|
1
|
Hu Y, Lu Y, Dong J, Xia D, Li J, Wang H, Rao M, Wang C, Tong W. Epidemiological and clinical characteristics of COVID-19 mortality: a retrospective study. Front Med (Lausanne) 2025; 12:1464274. [PMID: 40130249 PMCID: PMC11930819 DOI: 10.3389/fmed.2025.1464274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2024] [Accepted: 02/25/2025] [Indexed: 03/26/2025] Open
Abstract
Background The global impact of SARS-CoV-2 and its associated coronavirus disease (COVID-19) has necessitated urgent characterization of prognostic biomarkers. This study aimed to delineate the epidemiological and clinical predictors of mortality among hospitalized COVID-19 patients. Methods A retrospective cohort study was conducted on 123 patients with laboratory-confirmed COVID-19 admitted to Huoshenshan Hospital (Wuhan, China) from 1 February 2020 to 30 April 2020. Kaplan-Meier curve and multivariate Cox regression were used to assess the independent factors with survival time. Statistical significance was set at a p-value of <0.05. Results The cohort exhibited a mortality rate of 49.6% (61/123), with the critical clinical type (HR = 7.970, p = 0.009), leukocytosis (HR = 3.408, p = 0.006), and lymphopenia (HR = 0.817, p = 0.038) emerging as independent predictors of reduced survival. Critical-type patients demonstrated significantly elevated inflammatory markers (neutrophils: 10.41 ± 6.23 × 109/L; CRP: 104.47 ± 29.18 mg/L) and coagulopathy (D-dimer: 5.21 ± 2.34 μg/ml) compared to non-critical cases. Deceased patients exhibited pronounced metabolic derangements, including hyperglycemia (9.81 ± 2.07 mmol/L) and hepatic dysfunction (ALP: 174.03 ± 30.13 U/L). Conclusion We revealed the epidemiological and clinical features of different clinical types of SARS-CoV-2 as summarized in this paper. We found that critical type, leukocyte, and lymphocyte are risk factors that affect survival time, which could be an early and helpful marker to improve management of COVID-19 patients.
Collapse
Affiliation(s)
- Yaohua Hu
- Department of Respiratory and Critical Care Medicine, Naval Medical Center of People’s Liberation Army, Shanghai, China
| | - You Lu
- Department of Respiratory Medicine, Shanghai Tenth People’s Hospital, Shanghai, China
| | - Jiagui Dong
- Department of Respiratory and Critical Care Medicine, Naval Medical Center of People’s Liberation Army, Shanghai, China
| | - Delin Xia
- Department of Respiratory and Critical Care Medicine, Naval Medical Center of People’s Liberation Army, Shanghai, China
| | - Jin Li
- Department of Respiratory and Critical Care Medicine, Naval Medical Center of People’s Liberation Army, Shanghai, China
| | - Hong Wang
- Department of Respiratory and Critical Care Medicine, Naval Medical Center of People’s Liberation Army, Shanghai, China
| | - Min Rao
- Department of Respiratory and Critical Care Medicine, Naval Medical Center of People’s Liberation Army, Shanghai, China
| | - Chenxing Wang
- Department of Respiratory and Critical Care Medicine, Naval Medical Center of People’s Liberation Army, Shanghai, China
| | - Wanning Tong
- Department of Respiratory and Critical Care Medicine, Naval Medical Center of People’s Liberation Army, Shanghai, China
| |
Collapse
|
|
2
|
Gavriliţă GD, Ungureanu Ş, Dăescu E, Gavriliță MN, Ţîncu CC, Enache A. Nosocomial Bacterial Bronchopneumonia and SARS-CoV-2 Pneumonia in Patients with Traumatic Injuries: Imaging Aspects and Macroscopic and Microscopic Findings of Lung Tissue. Diagnostics (Basel) 2024; 14:2737. [PMID: 39682644 DOI: 10.3390/diagnostics14232737] [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: 09/04/2024] [Revised: 10/04/2024] [Accepted: 12/03/2024] [Indexed: 12/18/2024] Open
Abstract
BACKGROUND Patients with traumatic injuries often represent the best hosts for healthcare-associated infections, especially pneumonia or bronchopneumonia. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic raised serious problems in the diagnosis and treatment of patients that had a SARS-CoV-2 infection and associated nosocomial bacterial bronchopneumonia. In forensic medicine, these aspects need to be considered when establishing the cause of death and the distinction between the two types of bronchopneumonia is of particular importance. METHODS We present nine cases that were autopsied at the Institute of Forensic Medicine Timisoara between 1 June 2020 and 31 December 2021, that presented traumatic injuries, a SARS-CoV-2 infection, and bronchopneumonia. RESULTS We focused on the main findings of the macroscopic and microscopic aspects of lung tissues. CONCLUSIONS We consider that the aspects we highlighted in this study, can be very useful in forensic practice in cases with a pluri-factorial pathology.
