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1
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Kong W, Liu Y, Li W, Yang K, Yu L, Jiao G. Correlation between oxygenation function and laboratory indicators in COVID-19 patients based on non-enhanced chest CT images and construction of an artificial intelligence prediction model. Front Microbiol 2024; 15:1495432. [PMID: 39569002 PMCID: PMC11576442 DOI: 10.3389/fmicb.2024.1495432] [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: 09/12/2024] [Accepted: 10/22/2024] [Indexed: 11/22/2024] Open
Abstract
Objective By extracting early chest CT radiomic features of COVID-19 patients, we explored their correlation with laboratory indicators and oxygenation index (PaO2/FiO2), thereby developed an Artificial Intelligence (AI) model based on radiomic features to predict the deterioration of oxygenation function in COVID-19 patients. Methods This retrospective study included 384 patients with COVID-19, whose baseline information, laboratory indicators, oxygenation-related parameters, and non-enhanced chest CT images were collected. Utilizing the PaO2/FiO2 stratification proposed by the Berlin criteria, patients were divided into 4 groups, and differences in laboratory indicators among these groups were compared. Radiomic features were extracted, and their correlations with laboratory indicators and the PaO2/FiO2 were analyzed, respectively. Finally, an AI model was developed using the PaO2/FiO2 threshold of less than 200 mmHg as the label, and the model's performance was assessed using the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Group datas comparison was analyzed using SPSS software, and radiomic features were extracted using Python-based Pyradiomics. Results There were no statistically significant differences in baseline characteristics among the groups. Radiomic features showed differences in all 4 groups, while the differences in laboratory indicators were inconsistent, with some PaO2/FiO2 groups showed differences and others not. Regardless of whether laboratory indicators demonstrated differences across different PaO2/FiO2 groups, they could all be captured by radiomic features. Consequently, we chose radiomic features as variables to establish an AI model based on chest CT radiomic features. On the training set, the model achieved an AUC of 0.8137 (95% CI [0.7631-0.8612]), accuracy of 0.7249, sensitivity of 0.6626 and specificity of 0.8208. On the validation set, the model achieved an AUC of 0.8273 (95% CI [0.7475-0.9005]), accuracy of 0.7739, sensitivity of 0.7429 and specificity of 0.8222. Conclusion This study found that the early chest CT radiomic features of COVID-19 patients are strongly associated not only with early laboratory indicators but also with the lowest PaO2/FiO2. Consequently, we developed an AI model based on CT radiomic features to predict deterioration in oxygenation function, which can provide a reliable basis for further clinical management and treatment.
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Affiliation(s)
- Weiheng Kong
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Yujia Liu
- College of Traditional Chinese Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang, China
| | - Wang Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Keyi Yang
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Lixin Yu
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Guangyu Jiao
- Department of Pulmonary and Critical Care Medicine, Shengjing Hospital of China Medical University, Shenyang, China
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Moradi G, Etehadi SS, Mousavi SH, Mohammadi R. Evaluating the Cavitary Lung Lesions on CT Scan of COVID-19 Patients: A Retrospective Study. J Community Hosp Intern Med Perspect 2024; 14:23-29. [PMID: 39839166 PMCID: PMC11745194 DOI: 10.55729/2000-9666.1411] [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: 04/01/2024] [Revised: 08/12/2024] [Accepted: 08/29/2024] [Indexed: 01/23/2025] Open
Abstract
Background It has been shown that cavitary lesions on CT scans of patients with COVID-19 may be related to their clinical symptoms and mortality rate. Materials and methods The study population included patients diagnosed with COVID-19 based on RT-PCR results from throat samples or typical clinical and chest CT scan findings who were hospitalized at Sina Hospital in Tehran in 2020 and underwent chest CT scans. Chest CT scans were examined for the severity of pulmonary opacities and the presence, number, size, wall thickness, and distribution of cavitary lung lesions. Results Oxygen saturation was lower in patients with cavitary lesions in the initial state and after treatment than those without cavitation, and a statistically significant relationship was observed (p < 0.05). In terms of gender, a significant correlation was observed, and the prevalence of cavitary lesions was higher in men (p < 0.05). Also, the in-hospital mortality rate was higher in patients with cavitary lesions (p < 0.05). Conclusion Based on our results, the presence of cavitary lung lesions in COVID-19 patients is related to the mortality rate, severity of pulmonary involvement, and patients' gender.
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Affiliation(s)
- Golnaz Moradi
- Department of Radiology, Sina Hospital, School of Medicine, Tehran University of Medical Sciences, Tehran,
Iran
| | | | - Seyed H. Mousavi
- Department of Urology, Tehran University of Medical Sciences, Tehran,
Iran
| | - Rayeheh Mohammadi
- Department of Radiology, Sina Hospital, School of Medicine, Tehran University of Medical Sciences, Tehran,
Iran
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3
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Roostaee A, Lima ZS, Aziz-Ahari A, Doosalivand H, Younesi L. Evaluation of the value of chest CT severity score in assessment of COVID-19 severity and short-term prognosis. J Family Med Prim Care 2024; 13:1670-1675. [PMID: 38948629 PMCID: PMC11213437 DOI: 10.4103/jfmpc.jfmpc_414_23] [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: 03/03/2023] [Revised: 05/07/2023] [Accepted: 07/26/2023] [Indexed: 07/02/2024] Open
Abstract
Background Evaluations have shown that the severity of pulmonary involvement is very important in the mortality rate of patients with coronavirus disease 2019 (COVID-19). The purpose of this study was to evaluate the value of chest CT severity score in assessment of COVID-19 severity and short-term prognosis. Materials and Methods This study was a cross-sectional study with a sample size of 197 patients, including all patients admitted to Rasoul Akram Hospital, with positive polymerase chain reaction, to investigate the relationship between computed tomography (CT) severity score and mortality. The demographic data and CT scan findings (including the pattern, side, and distribution of involvement), co-morbidities, and lab data were collected. Finally, gathered data were analyzed by SPSS-26. Results 119 (60.4%) patients were male, and 78 (39.6%) were female. The mean age was 58.58 ± 17.3 years. Totally, 61 patients died; of those, 41 (67.2%) were admitted to the intensive care unit (ICU), so there was a significant relation between death and ICU admission (P value = 0.000). Diabetes was the most common co-morbidity, followed by hypertension and IHD. There was no significant relation between co-morbidities and death (P value = 0.13). The most common patterns of CTs were interlobular septal thickening and ground glass opacities, and a higher CT severity score was in the second week from the onset of symptoms, which was associated with more mortality (P value < 0.05). Conclusion Our study showed that a patient with a higher CT severity score of the second week had a higher risk of mortality. Also, association of the CT severity score, laboratory data, and symptoms could be applicable in predicting the patient's condition.
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Affiliation(s)
- Ayda Roostaee
- Department of Radiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Zeinab Safarpour Lima
- Department of Radiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Alireza Aziz-Ahari
- Department of Radiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hadi Doosalivand
- Department of Radiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Ladan Younesi
- Department of Radiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
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Ghadery AH, Abbasian L, Jafari F, Yazdi NA, Ahmadinejad Z. Correlation of clinical, laboratory, and short-term outcomes of immunocompromised and immunocompetent COVID-19 patients with semi-quantitative chest CT score findings: A case-control study. Immun Inflamm Dis 2024; 12:e1239. [PMID: 38577996 PMCID: PMC10996371 DOI: 10.1002/iid3.1239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 03/15/2024] [Accepted: 03/20/2024] [Indexed: 04/06/2024] Open
Abstract
BACKGROUND As the effects of immunosuppression are not still clear on COVID-19 patients, we conducted this study to identify clinical and laboratory findings associated with pulmonary involvement in both immunocompromised and immunocompetent patients. METHODS A case-control of 107 immunocompromised and 107 immunocompetent COVID-19 patients matched for age and sex with either positive RT-PCR or clinical-radiological findings suggestive of COVID-19 enrolled in the study. Their initial clinical features, laboratory findings, chest CT scans, and short-term outcomes (hospitalization time and intensive care unit [ICU] admission) were recorded. In addition, pulmonary involvement was assessed with the semi-quantitative scoring system (0-25). RESULTS Pulmonary involvement was significantly lower in immunocompromised patients in contrast to immunocompetent patients, especially in RLL (p = 0.001), LUL (p = 0.023), and both central and peripheral (p = 0.002), and peribronchovascular (p = 0.004) sites of lungs. Patchy (p < 0.001), wedged (p = 0.002), confluent (p = 0.002) lesions, and ground glass with consolidation pattern (p < 0.001) were significantly higher among immunocompetent patients. Initial signs and symptoms of immunocompromised patients including dyspnea (p = 0.008) and hemoptysis (p = 0.036), respiratory rate of over 25 (p < 0.001), and spo2 of below 93% (p = 0.01) were associated with higher pulmonary involvement. Total chest CT score was also associated with longer hospitalization (p = 0.016) and ICU admission (p = 0.04) among immunocompromised patients. CONCLUSIONS Pulmonary involvement score was not significantly different among immunocompromised and immunocompetent patients. Initial clinical findings (dyspnea, hemoptysis, higher RR, and lower Spo2) of immunocompromised patients could better predict pulmonary involvement than laboratory findings.
