or
death up to 60 days after scored chest radiograph was estimated. NEWS2
and ISARIC-4C mortality at hospital admission were calculated. Daily
risk for admission to ICU or death was modeled with Cox proportional
hazards models that incorporated the chest radiograph scores adjusted
for NEWS2 or ISARIC-4C mortality. Results Admission chest radiographs of 50 patients (mean age, 74 years ±
16 [standard deviation]; 28 men) were scored by all three radiologists,
with good interreader reliability for all scores, as follows: intraclass
correlation coefficients were 0.87 for RALE (95% CI: 0.80, 0.92), 0.86
for Brixia (95% CI: 0.76, 0.92), and 0.72 for percentage opacification
(95% CI: 0.48, 0.85). Of 751 patients with a chest radiograph, those
with greater than 75% opacification had a median time to ICU admission
or death of just 1–2 days. Among 628 patients for whom data were
available (median age, 76 years [interquartile range, 61–84
years]; 344 men), opacification of 51%–75% increased risk for ICU
admission or death by twofold (hazard ratio, 2.2; 95% CI: 1.6, 2.8), and
opacification greater than 75% increased ICU risk by fourfold (hazard
ratio, 4.0; 95% CI: 3.4, 4.7) compared with opacification of
0%–25%, when adjusted for NEWS2 score. Conclusion Brixia, radiographic assessment of lung edema, and percentage
opacification scores all reliably helped predict adverse outcomes in
SARS-CoV-2 infection. © RSNA, 2021 Online supplemental material is available for this
article. See also the editorial by Little in this issue.
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Affiliation(s)
- Iain Au-Yong
- Department of Radiology, Nottingham University Hospitals NHS Trust, NG7 2UH
| | - Yutaro Higashi
- Department of Radiology, Nottingham University Hospitals NHS Trust, NG7 2UH
| | | | - Andrew Fogarty
- Nottingham University Hospitals NHS Trust.,Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH
| | - Joanne R Morling
- Nottingham University Hospitals NHS Trust.,Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH
| | - Matthew Grainge
- Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB
| | | | | | | | | | | | | | - Joe West
- Nottingham University Hospitals NHS Trust.,Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH.,East Midlands Academic Health Science Network, University of Nottingham, Nottingham, NG7 2TU
| | - Colin J Crooks
- Nottingham University Hospitals NHS Trust.,Translational Medical Sciences, School of Medicine, University of Nottingham, NG7 2UH.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH
| | - Timothy Card
- Nottingham University Hospitals NHS Trust.,Population and Lifespan Sciences, School of Medicine, University of Nottingham, NG5 1PB.,NIHR Nottingham Biomedical Research Centre (BRC), Nottingham University Hospitals NHS Trust and the University of Nottingham, NG7 2UH
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Bae S, Kim Y, Hwang S, Kwon KT, Chang HH, Kim SW. New Scoring System for Predicting Mortality in Patients with COVID-19. Yonsei Med J 2021; 62:806-813. [PMID: 34427066 PMCID: PMC8382723 DOI: 10.3349/ymj.2021.62.9.806] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 06/24/2021] [Accepted: 06/29/2021] [Indexed: 12/15/2022] Open
Abstract
PURPOSE We aimed to develop a novel mortality scoring system for inpatients with COVID-19 based on simple demographic factors and laboratory findings. MATERIALS AND METHODS We reviewed and analyzed data from patients who were admitted and diagnosed with COVID-19 at 10 hospitals in Daegu, South Korea, between January and July 2020. We randomized and assigned patients to the development and validation groups at a 70% to 30% ratio. Each point scored for selected risk factors helped build a new mortality scoring system using Cox regression analysis. We evaluated the accuracy of the new scoring system in the development and validation groups using the area under the curve. RESULTS The development group included 1232 patients, whereas the validation group included 528 patients. In the development group, predictors for the new scoring system as selected by Cox proportional hazards model were age ≥70 years, diabetes, chronic kidney disease, dementia, C-reactive protein levels >4 mg/dL, infiltration on chest X-rays at the initial diagnosis, and the need for oxygen support on admission. The areas under the curve for the development and validation groups were 0.914 [95% confidence interval (CI) 0.891-0.937] and 0.898 (95% CI 0.854-0.941), respectively. According to our scoring system, COVID-19 mortality was 0.4% for the low-risk group (score 0-3) and 53.7% for the very high-risk group (score ≥11). CONCLUSION We developed a new scoring system for quickly and easily predicting COVID-19 mortality using simple predictors. This scoring system can help physicians provide the proper therapy and strategy for each patient.