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Affiliation(s)
- Abdolkarim Haji Ghadery
- Department of Radiology, Advanced Diagnostic and Interventional Radiology Research Center(ADIR)Tehran University of Medical SciencesTehranIran
| | - Ladan Abbasian
- Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High Risk Behaviors, Department of Infectious Diseases, Imam Khomeini Hospital Complex, School of MedicineTehran University of Medical SciencesTehranIran
| | - Fatemeh Jafari
- Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High Risk Behaviors, Department of Infectious Diseases, Imam Khomeini Hospital Complex, School of MedicineTehran University of Medical SciencesTehranIran
| | - Niloofar Ayoobi Yazdi
- Department of Radiology, Advanced Diagnostic and Interventional Radiology Research Center (ADIR)Tehran University of Medical SciencesTehranIran
| | - Zahra Ahmadinejad
- Department of Infectious Diseases, Liver Transplantation Research Center, Imam Khomeini Hospital ComplexTehran University of Medical SciencesTehranIran
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5
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Shabani M, Shobeiri P, Nouri S, Moradi Z, Amenu RA, Mehrabi Nejad MM, Rezaei N. Risk of flare or relapse in patients with immune-mediated diseases following SARS-CoV-2 vaccination: a systematic review and meta-analysis. Eur J Med Res 2024; 29:55. [PMID: 38229141 DOI: 10.1186/s40001-024-01639-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 01/03/2024] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Patients with autoimmune and immune-mediated diseases (AI-IMD) are at greater risk of COVID-19 infection; therefore, they should be prioritized in vaccination programs. However, there are concerns regarding the safety of COVID-19 vaccines in terms of disease relapse, flare, or exacerbation. In this study, we aimed to provide a more precise and reliable vision using systematic review and meta-analysis. METHODS PubMed-MEDLINE, Embase, and Web of Science were searched for original articles reporting the relapse/flare in adult patients with AI-IMD between June 1, 2020 and September 25, 2022. Subgroup analysis and sensitivity analysis were conducted to investigate the sources of heterogeneity. Statistical analysis was performed using R software. RESULTS A total of 134 observations of various AI-IMDs across 74 studies assessed the rate of relapse, flare, or exacerbation in AI-IMD patients. Accordingly, the crude overall prevalence of relapse, flare, or exacerbation was 6.28% (95% CI [4.78%; 7.95%], I2 = 97.6%), changing from 6.28% (I2 = 97.6%) to 6.24% (I2 = 65.1%) after removing the outliers. AI-IMD patients administering mRNA, vector-based, and inactive vaccines showed 8.13% ([5.6%; 11.03%], I2 = 98.1%), 0.32% ([0.0%; 4.03%], I2 = 93.5%), and 3.07% ([1.09%; 5.9%], I2 = 96.2%) relapse, flare, or exacerbation, respectively (p-value = 0.0086). In terms of disease category, nephrologic (26.66%) and hematologic (14.12%) disorders had the highest and dermatologic (4.81%) and neurologic (2.62%) disorders exhibited to have the lowest crude prevalence of relapse, flare, or exacerbation (p-value < 0.0001). CONCLUSION The risk of flare/relapse/exacerbation in AI-IMD patients is found to be minimal, especially with vector-based vaccines. Vaccination against COVID-19 is recommended in this population.
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Affiliation(s)
- Mahya Shabani
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
- Department of Immunology, Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Qarib St, Keshavarz Blvd, 14194, Tehran, 1419733141, Iran
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Shadi Nouri
- Arak University of Medical Sciences, Arak, Iran
| | - Zahra Moradi
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Robel Assefa Amenu
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Mohammad-Mehdi Mehrabi Nejad
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences (TUMS), Qarib St, Keshavarz Blvd, 14194, Tehran, 1419733141, Iran.
| | - Nima Rezaei
- Department of Immunology, Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Qarib St, Keshavarz Blvd, 14194, Tehran, 1419733141, Iran.
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
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Arian A, Mehrabi Nejad MM, Zoorpaikar M, Hasanzadeh N, Sotoudeh-Paima S, Kolahi S, Gity M, Soltanian-Zadeh H. Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis. PLoS One 2023; 18:e0294899. [PMID: 38064442 PMCID: PMC10707659 DOI: 10.1371/journal.pone.0294899] [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: 03/26/2023] [Accepted: 11/11/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19. OBJECTIVES This study investigates the performance of an AI-aided quantification model in predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with radiologists' performance. SUBJECTS AND METHODS A total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were recruited in this cross-sectional study. Quantification of the total and compromised lung parenchyma was performed by two expert radiologists using a volumetric image analysis software and compared against an AI-assisted package consisting of a modified U-Net model for segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for segmenting lung volume. The fraction of compromised lung parenchyma (%CL) was calculated. Based on clinical results, the subjects were divided into two categories: critical (n = 45) and noncritical (n = 45). All admission data were compared between the two groups. RESULTS There was an excellent agreement between the radiologist-obtained and AI-assisted measurements (intraclass correlation coefficient = 0.88, P < 0.001). Both the AI-assisted and radiologist-obtained %CLs were significantly higher in the critical subjects (P = 0.009 and 0.02, respectively) than in the noncritical subjects. In the multivariate logistic regression analysis to distinguish the critical subjects, an AI-assisted %CL ≥35% (odds ratio [OR] = 17.0), oxygen saturation level of <88% (OR = 33.6), immunocompromised condition (OR = 8.1), and other comorbidities (OR = 15.2) independently remained as significant variables in the models. Our proposed model obtained an accuracy of 83.9%, a sensitivity of 79.1%, and a specificity of 88.6% in predicting critical outcomes. CONCLUSIONS AI-assisted measurements are similar to quantitative radiologist-obtained measurements in determining lung involvement in COVID-19 subjects.
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Affiliation(s)
- Arvin Arian
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad-Mehdi Mehrabi Nejad
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mostafa Zoorpaikar
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Navid Hasanzadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Saman Sotoudeh-Paima
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Shahriar Kolahi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Masoumeh Gity
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
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Atceken Z, Celik Y, Atasoy C, Peker Y. The Diagnostic Utility of Artificial Intelligence-Guided Computed Tomography-Based Severity Scores for Predicting Short-Term Clinical Outcomes in Adults with COVID-19 Pneumonia. J Clin Med 2023; 12:7039. [PMID: 38002652 PMCID: PMC10672493 DOI: 10.3390/jcm12227039] [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: 09/27/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023] Open
Abstract
Chest computed tomography (CT) imaging with the use of an artificial intelligence (AI) analysis program has been helpful for the rapid evaluation of large numbers of patients during the COVID-19 pandemic. We have previously demonstrated that adults with COVID-19 infection with high-risk obstructive sleep apnea (OSA) have poorer clinical outcomes than COVID-19 patients with low-risk OSA. In the current secondary analysis, we evaluated the association of AI-guided CT-based severity scores (SSs) with short-term outcomes in the same cohort. In total, 221 patients (mean age of 52.6 ± 15.6 years, 59% men) with eligible chest CT images from March to May 2020 were included. The AI program scanned the CT images in 3D, and the algorithm measured volumes of lobes and lungs as well as high-opacity areas, including ground glass and consolidation. An SS was defined as the ratio of the volume of high-opacity areas to that of the total lung volume. The primary outcome was the need for supplemental oxygen and hospitalization over 28 days. A receiver operating characteristic (ROC) curve analysis of the association between an SS and the need for supplemental oxygen revealed a cut-off score of 2.65 on the CT images, with a sensitivity of 81% and a specificity of 56%. In a multivariate logistic regression model, an SS > 2.65 predicted the need for supplemental oxygen, with an odds ratio (OR) of 3.98 (95% confidence interval (CI) 1.80-8.79; p < 0.001), and hospitalization, with an OR of 2.40 (95% CI 1.23-4.71; p = 0.011), adjusted for age, sex, body mass index, diabetes, hypertension, and coronary artery disease. We conclude that AI-guided CT-based SSs can be used for predicting the need for supplemental oxygen and hospitalization in patients with COVID-19 pneumonia.
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Affiliation(s)
- Zeynep Atceken
- Department of Radiology, Koc University School of Medicine, Istanbul 34010, Turkey; (Z.A.); (C.A.)
| | - Yeliz Celik
- Center for Translational Medicine (KUTTAM), Department of Pulmonary Medicine, Koc University School of Medicine, and Koc University Research, Koc University, Istanbul 34010, Turkey;
| | - Cetin Atasoy
- Department of Radiology, Koc University School of Medicine, Istanbul 34010, Turkey; (Z.A.); (C.A.)
| | - Yüksel Peker
- Center for Translational Medicine (KUTTAM), Department of Pulmonary Medicine, Koc University School of Medicine, and Koc University Research, Koc University, Istanbul 34010, Turkey;
- Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, 40530 Gothenburg, Sweden
- Department of Clinical Sciences, Respiratory Medicine and Allergology, Faculty of Medicine, Lund University, 22185 Lund, Sweden
- Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Division of Sleep and Circadian Disorders, Harvard Medical School, Brigham and Women’s Hospital, Boston, MA 02115, USA
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8
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Oi Y, Ogawa F, Yamashiro T, Matsushita S, Oguri A, Utada S, Misawa N, Honzawa H, Abe T, Takeuchi I. Prediction of prognosis in patients with severe COVID-19 pneumonia using CT score by emergency physicians: a single-center retrospective study. Sci Rep 2023; 13:4045. [PMID: 36899171 PMCID: PMC10004443 DOI: 10.1038/s41598-023-31312-5] [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: 11/30/2022] [Accepted: 03/09/2023] [Indexed: 03/12/2023] Open
Abstract
We aimed to develop a method to determine the CT score that can be easily obtained from CT images and examine its prognostic value for severe COVID pneumonia. Patients with COVID pneumonia who required ventilatory management by intubation were included. CT score was based on anatomical information in axial CT images and were divided into three sections of height from the apex to the bottom. The extent of pneumonia in each section was rated from 0 to 5 and summed. The primary outcome was the prediction of patients who died or were managed on extracorporeal membrane oxygenation (ECMO) based on the CT score at admission. Of the 71 patients included, 12 (16.9%) died or required ECMO management, and the CT score predicted death or ECMO management with ROC of 0.718 (0.561-0.875). The death or ECMO versus survival group (median [quartiles]) had a CT score of 17.75 (14.75-20) versus 13 (11-16.5), p = 0.017. In conclusion, a higher score on our generated CT score could predict the likelihood of death or ECMO management. A CT score at the time of admission allows for early preparation and transfer to a hospital that can manage patients who may need ECMO.
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Affiliation(s)
- Yasufumi Oi
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan. .,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan.
| | - Fumihiro Ogawa
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Tsuneo Yamashiro
- Department of Radiology, Yokohama City University School of Medicine, Yokohama, Japan
| | - Shoichiro Matsushita
- Department of Radiology, Yokohama City University School of Medicine, Yokohama, Japan
| | - Ayako Oguri
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Shusuke Utada
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Naho Misawa
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Hiroshi Honzawa
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan
| | - Takeru Abe
- Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan.,Advanced Critical Care and Emergency Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Ichiro Takeuchi
- Emergency Care Department, Yokohama City University Hospital, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.,Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Japan.,Advanced Critical Care and Emergency Center, Yokohama City University Medical Center, Yokohama, Japan
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9
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Prakash J, Kumar N, Saran K, Yadav AK, Kumar A, Bhattacharya PK, Prasad A. Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis. J Med Imaging Radiat Sci 2023; 54:364-375. [PMID: 36907753 PMCID: PMC9933858 DOI: 10.1016/j.jmir.2023.02.003] [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: 07/26/2022] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/18/2023]
Abstract
BACKGROUND Prediction of outcomes in severe COVID-19 patients using chest computed tomography severity score (CTSS) may enable more effective clinical management and early, timely ICU admission. We conducted a systematic review and meta-analysis to determine the predictive accuracy of the CTSS for disease severity and mortality in severe COVID-19 subjects. METHODS The electronic databases PubMed, Google Scholar, Web of Science, and the Cochrane Library were searched to find eligible studies that investigated the impact of CTSS on disease severity and mortality in COVID-19 patients between 7 January 2020 and 15 June 2021. Two independent authors looked into the risk of bias using the Quality in Prognosis Studies (QUIPS) tool. RESULTS Seventeen studies involving 2788 patients reported the predictive value of CTSS for disease severity. The pooled sensitivity, specificity, and summary area under the curve (sAUC) of CTSS were 0.85 (95% CI 0.78-0.90, I2 =83), 0.86 (95% CI 0.76-0.92, I2 =96) and 0.91 (95% CI 0.89-0.94), respectively. Six studies involving 1403 patients reported the predictive values of CTSS for COVID-19 mortality. The pooled sensitivity, specificity, and sAUC of CTSS were 0.77 (95% CI 0.69-0.83, I2 = 41), 0.79 (95% CI 0.72-0.85, I2 = 88), and 0.84 (95% CI 0.81-0.87), respectively. DISCUSSION Early prediction of prognosis is needed to deliver the better care to patients and stratify them as soon as possible. Because different CTSS thresholds have been reported in various studies, clinicians are still determining whether CTSS thresholds should be used to define disease severity and predict prognosis. CONCLUSION Early prediction of prognosis is needed to deliver optimal care and timely stratification of patients. CTSS has strong discriminating power for the prediction of disease severity and mortality in patients with COVID-19.
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Affiliation(s)
- Jay Prakash
- Department of Critical Care Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
| | - Naveen Kumar
- Department of Radiology, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
| | - Khushboo Saran
- Department of Pathology, Gandhi Nagar Hospital, Central Coalfields Limited, Kanke, Ranchi, Jharkhand, India.
| | - Arun Kumar Yadav
- Department of Community Medicine, Armed Force Medical College, Pune, Maharashtra, India
| | - Amit Kumar
- Department of Laboratory Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
| | - Pradip Kumar Bhattacharya
- Department of Critical Care Medicine, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
| | - Anupa Prasad
- Department of Biochemistry, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India.
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10
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Evaluation of Follow-Up CT Scans in Patients with Severe Initial Pulmonary Involvement by COVID-19. Can Respir J 2022; 2022:6972998. [PMID: 36618585 PMCID: PMC9815919 DOI: 10.1155/2022/6972998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 07/10/2022] [Accepted: 12/09/2022] [Indexed: 12/31/2022] Open
Abstract
Objective To investigate the predictive factors of residual pulmonary opacity on midterm follow-up CT scans in patients hospitalized with COVID-19 pneumonia. Materials and Methods This prospective study was conducted in a tertiary referral university hospital in Iran, from March 2020 to December 2020. Patients hospitalized due to novel coronavirus pneumonia with bilateral pulmonary involvement in the first CT scan were included and underwent an 8-week follow-up CT scan. Pulmonary involvement (PI) severity was assessed using a 25-scale semiquantitative scoring system. Density of opacities was recorded using the Hounsfield unit (HU). Results The chest CT scans of 50 participants (mean age = 54.4 ± 14.2 years, 72% male) were reviewed, among whom 8 (16%) had residual findings on follow-up CT scans. The most common residual findings were faint ground-glass opacities (GGOs) (14%); fibrotic-like changes were observed in 2 (4%) patients. Demographic findings, underlying disease, and laboratory findings did not show significant association with remaining pulmonary opacities. The total PI score was significantly higher in participants with remaining parenchymal involvement (14.5 ± 6.5 versus 10.2 ± 3.7; P=0.02). On admission, the HU of patients with remaining opacities was significantly higher (-239.8 ± 107.6 versus -344.0 ± 157.4; P=0.01). Remaining pulmonary findings were more frequently detected in patients who had received antivirals, steroid pulse, or IVIG treatments (P=0.02, 0.02, and 0.001, respectively). Only the PI score remained statistically significant in multivariate logistic regression with 88.1% accuracy (OR = 1.2 [1.01-1.53]; P=0.03). Conclusion Pulmonary opacities are more likely to persist in midterm follow-up CT scans in patients with severe initial pulmonary involvement.
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11
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Neag MA, Vulturar DM, Gherman D, Burlacu CC, Todea DA, Buzoianu AD. Gastrointestinal microbiota: A predictor of COVID-19 severity? World J Gastroenterol 2022; 28:6328-6344. [PMID: 36533107 PMCID: PMC9753053 DOI: 10.3748/wjg.v28.i45.6328] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 10/26/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by a severe acute respiratory syndrome coronavirus 2 infection, has raised serious concerns worldwide over the past 3 years. The severity and clinical course of COVID-19 depends on many factors (e.g., associated comorbidities, age, etc) and may have various clinical and imaging findings, which raises management concerns. Gut microbiota composition is known to influence respiratory disease, and respiratory viral infection can also influence gut microbiota. Gut and lung microbiota and their relationship (gut-lung axis) can act as modulators of inflammation. Modulating the intestinal microbiota, by improving its composition and diversity through nutraceutical agents, can have a positive impact in the prophylaxis/treatment of COVID-19.