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Affiliation(s)
- Sohyun Bae
- Division of Infectious Diseases, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Yoonjung Kim
- Division of Infectious Diseases, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Soyoon Hwang
- Division of Infectious Diseases, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Ki Tae Kwon
- Division of Infectious Diseases, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Hyun Ha Chang
- Division of Infectious Diseases, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Shin Woo Kim
- Division of Infectious Diseases, Department of Internal Medicine, School of Medicine, Kyungpook National University, Daegu, Korea.
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Boari GEM, Bonetti S, Braglia-Orlandini F, Chiarini G, Faustini C, Bianco G, Santagiuliana M, Guarinoni V, Saottini M, Viola S, Ferrari-Toninelli G, Pasini G, Bonzi B, Desenzani P, Tusi C, Malerba P, Zanotti E, Turini D, Rizzoni D. Short-Term Consequences of SARS-CoV-2-Related Pneumonia: A Follow Up Study. High Blood Press Cardiovasc Prev 2021; 28:373-381. [PMID: 33909284 PMCID: PMC8080190 DOI: 10.1007/s40292-021-00454-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 04/15/2021] [Indexed: 01/08/2023] Open
Abstract
The aim of the study was to assess the short-term consequences of SARS-CoV-2-related pneumonia, also in relation to radiologic/laboratory/clinical indices of risk at baseline. This prospective follow-up cohort study included 94 patients with confirmed COVID-19 admitted to a medical ward at the Montichiari Hospital, Brescia, Italy from February 28th to April 30th, 2020. Patients had COVID-19 related pneumonia with respiratory failure. Ninety-four patients out of 193 survivors accepted to be re-evaluated after discharge, on average after 4 months. In ¼ of the patients an evidence of pulmonary fibrosis was detected, as indicated by an altered diffusing capacity of the lung for carbon monoxide (DLCO); in 6-7% of patients the alteration was classified as of moderate/severe degree. We also evaluated quality of life thorough a structured questionnaire: 52% of the patients still lamented fatigue, 36% effort dyspnea, 10% anorexia, 14% dysgeusia or anosmia, 31% insomnia and 21% anxiety. Finally, we evaluated three prognostic indices (the Brixia radiologic score, the Charlson Comorbidity Index and the 4C mortality score) in terms of prediction of the clinical consequences of the disease. All of them significantly predicted the extent of short-term lung involvement. In conclusion, our study demonstrated that SARS-CoV-2-related pneumonia is associated to relevant short-term clinical consequences, both in terms of persistence of symptoms and in terms of impairment of DLCO (indicator of a possible development of pulmonary fibrosis); some severity indices of the disease may predict short-term clinical outcome. Further studies are needed to ascertain whether such manifestations may persist long-term.