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Affiliation(s)
- Maria Adriana Neag
- Department of Pharmacology, Toxicology and Clinical Pharmacology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca 400337, Romania
| | - Damiana-Maria Vulturar
- Department of Pneumology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca 400332, Romania
| | - Diana Gherman
- Department of Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca 400347, Romania
| | - Codrin-Constantin Burlacu
- Faculty of Medicine, Iuliu Haţieganu University of Medicine and Pharmacy, Cluj-Napoca 400347, Romania
| | - Doina Adina Todea
- Department of Pneumology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca 400332, Romania
| | - Anca Dana Buzoianu
- Department of Pharmacology, Toxicology and Clinical Pharmacology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca 400337, Romania
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12
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Jensen CM, Costa JC, Nørgaard JC, Zucco AG, Neesgaard B, Niemann CU, Ostrowski SR, Reekie J, Holten B, Kalhauge A, Matthay MA, Lundgren JD, Helleberg M, Moestrup KS. Chest x-ray imaging score is associated with severity of COVID-19 pneumonia: the MBrixia score. Sci Rep 2022; 12:21019. [PMID: 36471093 PMCID: PMC9722655 DOI: 10.1038/s41598-022-25397-7] [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: 07/04/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Spatial resolution in existing chest x-ray (CXR)-based scoring systems for coronavirus disease 2019 (COVID-19) pneumonia is low, and should be increased for better representation of anatomy, and severity of lung involvement. An existing CXR-based system, the Brixia score, was modified to increase the spatial resolution, creating the MBrixia score. The MBrixia score is the sum, of a rule-based quantification of CXR severity on a scale of 0 to 3 in 12 anatomical zones in the lungs. The MBrixia score was applied to CXR images from COVID-19 patients at a single tertiary hospital in the period May 4th-June 5th, 2020. The relationship between MBrixia score, and level of respiratory support at the time of performed CXR imaging was investigated. 37 hospitalized COVID-19 patients with 290 CXRs were identified, 22 (59.5%) were admitted to the intensive care unit and 10 (27%) died during follow-up. In a Poisson regression using all 290 MBrixia scored CXRs, a higher MBrixia score was associated with a higher level of respiratory support at the time of performed CXR. The MBrixia score could potentially be valuable as a quantitative surrogate measurement of COVID-19 pneumonia severity, and future studies should investigate the score's validity and capabilities of predicting clinical outcomes.
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Affiliation(s)
- Christian M. Jensen
- grid.5254.60000 0001 0674 042XCentre of Excellence for Health, Immunity and Infections (CHIP), Section 2100, , Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Junia C. Costa
- grid.5254.60000 0001 0674 042XDepartment of Diagnostic Radiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Jens C. Nørgaard
- grid.5254.60000 0001 0674 042XCentre of Excellence for Health, Immunity and Infections (CHIP), Section 2100, , Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Adrian G. Zucco
- grid.5254.60000 0001 0674 042XCentre of Excellence for Health, Immunity and Infections (CHIP), Section 2100, , Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Bastian Neesgaard
- grid.5254.60000 0001 0674 042XCentre of Excellence for Health, Immunity and Infections (CHIP), Section 2100, , Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Carsten U. Niemann
- grid.5254.60000 0001 0674 042XDepartment of Haematology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark ,grid.5254.60000 0001 0674 042XDepartment of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Sisse R. Ostrowski
- grid.5254.60000 0001 0674 042XDepartment of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark ,grid.5254.60000 0001 0674 042XDepartment of Clinical Immunology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Joanne Reekie
- grid.5254.60000 0001 0674 042XCentre of Excellence for Health, Immunity and Infections (CHIP), Section 2100, , Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Birgit Holten
- grid.5254.60000 0001 0674 042XDepartment of Diagnostic Radiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Anna Kalhauge
- grid.5254.60000 0001 0674 042XDepartment of Diagnostic Radiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Michael A. Matthay
- grid.266102.10000 0001 2297 6811Departments of Medicine and Anaesthesia, Cardiovascular Research Institute, University of California, San Francisco, CA USA
| | - Jens D. Lundgren
- grid.5254.60000 0001 0674 042XCentre of Excellence for Health, Immunity and Infections (CHIP), Section 2100, , Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark ,grid.5254.60000 0001 0674 042XDepartment of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark ,grid.5254.60000 0001 0674 042XDepartment of Infectious Diseases, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Marie Helleberg
- grid.5254.60000 0001 0674 042XCentre of Excellence for Health, Immunity and Infections (CHIP), Section 2100, , Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark ,grid.5254.60000 0001 0674 042XDepartment of Infectious Diseases, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Kasper S. Moestrup
- grid.5254.60000 0001 0674 042XCentre of Excellence for Health, Immunity and Infections (CHIP), Section 2100, , Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
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Almasi Nokiani A, Shahnazari R, Abbasi MA, Divsalar F, Bayazidi M, Sadatnaseri A. CT severity score in COVID-19 patients, assessment of performance in triage and outcome prediction: a comparative study of different methods. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [PMCID: PMC9116070 DOI: 10.1186/s43055-022-00781-5] [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] [Indexed: 01/03/2023] Open
Abstract
Background Lung involvement in COVID-19 can be quantified by chest CT scan with some triage and prognostication value. Optimizing initial triage of patients could help decrease adverse health impacts of the disease through better clinical management. At least 6 CT severity score (CTSS) systems have been proposed. We aimed to evaluate triage and prognostication performance of seven different CTSSs, including one proposed by ourselves, in hospitalized COVID-19 patients diagnosed by positive polymerase chain reaction (PCR). Results After exclusion of 14 heart failure and significant preexisting pulmonary disease patients, 96 COVID-19, PCR-positive patients were included into our retrospective study, admitted from February 20, 2020, to July 22. Their mean age was 63.6 ± 17.4 years (range 21–88, median 67). Fifty-seven (59.4%) were men, and 39 (40.6%) were women. All CTSSs showed good interrater reliability as calculated intraclass correlation coefficients (ICCs) between two radiologists were 0.764–0.837. Those CTSSs with more numerous segmentations showed the best ICCs. As judged by area under curve (AUC) for each receiver operator characteristic (ROC) curve, only three CTSSs showed acceptable AUCs (AUC = 0.7) for triage of severe/critical patients. All CTSSs showed acceptable AUCs for prognostication (AUCs = 0.76–0.79). Calculated AUCs for different CTSSs were not significantly different for triage and for prediction of severe/critical disease, but some difference was shown for prediction of critical disease. Conclusions Men are probably affected more frequently than women by COVID-19. Quantification of lung disease in COVID-19 is a readily available and easy tool to be used in triage and prognostication, but we do not advocate its use in heart failure or chronic respiratory disease patients. The scoring systems with more numerous segmentations are recommended if any future imaging for comparison is contemplated. CTSS performance in triage was much lower than earlier reports, and only three CTSSs showed acceptable AUCs in this regard. CTSS performed better for prognostic purposes than for triage as all 7 CTSSs showed acceptable AUCs in both types of prognostic ROC curves. There is not much difference among performance of different CTSSs.
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14
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Zakariaee SS, Naderi N, Rezaee D. Prognostic accuracy of visual lung damage computed tomography score for mortality prediction in patients with COVID-19 pneumonia: a systematic review and meta-analysis. EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [PMCID: PMC8907554 DOI: 10.1186/s43055-022-00741-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background Chest computed tomography (CT) findings provide great added value in characterizing the extent of disease and severity of pulmonary involvements. Chest CT severity score (CT-SS) could be considered as an appropriate prognostic factor for mortality prediction in patients with COVID-19 pneumonia. In this study, we performed a meta-analysis evaluating the prognostic accuracy of CT-SS for mortality prediction in patients with COVID-19 pneumonia. Methods A systematic search was conducted on Web of Science, PubMed, Embase, Scopus, and Google Scholar databases between December 2019 and September 2021. The meta-analysis was performed using the random-effects model, and sensitivity and specificity (with 95%CIs) of CT-SS were calculated using the study authors’ pre-specified threshold. Results Sensitivity estimates ranged from 0.32 to 1.00, and the pooled estimate of sensitivity was 0.67 [95%CI (0.59–0.75)]. Specificity estimates ranged from 0.53 to 0.95 and the pooled estimate of specificity was 0.79 [95%CI (0.74–0.84)]. Results of meta-regression analysis showed that radiologist experiences did not affect the sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients (P = 0.314 and 0.283, respectively). The test for subgroup differences suggests that study location significantly modifies sensitivity and specificity of CT-SS to predict mortality in COVID-19 patients. The area under the summary receiver operator characteristic (ROC) curve was 0.8248. Conclusion Our results have shown that CT-SS has acceptable prognostic accuracy for mortality prediction in COVID-19 patients. This simple scoring method could help to improve the management of high-risk patients with COVID-19.
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15
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Ortiz-Vilchis P, Ramirez-Arellano A. An Entropy-Based Measure of Complexity: An Application in Lung-Damage. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1119. [PMID: 36010783 PMCID: PMC9407132 DOI: 10.3390/e24081119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/23/2022] [Accepted: 08/12/2022] [Indexed: 06/15/2023]
Abstract
The computed tomography (CT) chest is a tool for diagnostic tests and the early evaluation of lung infections, pulmonary interstitial damage, and complications caused by common pneumonia and COVID-19. Additionally, computer-aided diagnostic systems and methods based on entropy, fractality, and deep learning have been implemented to analyse lung CT images. This article aims to introduce an Entropy-based Measure of Complexity (EMC). In addition, derived from EMC, a Lung Damage Measure (LDM) is introduced to show a medical application. CT scans of 486 healthy subjects, 263 diagnosed with COVID-19, and 329 with pneumonia were analysed using the LDM. The statistical analysis shows a significant difference in LDM between healthy subjects and those suffering from COVID-19 and common pneumonia. The LDM of common pneumonia was the highest, followed by COVID-19 and healthy subjects. Furthermore, LDM increased as much as clinical classification and CO-RADS scores. Thus, LDM is a measure that could be used to determine or confirm the scored severity. On the other hand, the d-summable information model best fits the information obtained by the covering of the CT; thus, it can be the cornerstone for formulating a fractional LDM.
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16
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Mehrabi Nejad MM, Shobeiri P, Dehghanbanadaki H, Tabary M, Aryannejad A, Haji Ghadery A, Shabani M, Moosaie F, SeyedAlinaghi S, Rezaei N. Seroconversion following the first, second, and third dose of SARS-CoV-2 vaccines in immunocompromised population: a systematic review and meta-analysis. Virol J 2022; 19:132. [PMID: 35941646 PMCID: PMC9358061 DOI: 10.1186/s12985-022-01858-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 07/18/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Immunocompromised (IC) patients are at higher risk of more severe COVID-19 infections than the general population. Special considerations should be dedicated to such patients. We aimed to investigate the efficacy of COVID-19 vaccines based on the vaccine type and etiology as well as the necessity of booster dose in this high-risk population. MATERIALS AND METHODS We searched PubMed, Web of Science, and Scopus databases for observational studies published between June 1st, 2020, and September 1st, 2021, which investigated the seroconversion after COVID-19 vaccine administration in adult patients with IC conditions. For investigation of sources of heterogeneity, subgroup analysis and sensitivity analysis were conducted. Statistical analysis was performed using R software. RESULTS According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, we included 81 articles in the meta-analysis. The overall crude prevalence of seroconversion after the first (n: 7460), second (n: 13,181), and third (n: 909, all population were transplant patients with mRNA vaccine administration) dose administration was 26.17% (95% CI 19.01%, 33.99%, I2 = 97.1%), 57.11% (95% CI: 49.22%, 64.83%, I2 = 98.4%), and 48.65% (95% CI: 34.63%, 62.79%, I2 = 94.4%). Despite the relatively same immunogenicity of mRNA and vector-based vaccines after the first dose, the mRNA vaccines induced higher immunity after the second dose. Regarding the etiologic factor, transplant patients were less likely to develop immunity after both first and second dose rather than patients with malignancy (17.0% vs 37.0% after first dose, P = 0.02; 38.3% vs 72.1% after second dose, P < 0.001) or autoimmune disease (17.0% vs 36.4%, P = 0.04; 38.3% vs 80.2%, P < 0.001). To evaluate the efficacy of the third dose, we observed an increasing trend in transplant patients after the first (17.0%), second (38.3%), and third (48.6%) dose. CONCLUSION The rising pattern of seroconversion after boosting tends to be promising. In this case, more attention should be devoted to transplant patients who possess the lowest response rate.
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Affiliation(s)
- Mohammad-Mehdi Mehrabi Nejad
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences (TUMS), Tehran, Iran.,School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Parnian Shobeiri
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.,Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.,Department of Immunology, Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Qarib St, Keshavarz Blvd, Tehran, 1419733141, Iran.,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Hojat Dehghanbanadaki
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Tabary
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Armin Aryannejad
- Experimental Medicine Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Abdolkarim Haji Ghadery
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mahya Shabani
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Moosaie
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - SeyedAhmad SeyedAlinaghi
- Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences, Imam Khomeini Hospital Complex, Keshavarz Blvd., Tehran, 1419733141, Iran.
| | - Nima Rezaei
- Department of Immunology, Research Center for Immunodeficiencies, Pediatrics Center of Excellence, Children's Medical Center, Tehran University of Medical Sciences, Qarib St, Keshavarz Blvd, Tehran, 1419733141, Iran. .,Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
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17
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Chelan SH, Alimohammadzadeh K, Maher A. The relationship between talent management and the efficiency of head nurses and senior and middle managers from the educational and medical centers in Tabriz, Iran - a case study. J Med Life 2022; 15:1018-1024. [PMID: 36188650 PMCID: PMC9514814 DOI: 10.25122/jml-2017-0066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Accepted: 08/18/2018] [Indexed: 11/17/2022] Open
Abstract
Nowadays, organizations understand that they need the best talent to succeed in the complex world economy and survive in a competitive business environment. Therefore, talent management can ensure that each employee with a unique talent or ability will be placed in the correct position. This article aimed to study the relationship between talent management, senior and middle managers, and head nurses from educational health and research centers in Tabriz, in 2016. The target population included senior and middle managers and head nurses from Tabriz University of Medical Sciences, approximately 197 people. The sample for this study was selected based on Morgan's table, which rounds up to 123 people. The Kolmogorov-Smirnov test was used to analyze data, and if data were normal, correlation and regression analysis were performed. There was a significant relationship between talent management and the efficiency of senior and middle management and head nurses from the educational and medical centers in Tabriz. Therefore, when talent management increases, the efficiency level also rises to a noticeable degree. Also, the linear regression showed a linear relationship between talent management as an independent variable and efficiency as a dependent variable. Applying talent management strategies in the management selection process in organizations with demanding environments such as hospitals seems inevitable so that managers with the highest efficiency are hired.
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Affiliation(s)
- Sajad Hadi Chelan
- School of Management and Social Sciences, North Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Khalil Alimohammadzadeh
- Department of Healthcare Management, School of Management and Social Sciences, North Tehran Branch, Islamic Azad University, Tehran, Iran,Health Economics Policy Research Center, Tehran Medical Sciences Islamic Azad University, Tehran, Iran,Corresponding Author: Khalil Alimohammadzadeh, Department of Healthcare Management, School of Management and Social Sciences, North Tehran Branch, Islamic Azad University, Tehran, Iran. Health Economics Policy Research Center, Tehran Medical Sciences Islamic Azad University, Tehran, Iran. E-mail:
| | - Ali Maher
- Department of Healthcare Management, School of Management and Social Sciences, North Tehran Branch, Islamic Azad University, Tehran, Iran
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18
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Zakariaee SS, Salmanipour H, Naderi N, Kazemi-Arpanahi H, Shanbehzadeh M. Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis. Clin Transl Imaging 2022; 10:663-676. [PMID: 35892066 PMCID: PMC9302953 DOI: 10.1007/s40336-022-00512-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/05/2022] [Indexed: 01/08/2023]
Abstract
Purpose Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis. Methods Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes. Results This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157–1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307–9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both P > 0.2). The shapes of Begg’s funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg’s test P = 0.945 and 0.356, respectively). Conclusions The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement.
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Affiliation(s)
- Seyed Salman Zakariaee
- Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
| | - Hossein Salmanipour
- Department of Radiology, Faculty of Medicine, Ilam University of Medical Sciences, Ilam, Iran
| | - Negar Naderi
- Department of Midwifery, Faculty of Nursing and Midwifery, Ilam University of Medical Sciences, Ilam, Iran
| | - Hadi Kazemi-Arpanahi
- Department of Health Information Technology, School of Management and Medical Information Sciences, Abadan University of Medical Sciences, Abadan, Iran
| | - Mostafa Shanbehzadeh
- Department of Health Information Technology, School of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran
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19
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Bonato M, Peditto P, Landini N, Fraccaro A, Catino C, Cuzzola M, Malacchini N, Savoia F, Roma N, Salasnich M, Turrin M, Zampieri F, Zanardi G, Zeraj F, Rattazzi M, Peta M, Baraldo S, Saetta M, Fusaro M, Morana G, Romagnoli M. Multidimensional 3-Month Follow-Up of Severe COVID-19: Airways beyond the Parenchyma in Symptomatic Patients. J Clin Med 2022; 11:jcm11144046. [PMID: 35887810 PMCID: PMC9319969 DOI: 10.3390/jcm11144046] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/01/2022] [Accepted: 07/07/2022] [Indexed: 12/10/2022] Open
Abstract
SARS-CoV-2 may lead to a large spectrum of respiratory manifestations, including pulmonary sequelae. We conducted a single-center longitudinal study of survivors from severe COVID-19 cases who underwent a chest CT during hospitalization (CTH). Three months after being discharged, these patients were evaluated by a clinical examination, pulmonary function tests and a chest-CT scan (CTFU). Sixty-two patients were enrolled. At follow-up, 27% complained of exertional dyspnoea and 12% of cough. Dyspnoeic patients had a lower forced expiratory flow (FEF)25–75 (p = 0.015), while a CT scan (p = 0.016 showed that patients with cough had a higher extent of bronchiectasis. Lung volumes and diffusion of carbon monoxide (DLCO) at follow-up were lower in patients who had been invasively ventilated, which correlated inversely with the length of hospitalization and ground-glass extension at CTH. At follow-up, 14.5% of patients had a complete radiological resolution, while 85.5% presented persistence of ground-glass opacities, and 46.7% showed fibrotic-like alterations. Residual ground-glass at CTFU was related to the length of hospitalization (r = 0.48; p = 0.0002) and to the need for mechanical ventilation or high flow oxygen (p = 0.01) during the acute phase. In conclusion, although patients at three months from discharge showed functional impairment and radiological abnormalities, which correlated with a prolonged hospital stay and need for mechanical ventilation, the persistence of respiratory symptoms was related not to parenchymal but rather to airway sequelae.