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Affiliation(s)
- Gianluca E. M. Boari
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | - Silvia Bonetti
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
- Department of Medical and Surgical Sciences, University of Brescia, Brescia, Italy
| | - Federico Braglia-Orlandini
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
- Department of Medical and Surgical Sciences, University of Brescia, Brescia, Italy
| | - Giulia Chiarini
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
- Department of Medical and Surgical Sciences, University of Brescia, Brescia, Italy
| | - Cristina Faustini
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
- Department of Medical and Surgical Sciences, University of Brescia, Brescia, Italy
| | - Gianluca Bianco
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
- Department of Medical and Surgical Sciences, University of Brescia, Brescia, Italy
| | - Marzia Santagiuliana
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
- Department of Medical and Surgical Sciences, University of Brescia, Brescia, Italy
| | - Vittoria Guarinoni
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | - Michele Saottini
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | - Sara Viola
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | | | - Giancarlo Pasini
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | - Bianca Bonzi
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | - Paolo Desenzani
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | - Claudia Tusi
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | - Paolo Malerba
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
- Department of Medical and Surgical Sciences, University of Brescia, Brescia, Italy
| | - Eros Zanotti
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | - Daniele Turini
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
| | - Damiano Rizzoni
- Division of Medicine, Covid-19 Unit M, Spedali Civili di Brescia, Montichiari, Brescia Italy
- Department of Medical and Surgical Sciences, University of Brescia, Brescia, Italy
- Clinica Medica, Department of Clinical and Experimental Sciences, University of Brescia, c/o 2a Medicina Spedali Civili di Brescia, Piazza Spedali Civili 1, 25100 Brescia, Italy
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Chest X-ray Score and Frailty as Predictors of In-Hospital Mortality in Older Adults with COVID-19. J Clin Med 2021; 10:jcm10132965. [PMID: 34279449 PMCID: PMC8268684 DOI: 10.3390/jcm10132965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 06/28/2021] [Accepted: 06/29/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The purpose of this study was to evaluate the prognostic impact of chest X-ray (CXR) score, frailty, and clinical and laboratory data on in-hospital mortality of hospitalized older patients with COVID-19. METHODS This retrospective study included 122 patients 65 years or older with positive reverse transcription polymerase chain reaction for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and with availability to CXRs on admission. The primary outcome of the study was in-hospital mortality. Statistical analysis was conducted using Cox regression. The predictive ability of the CXR score was compared with the Clinical Frailty Scale (CFS) and fever data using Area Under the Curve (AUC) and net reclassification improvement (NRI) statistics. RESULTS Of 122 patients, 67 died during hospital stay (54.9%). The CXR score (HR: 1.16, 95% CI, 1.04-1.28), CFS (HR: 1.27; 95% CI, 1.09-1.47), and presence of fever (HR: 1.75; 95% CI, 1.03-2.97) were significant predictors of in-hospital mortality. The addition of both the CFS and presence of fever to the CXR score significantly improved the prediction of in-hospital mortality (NRI, 0.460; 95% CI, 0.102 to 0.888; AUC difference: 0.117; 95% CI, 0.041 to 0.192, p = 0.003). CONCLUSIONS CXR score, CFS, and presence of fever were the main predictors of in-hospital mortality in our cohort of hospitalized older patients with COVID-19. Adding frailty and presence of fever to the CXR score statistically improved predictive accuracy compared to single risk factors.
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Signoroni A, Savardi M, Benini S, Adami N, Leonardi R, Gibellini P, Vaccher F, Ravanelli M, Borghesi A, Maroldi R, Farina D. BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset. Med Image Anal 2021; 71:102046. [PMID: 33862337 PMCID: PMC8010334 DOI: 10.1016/j.media.2021.102046] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 02/04/2021] [Accepted: 03/17/2021] [Indexed: 12/22/2022]
Abstract
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely Brixia score, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a "from-the-part-to-the-whole" procedure involving different datasets. In particular, we exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps are also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We also consider other scores proposed in literature and provide a comparison with a recently proposed non-specific approach. We eventually test the performance robustness of our model on an assorted public COVID-19 dataset, for which we also provide Brixia score annotations, observing good direct generalization and fine-tuning capabilities that highlight the portability of BS-Net in other clinical settings. The CXR dataset along with the source code and the trained model are publicly released for research purposes.