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Affiliation(s)
- Matteo Bonato
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (S.B.); (M.S.)
- Correspondence: ; Tel.: +39-0422-322729; Fax: +39-0422-322738
| | - Piera Peditto
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Nicholas Landini
- Department of Radiology, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (N.L.); (N.R.); (G.M.)
- Department of Radiological, Oncological and Pathological Sciences, Policlinico Umberto I Hospital, “Sapienza” Rome University, 00185 Rome, Italy
| | - Alessia Fraccaro
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Cosimo Catino
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Maria Cuzzola
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Nicola Malacchini
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Francesca Savoia
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Nicola Roma
- Department of Radiology, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (N.L.); (N.R.); (G.M.)
| | - Mauro Salasnich
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Martina Turrin
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Francesca Zampieri
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Giuseppe Zanardi
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Fabiola Zeraj
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
| | - Marcello Rattazzi
- Department of Internal Medicine, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy;
| | - Mario Peta
- Department of Emergency, Anesthesiology, Intensive Care, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy;
| | - Simonetta Baraldo
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (S.B.); (M.S.)
| | - Marina Saetta
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35122 Padova, Italy; (S.B.); (M.S.)
| | - Michele Fusaro
- Department of Radiology, Oderzo City Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31046 Oderzo, Italy;
| | - Giovanni Morana
- Department of Radiology, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (N.L.); (N.R.); (G.M.)
| | - Micaela Romagnoli
- Pulmonology Unit, Ca’ Foncello Hospital, Azienda Unità Locale Socio-Sanitaria 2 Marca Trevigiana, 31100 Treviso, Italy; (P.P.); (A.F.); (C.C.); (M.C.); (N.M.); (F.S.); (M.S.); (M.T.); (F.Z.); (G.Z.); (F.Z.); (M.R.)
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20
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Abdolrahimzadeh Fard H, Mahmudi-Azer S, Abdulzahraa Yaqoob Q, Sabetian G, Iranpour P, Shayan Z, Bolandparvaz S, Abbasi HR, Aminnia S, Salimi M, Mahmoudi MM, Paydar S, Borazjani R, Taheri Akerdi A, Zare M, Shayan L, Sasani M. Comparison of chest CT scan findings between COVID-19 and pulmonary contusion in trauma patients based on RSNA criteria: Established novel criteria for trauma victims. Chin J Traumatol 2022; 25:170-176. [PMID: 35101294 PMCID: PMC8769602 DOI: 10.1016/j.cjtee.2022.01.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 11/26/2021] [Accepted: 12/15/2021] [Indexed: 02/04/2023] Open
Abstract
PROPOSE In this study, we re-assessed the criteria defined by the radiological society of North America (RSNA) to determine novel radiological findings helping the physicians differentiating COVID-19 from pulmonary contusion. METHODS All trauma patients with blunt chest wall trauma and subsequent pulmonary contusion, COVID-19-related signs and symptoms before the trauma were enrolled in this retrospective study from February to May 2020. Included patients (Group P) were then classified into two groups based on polymerase chain reaction tests (Group Pa for positive patients and Pb for negative ones). Moreover, 44 patients from the pre-pandemic period (Group PP) were enrolled. They were matched to Group P regarding age, sex, and trauma-related scores. Two radiologists blindly reviewed the CT images of all enrolled patients according to criteria defined by the RSNA criteria. The radiological findings were compared between Group P and Group PP; statistically significant ones were re-evaluated between Group Pa and Group Pb thereafter. Finally, the sensitivity and specificity of each significant findings were calculated. The Chi-square test was used to compare the radiological findings between Group P and Group PP. RESULTS In the Group PP, 73.7% of all ground-glass opacities (GGOs) and 80% of all multiple bilateral GGOs were detected (p < 0.001 and p = 0.25, respectively). Single bilateral GGOs were only seen among the Group PP. The Chi-square tests showed that the prevalence of diffused GGOs, multiple unilateral GGOs, multiple consolidations, and multiple bilateral consolidations were significantly higher in the Group P (p = 0.001, 0.01, 0.003, and 0.003, respectively). However, GGOs with irregular borders and single consolidations were more significant among the Group PP (p = 0.01 and 0.003, respectively). Of note, reticular distortions and subpleural spares were exclusively detected in the Group PP. CONCLUSION We concluded that the criteria set by RSNA for the diagnosis of COVID-19 are not appropriate in trauma patients. The clinical signs and symptoms are not always useful either. The presence of multiple unilateral GGOs, diffused GGOs, and multiple bilateral consolidations favor COVID-19 with 88%, 97.62%, and 77.7% diagnostic accuracy.
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Affiliation(s)
- Hossein Abdolrahimzadeh Fard
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Salahaddin Mahmudi-Azer
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Golnar Sabetian
- Department of Intensive Care Medicine, Trauma Research Center, Shahid Rajaee (Emtiaz) Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Pooya Iranpour
- Department of Radiology, Medical Imaging Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Zahra Shayan
- Department of Biostatistics, Trauma Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahram Bolandparvaz
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hamid Reza Abbasi
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shiva Aminnia
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Maryam Salimi
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammad Mehdi Mahmoudi
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Shahram Paydar
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Roham Borazjani
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ali Taheri Akerdi
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Masome Zare
- Trauma Intensive Care Unit, Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Leila Shayan
- Trauma Research Center, Shahid Rajaee (Emtiaz) Trauma Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohammadreza Sasani
- Department of Radiology, Medical Imaging Research Center, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran,Corresponding author.
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21
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Yurasakpong L, Asuvapongpatana S, Weerachatyanukul W, Meemon K, Jongkamonwiwat N, Kruepunga N, Chaiyamoon A, Sudsang T, Iwanaga J, Tubbs RS, Suwannakhan A. Anatomical variants identified on chest computed tomography of 1000+ COVID-19 patients from an open-access dataset. Clin Anat 2022; 35:723-731. [PMID: 35385153 PMCID: PMC9083245 DOI: 10.1002/ca.23873] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/01/2022] [Accepted: 04/02/2022] [Indexed: 12/03/2022]
Abstract
Chest computed tomography (CT) has been the preferred imaging modality during the pandemic owing to its sensitivity in detecting COVID‐19 infections. Recently, a large number of COVID‐19 imaging datasets have been deposited in public databases, leading to rapid advances in COVID‐19 research. However, the application of these datasets beyond COVID‐19‐related research has been little explored. The authors believe that they could be used in anatomical research to elucidate the link between anatomy and disease and to study disease‐related alterations to normal anatomy. Therefore, the present study was designed to investigate the prevalence of six well‐known anatomical variants in the thorax using open‐access CT images obtained from over 1000 Iranian COVID‐19 patients aged between 6 and 89 years (60.9% male and 39.1% female). In brief, we found that the azygos lobe, tracheal bronchus, and cardiac bronchus were present in 0.8%, 0.2%, and 0% of the patients, respectively. Variations of the sternum, including sternal foramen, episternal ossicles, and sternalis muscle, were observed in 9.6%, 2.9%, and 1.5%, respectively. We believe anatomists could benefit from using open‐access datasets as raw materials for research because these datasets are freely accessible and are abundant, though further research is needed to evaluate the uses of other datasets from different body regions and imaging modalities. Radiologists should also be aware of these common anatomical variants when examining lung CTs, especially since the use of this imaging modality has increased during the pandemic.
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Affiliation(s)
- Laphatrada Yurasakpong
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand.,In Silico and Clinical Anatomy Research Group (iSCAN), Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand
| | | | | | - Krai Meemon
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand
| | | | - Nutmethee Kruepunga
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand.,In Silico and Clinical Anatomy Research Group (iSCAN), Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Arada Chaiyamoon
- Department of Anatomy, Faculty of Medicine, Khon Kaen University, KhonKaen, Thailand
| | - Thanwa Sudsang
- Department of Diagnostic and Therapeutic Radiology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Joe Iwanaga
- Department of Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana.,Department of Neurology, Tulane University School of Medicine, New Orleans, Louisiana.,Dental and Oral Medical Center, Kurume University School of Medicine, Fukuoka, Japan.,Department of Anatomy, Kurume University School of Medicine, Fukuoka, Japan
| | - R Shane Tubbs
- Department of Neurosurgery, Tulane University School of Medicine, New Orleans, Louisiana.,Department of Structural and Cellular Biology, Tulane University School of Medicine, New Orleans, Louisiana.,Department of Neurosurgery and Ochsner Neuroscience Institute, Ochsner Health System, New Orleans, Louisiana.,Department of Anatomical Sciences, St. George's University St. George.'s, Grenada
| | - Athikhun Suwannakhan
- Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand.,In Silico and Clinical Anatomy Research Group (iSCAN), Department of Anatomy, Faculty of Science, Mahidol University, Bangkok, Thailand
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22
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Laino ME, Ammirabile A, Lofino L, Lundon DJ, Chiti A, Francone M, Savevski V. Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence. Emerg Radiol 2022; 29:243-262. [PMID: 35048222 PMCID: PMC8769787 DOI: 10.1007/s10140-021-02008-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 12/03/2021] [Indexed: 01/08/2023]
Abstract
Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1-2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Ludovica Lofino
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Dara Joseph Lundon
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Humanitas Clinical and Research Center—IRCCS, Via Manzoni 56, 20089 Rozzano, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
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23
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Chest CT Scan Features to Predict COVID-19 Patients’ Outcome and Survival. Radiol Res Pract 2022; 2022:4732988. [PMID: 35256908 PMCID: PMC8898111 DOI: 10.1155/2022/4732988] [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] [Received: 04/11/2021] [Revised: 08/26/2021] [Accepted: 01/28/2022] [Indexed: 11/17/2022] Open
Abstract
Background. Providing efficient care for infectious coronavirus disease 2019 (COVID-19) patients requires an accurate and accessible tool to medically optimize medical resource allocation to high-risk patients. Purpose. To assess the predictive value of on-admission chest CT characteristics to estimate COVID-19 patients’ outcome and survival time. Materials and Methods. Using a case-control design, we included all laboratory-confirmed COVID-19 patients who were deceased, from June to September 2020, in a tertiary-referral-collegiate hospital and had on-admission chest CT as the case group. The patients who did not die and were equivalent in terms of demographics and other clinical features to cases were considered as the control (survivors) group. The equivalency evaluation was performed by a fellowship-trained radiologist and an expert radiologist. Pulmonary involvement (PI) was scored (0–25) using a semiquantitative scoring tool. The PI density index was calculated by dividing the total PI score by the number of involved lung lobes. All imaging parameters were compared between case and control group members. Survival time was recorded for the case group. All demographic, clinical, and imaging variables were included in the survival analyses. Results. After evaluating 384 cases, a total of 186 patients (93 in each group) were admitted to the studied setting, consisting of 126 (67.7%) male patients with a mean age of 60.4 ± 13.6 years. The PI score and PI density index in the case vs. the control group were on average 8.9 ± 4.5 vs. 10.7 ± 4.4 (
value: 0.001) and 2.0 ± 0.7 vs. 2.6 ± 0.8 (
value: 0.01), respectively. Axial distribution (p value: 0.01), cardiomegaly (
value: 0.005), pleural effusion (p value: 0.001), and pericardial effusion (
value: 0.04) were mostly observed in deceased patients. Our survival analyses demonstrated that PI score ≥ 10 (
value: 0.02) and PI density index ≥ 2.2 (
value: 0.03) were significantly associated with a lower survival rate. Conclusion. On-admission chest CT features, particularly PI score and PI density index, are potential great tools to predict the patient’s clinical outcome.
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24
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Mehrabi Nejad MM, Moosaie F, Dehghanbanadaki H, Haji Ghadery A, Shabani M, Tabary M, Aryannejad A, SeyedAlinaghi S, Rezaei N. Immunogenicity of COVID-19 mRNA vaccines in immunocompromised patients: a systematic review and meta-analysis. Eur J Med Res 2022; 27:23. [PMID: 35151362 PMCID: PMC8840778 DOI: 10.1186/s40001-022-00648-5] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 01/28/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Immunocompromised (IC) patients are at higher risk of severe SARS-CoV-2 infection, morbidity, and mortality compared to the general population. They should be prioritized for primary prevention through vaccination. This study aimed to evaluate the efficacy of COVID-19 mRNA vaccines in IC patients through a systematic review and meta-analysis approach. METHOD PubMed-MEDLINE, Scopus, and Web of Science were searched for original articles reporting the immunogenicity of two doses of mRNA COVID-19 vaccines in adult patients with IC condition between June 1, 2020 and September 1, 2021. Meta-analysis was performed using either random or fixed effect according to the heterogeneity of the studies. Subgroup analysis was performed to identify potential sources of heterogeneity. RESULTS A total of 26 studies on 3207 IC patients and 1726 healthy individuals were included. The risk of seroconversion in IC patients was 48% lower than those in controls (RR = 0.52 [0.42, 0.65]). IC patients with autoimmune conditions were 54%, and patients with malignancy were 42% more likely to have positive seroconversion than transplant recipients (P < 0.01). Subgroup meta-analysis based on the type of malignancy, revealed significantly higher proportion of positive seroconversion in solid organ compared to hematologic malignancies (RR = 0.88 [0.85, 0.92] vs. 0.61 [0.44, 0.86], P = 0.03). Subgroup meta-analysis based on type of transplantation (kidney vs. others) showed no statistically significant between-group difference of seroconversion (P = 0.55). CONCLUSIONS IC patients, especially transplant recipients, developed lower immunogenicity with two-dose of COVID-19 mRNA vaccines. Among patients with IC, those with autoimmune conditions and solid organ malignancies are mostly benefited from COVID-19 vaccination. Findings from this meta-analysis could aid healthcare policymakers in making decisions regarding the importance of the booster dose or more strict personal protections in the IC patients.
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Affiliation(s)
- Mohammad-Mehdi Mehrabi Nejad
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Fatemeh Moosaie
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hojat Dehghanbanadaki
- Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Abdolkarim Haji Ghadery
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mahya Shabani
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Tabary
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Armin Aryannejad
- Experimental Medicine Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - SeyedAhmad SeyedAlinaghi
- Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran.
| | - Nima Rezaei
- Research Center for Immunodeficiencies, Children's Medical Center, Tehran University of Medical Sciences, Tehran, Iran.
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Qarib St, Keshavarz Blvd, 1419733141, Tehran, Iran.
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25
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Mehrabi Nejad MM, Salehi M, Azadbakht J, Jahani Z, Veisi P, Sedighi N, Salahshour S. Is target sign (bull's eye appearance) associated with adverse outcomes in COVID-19 patients? A case series and literature review. CASPIAN JOURNAL OF INTERNAL MEDICINE 2022; 13:270-276. [PMID: 35872681 PMCID: PMC9272949 DOI: 10.22088/cjim.13.0.270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/02/2021] [Accepted: 07/10/2021] [Indexed: 12/02/2022]
Abstract
BACKGROUND In COVID-19 pneumonia, chest CT scan plays a crucial role in diagnosing and closely monitoring lung parenchyma. The main reportedly chest CT features of novel coronavirus pneumonia (NCP) have been fully discussed in the literature, but there is still a paucity of reports on uncommon CT manifestations. CASE PRESENTATION Herewith, we have reported ten rRT-PCR confirmed COVID-19 patients with CT target signs (bull's eye appearance); additionally, we have reviewed previously reported cases. Reviewing the literature, we found eight COVID-19 patients with target sign in the literature. 18 patients were included with a median age of 43. 11 (61%) patients were males. In 87% of patients, the lesions developed within the second-week post symptom onset. These patients mostly experienced an extended hospital stay (median = 10 days), with 53.8% of cases being admitted in ICU. The in-hospital mortality rate was 23%. CONCLUSION Our findings indicate that lesions with a bull's eye appearance are not significantly associated with higher mortality in hospitalized COVID-19 patients.
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Affiliation(s)
- Mohammad-Mehdi Mehrabi Nejad
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran, Both authors contributed equally to this work
| | - Mohammadreza Salehi
- Department of Infectious Diseases and Tropical Medicines, Tehran University of Medical Sciences, Tehran, Iran, Both authors contributed equally to this work
| | - Javid Azadbakht
- Department of Radiology, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran
| | - Zahra Jahani
- Department of Infectious Diseases and Tropical Medicines, Tehran University of Medical Sciences, Tehran, Iran
| | - Parastoo Veisi
- Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Nahid Sedighi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Sedighi Salahshour
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran,Correspondence: Faeze Salahshour ,Advanced Diagnostic and Interventional Radiology (ADIR) Research Center, Medical Imaging Center, Tehran University of Medical Sciences (TUMS), Tehran, Iran. E-mail: , Tel: 0098 2166581535, Fax: 0098 2166581535
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Alicioglu B, Bayav M. Study of thymus volume and density in COVID-19 patients: Is there a correlation in terms of pulmonary CT severity score? THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022; 53:233. [PMCID: PMC9643947 DOI: 10.1186/s43055-022-00917-7] [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: 08/24/2022] [Accepted: 10/29/2022] [Indexed: 08/30/2023] Open
Abstract
Background Thymus has a pivotal role in combating infectious diseases. Although some reviews have been published about its critical role in COVID-19, there is not enough research. In this study, the size and density of thymus related to computed tomography pulmonary severity score (CT-SS) were researched. Results A total of 196 patients were analyzed with a mean age of 52.54 ± 18.78 years; 97 (49.5%) of them were RT-PCR (−) and 99 (50.5%) were RT-PCR (+). Within RT-PCR (+) group 62 (62.6%) of them had pneumonia with a mean CT-SS of 9.37 ± 8.83; within RT-PCR (−) group 20 (20.6%) of them had pneumonia with the mean CT-SS of 12.00 ± 10.18. CT-SS had moderate negative correlation with thymus volume and thymus maximum diameter in patients having nodular-type thymus (R = −0.591, P = 0.02; R = −0.515, P = 0.049, respectively). Homogenous fat infiltration was more commonly seen in RT-PCR (−) group while reticular and nodular types were commonly seen in RT-PCR (+) group (p = 0.015). The mean volume and maximum diameter of thymus were statistically significantly higher in RT-PCR (+) group (p = 0.027 and p = 0.048, respectively). Conclusion This study showed the higher thymic volume and maximum diameter and more involution in COVID-19 patients. CT-SS had a moderate negative correlation with thymus volume and thymus maximum diameter. Pneumonia was more frequent in COVID patients, but mean CT-SS of the non-COVID cases was higher.