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Affiliation(s)
- Alberto Signoroni
- Department of Information Engineering, University of Brescia, Brescia, Italy.
| | - Mattia Savardi
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Sergio Benini
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Nicola Adami
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Riccardo Leonardi
- Department of Information Engineering, University of Brescia, Brescia, Italy
| | - Paolo Gibellini
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Filippo Vaccher
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Andrea Borghesi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Roberto Maroldi
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
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Initial findings in chest X-rays as predictors of worsening lung infection in patients with COVID-19: correlation in 265 patients. RADIOLOGIA 2021; 63:324-333. [PMID: 34246423 PMCID: PMC8179119 DOI: 10.1016/j.rxeng.2021.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 03/16/2021] [Indexed: 12/23/2022]
Abstract
Background and aims We aimed to analyze the relationship between the initial chest X-ray findings in patients with severe acute respiratory syndrome due to infection with SARS-CoV-2 and eventual clinical worsening and to compare three systems of quantifying these findings. Material and methods This retrospective study reviewed the clinical and radiological evolution of 265 adult patients with COVID-19 attended at our center between March 2020 and April 2020. We recorded data related to patients’ comorbidities, hospital stay, and clinical worsening (admission to the ICU, intubation, and death). We used three scoring systems taking into consideration 6 or 8 lung fields (designated 6A, 6B, and 8) to quantify lung involvement in each patient’s initial pathological chest X-ray and to classify its severity as mild, moderate, or severe, and we compared these three systems. We also recorded the presence of alveolar opacities and linear opacities (fundamentally linear atelectasis) in the first chest X-ray with pathologic findings. Results In the χ2 analysis, moderate or severe involvement in the three classification systems correlated with hospital admission (P = .009 in 6A, P = .001 in 6B, and P = .001 in 8) and with death (P = .02 in 6A, P = .01 in 6B, and P = .006 in 8). In the regression analysis, the most significant associations were 6B with alveolar involvement (OR 2.3; 95%CI 1.1.–4.7; P = .025;) and 8 with alveolar involvement (OR 2.07; 95% CI 1.01.–4.25; P = .046). No differences were observed in the ability of the three systems to predict clinical worsening by classifications of involvement in chest X-rays as moderate or severe. Conclusion Moderate/severe extension in the three chest X-ray scoring systems evaluating the extent of involvement over 6 or 8 lung fields and the finding of alveolar opacities in the first pathologic X-ray correlated with mortality and the rate of hospitalization in the patients studied. No significant difference was found in the predictive ability of the three classification systems proposed.
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Brogna B, Bignardi E, Brogna C, Volpe M, Lombardi G, Rosa A, Gagliardi G, Capasso PFM, Gravino E, Maio F, Pane F, Picariello V, Buono M, Colucci L, Musto LA. A Pictorial Review of the Role of Imaging in the Detection, Management, Histopathological Correlations, and Complications of COVID-19 Pneumonia. Diagnostics (Basel) 2021; 11:437. [PMID: 33806423 PMCID: PMC8000129 DOI: 10.3390/diagnostics11030437] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 02/22/2021] [Accepted: 02/27/2021] [Indexed: 02/07/2023] Open
Abstract
Imaging plays an important role in the detection of coronavirus (COVID-19) pneumonia in both managing the disease and evaluating the complications. Imaging with chest computed tomography (CT) can also have a potential predictive and prognostic role in COVID-19 patient outcomes. The aim of this pictorial review is to describe the role of imaging with chest X-ray (CXR), lung ultrasound (LUS), and CT in the diagnosis and management of COVID-19 pneumonia, the current indications, the scores proposed for each modality, the advantages/limitations of each modality and their role in detecting complications, and the histopathological correlations.
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Affiliation(s)
- Barbara Brogna
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Elio Bignardi
- Radiology Unit, Cotugno Hospital, Naples, Via Quagliariello 54, 80131 Naples, Italy;
| | - Claudia Brogna
- Neuropsychiatric Unit ASL Avellino, Via Degli Imbimbo 10/12, 83100 Avellino, Italy;
| | - Mena Volpe
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Giulio Lombardi
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Alessandro Rosa
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Giuliano Gagliardi
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Pietro Fabio Maurizio Capasso
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Enzo Gravino
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Francesca Maio
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Francesco Pane
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Valentina Picariello
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Marcella Buono
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Lorenzo Colucci
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Lanfranco Aquilino Musto
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
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