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Affiliation(s)
- Banu Alicioglu
- Radiology Department, Faculty of Medicine, Zonguldak Bulent Ecevit University, 67100 Kozlu, Zonguldak, Turkey
| | - Murat Bayav
- Radiology Department, Faculty of Medicine, Zonguldak Bulent Ecevit University, 67100 Kozlu, Zonguldak, Turkey
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27
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Mehrabi Z, Salimi M, Niknam K, Mohammadi F, Mamaghani HJ, Sasani MR, Ashraf MJ, Salimi A, Zahedroozegar MH, Erfani Z. Sinoorbital Mucormycosis Associated with Corticosteroid Therapy in COVID-19 Infection. Case Rep Ophthalmol Med 2021; 2021:9745701. [PMID: 34745674 PMCID: PMC8568553 DOI: 10.1155/2021/9745701] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 10/21/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Mucormycosis is a rare and invasive fungal infection, affecting almost exclusively immunocompromised individuals. Immunosuppressive effects of corticosteroids which are widely prescribed in COVID-19 patients might be a predisposing factor for opportunistic infections even though the other factors should also be considered. Case Presentation. A middle-aged man without any significant past medical history was admitted to the hospital due to a severe COVID-19 infection. He received a high dose of corticosteroids as a part of the treatment. Five days after discharge, he presents with a headache and fever. Eventually, orbital mucormycosis was diagnosed for him and he was treated with antifungal medications. CONCLUSION Opportunistic infections should be considered during the current pandemic of COVID-19, during which corticosteroids are widely prescribed.
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Affiliation(s)
- Zeinab Mehrabi
- Department of Internal Medicine, Namazi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Maryam Salimi
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Kianoush Niknam
- Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Hesan Jelodari Mamaghani
- Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Sasani
- Department of Radiology, Medical Imaging Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Amirhossein Salimi
- Student Research Committee, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | | | - Zohreh Erfani
- Students' Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran
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Abkhoo A, Shaker E, Mehrabinejad MM, Azadbakht J, Sadighi N, Salahshour F. Factors Predicting Outcome in Intensive Care Unit-Admitted COVID-19 Patients: Using Clinical, Laboratory, and Radiologic Characteristics. Crit Care Res Pract 2021; 2021:9941570. [PMID: 34306751 PMCID: PMC8285200 DOI: 10.1155/2021/9941570] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/23/2021] [Accepted: 06/23/2021] [Indexed: 01/09/2023] Open
Abstract
PURPOSE To investigate the factors contributing to mortality in coronavirus disease 2019 (COVID-19) patients admitted in the intensive care unit (ICU) and design a model to predict the mortality rate. METHOD We retrospectively evaluated the medical records and CT images of the ICU-admitted COVID-19 patients who had an on-admission chest CT scan. We analyzed the patients' demographic, clinical, laboratory, and radiologic findings and compared them between survivors and nonsurvivors. RESULTS Among the 121 enrolled patients (mean age, 62.2 ± 14.0 years; male, 82 (67.8%)), 41 (33.9%) survived, and the rest succumbed to death. The most frequent radiologic findings were ground-glass opacity (GGO) (71.9%) with peripheral (38.8%) and bilateral (98.3%) involvement, with lower lobes (94.2%) predominancy. The most common additional findings were cardiomegaly (63.6%), parenchymal band (47.9%), and crazy-paving pattern (44.4%). Univariable analysis of radiologic findings showed that cardiomegaly (p : 0.04), pleural effusion (p : 0.02), and pericardial effusion (p : 0.03) were significantly more prevalent in nonsurvivors. However, the extension of pulmonary involvement was not significantly different between the two subgroups (11.4 ± 4.1 in survivors vs. 11.9 ± 5.1 in nonsurvivors, p : 0.59). Among nonradiologic factors, advanced age (p : 0.002), lower O2 saturation (p : 0.01), diastolic blood pressure (p : 0.02), and hypertension (p : 0.03) were more commonly found in nonsurvivors. There was no significant difference between survivors and nonsurvivors in terms of laboratory findings. Three following factors remained significant in the backward logistic regression model: O2 saturation (OR: 0.91 (95% CI: 0.84-0.97), p : 0.006), pericardial effusion (6.56 (0.17-59.3), p : 0.09), and hypertension (4.11 (1.39-12.2), p : 0.01). This model had 78.7% sensitivity, 61.1% specificity, 90.0% positive predictive value, and 75.5% accuracy in predicting in-ICU mortality. CONCLUSION A combination of underlying diseases, vital signs, and radiologic factors might have prognostic value for mortality rate prediction in ICU-admitted COVID-19 patients.
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Affiliation(s)
- Aminreza Abkhoo
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Elaheh Shaker
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad-Mehdi Mehrabinejad
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Javid Azadbakht
- Department of Radiology, Faculty of Medicine, Kashan University of Medical Sciences, Kashan, Iran
| | - Nahid Sadighi
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Faeze Salahshour
- Department of Radiology, School of Medicine, Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Imam Khomeini Hospital, Tehran University of Medical Sciences, Tehran, Iran
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29
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Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Bonten MMJ, Dahly DL, Damen JAA, Debray TPA, de Jong VMT, De Vos M, Dhiman P, Haller MC, Harhay MO, Henckaerts L, Heus P, Kammer M, Kreuzberger N, Lohmann A, Luijken K, Ma J, Martin GP, McLernon DJ, Andaur Navarro CL, Reitsma JB, Sergeant JC, Shi C, Skoetz N, Smits LJM, Snell KIE, Sperrin M, Spijker R, Steyerberg EW, Takada T, Tzoulaki I, van Kuijk SMJ, van Bussel B, van der Horst ICC, van Royen FS, Verbakel JY, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons KGM, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020; 369:m1328. [PMID: 32265220 PMCID: PMC7222643 DOI: 10.1136/bmj.m1328] [Citation(s) in RCA: 1720] [Impact Index Per Article: 344.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/31/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.
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Affiliation(s)
- Laure Wynants
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Georg Heinze
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Marc M J Bonten
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Medical Microbiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Darren L Dahly
- HRB Clinical Research Facility, Cork, Ireland
- School of Public Health, University College Cork, Cork, Ireland
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Valentijn M T de Jong
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten De Vos
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT Stadius, KU Leuven, Leuven, Belgium
| | - Paul Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Musculoskeletal Sciences, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Maria C Haller
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Ordensklinikum Linz, Hospital Elisabethinen, Department of Nephrology, Linz, Austria
| | - Michael O Harhay
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Palliative and Advanced Illness Research Center and Division of Pulmonary and Critical Care Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liesbet Henckaerts
- Department of Microbiology, Immunology and Transplantation, KU Leuven-University of Leuven, Leuven, Belgium
- Department of General Internal Medicine, KU Leuven-University Hospitals Leuven, Leuven, Belgium
| | - Pauline Heus
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Michael Kammer
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Nina Kreuzberger
- Evidence-Based Oncology, Department I of Internal Medicine and Centre for Integrated Oncology Aachen Bonn Cologne Dusseldorf, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Anna Lohmann
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Kim Luijken
- Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, Netherlands
| | - Jie Ma
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Glen P Martin
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | - David J McLernon
- Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Chunhu Shi
- Division of Nursing, Midwifery and Social Work, School of Health Sciences, University of Manchester, Manchester, UK
| | - Nicole Skoetz
- Department of Nephrology, Medical University of Vienna, Vienna, Austria
| | - Luc J M Smits
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
| | - Kym I E Snell
- Centre for Prognosis Research, School of Primary, Community and Social Care, Keele University, Keele, UK
| | - Matthew Sperrin
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - René Spijker
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Medical Library, Netherlands
| | - Ewout W Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, Netherlands
| | - Toshihiko Takada
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College London School of Public Health, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Sander M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre+, Maastricht, Netherlands
| | - Bas van Bussel
- Department of Epidemiology, CAPHRI Care and Public Health Research Institute, Maastricht University, Peter Debyeplein 1, 6229 HA Maastricht, Netherlands
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Iwan C C van der Horst
- Department of Intensive Care, Maastricht University Medical Centre+, Maastricht University, Maastricht, Netherlands
| | - Florien S van Royen
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Jan Y Verbakel
- EPI-Centre, Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Christine Wallisch
- Section for Clinical Biometrics, Centre for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria
- Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Jack Wilkinson
- Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
| | | | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
- Cochrane Netherlands, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
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