Collapse
Affiliation(s)
- Georgiana-Denisa Gavriliţă
- Doctoral School, Victor Babes University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
- Institute of Forensic Medicine Timisoara, 300041 Timisoara, Romania
- Ethics and Human Identification Research Center, Department of Neuroscience, Discipline of Forensic Medicine, Bioethics, Deontology and Medical Law, Victor Babes University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
| | - Ştefania Ungureanu
- Doctoral School, Victor Babes University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
- Institute of Forensic Medicine Timisoara, 300041 Timisoara, Romania
- Ethics and Human Identification Research Center, Department of Neuroscience, Discipline of Forensic Medicine, Bioethics, Deontology and Medical Law, Victor Babes University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
- Discipline of Forensic Medicine, Bioethics, Deontology and Medical Law, Department of Neuroscience, Victor Babes University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
| | - Ecaterina Dăescu
- Institute of Forensic Medicine Timisoara, 300041 Timisoara, Romania
- Department I of Anatomy and Embryology, "Victor Babes" University of Medicine and Pharmacy Timișoara, 300041 Timisoara, Romania
| | | | | | - Alexandra Enache
- Institute of Forensic Medicine Timisoara, 300041 Timisoara, Romania
- Ethics and Human Identification Research Center, Department of Neuroscience, Discipline of Forensic Medicine, Bioethics, Deontology and Medical Law, Victor Babes University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
- Discipline of Forensic Medicine, Bioethics, Deontology and Medical Law, Department of Neuroscience, Victor Babes University of Medicine and Pharmacy Timisoara, 300041 Timisoara, Romania
| |
Collapse
|
|
3
|
Sanampudi S, Kypreos M, Chabbra S, Batra K. Paraseptal Lucencies Mimicking Emphysema in a Non-smoker With Acute Lung Injury in COVID-19. Cureus 2024; 16:e71010. [PMID: 39507157 PMCID: PMC11540043 DOI: 10.7759/cureus.71010] [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] [Accepted: 10/07/2024] [Indexed: 11/08/2024] Open
Abstract
Paraseptal emphysema can be smoking-related but has other causes, including surfactant deficiency, COVID-19, and age. The typical acute chest tomographic findings of COVID-19 include bilateral ground-glass opacities with or without consolidation and interstitial thickening in a peripheral and posterior predominant distribution. Evolution of these findings can occur and ultimately lead to fibrosis. The development of bullae, pneumomediastinum, and pneumothorax can occur as complications of non-invasive or mechanical ventilation. This case report describes incidental paraseptal lucencies that mimicked paraseptal emphysema in a patient with acute hypoxemic respiratory failure secondary to COVID-19 without a prior history of smoking only requiring a high-flow nasal cannula.
Collapse
Affiliation(s)
- Sreeja Sanampudi
- Radiology, University of Texas Southwestern Medical Center, Dallas, USA
| | - Margaret Kypreos
- Pulmonology and Critical Care, University of Texas Southwestern Medical Center, Dallas, USA
| | | | - Kiran Batra
- Cardiothoracic Imaging, University of Texas Southwestern Medical Center, Dallas, USA
| |
Collapse
|
|
4
|
Rahayu DRP, Rusli M, Bramantono B, Widyoningroem A. Association between chest X-ray score and clinical outcome in COVID-19 patients: A study on modified radiographic assessment of lung edema score (mRALE) in Indonesia. NARRA J 2024; 4:e691. [PMID: 38798849 PMCID: PMC11125424 DOI: 10.52225/narra.v4i1.691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/03/2024] [Indexed: 05/29/2024]
Abstract
Radiological examinations such as chest X-rays (CXR) play a crucial role in the early diagnosis and determining disease severity in coronavirus disease 2019 (COVID-19). Various CXR scoring systems have been developed to quantitively assess lung abnormalities in COVID-19 patients, including CXR modified radiographic assessment of lung edema (mRALE). The aim of this study was to determine the relationship between mRALE scores and clinical outcome (mortality), as well as to identify the correlation between mRALE score and the severity of hypoxia (PaO2/FiO2 ratio). A retrospective cohort study was conducted among hospitalized COVID-19 patients at Dr. Soetomo General Academic Hospital Surabaya, Indonesia, from February to April 2022. All CXR data at initial admission were scored using the mRALE scoring system, and the clinical outcomes at the end of hospitalization were recorded. Of the total 178 COVID-19 patients, 62.9% survived after completing the treatment. Patients within non-survived had significantly higher quick sequential organ failure assessment (qSOFA) score (p<0.001), lower PaO2/FiO2 ratio (p=0.004), and higher blood urea nitrogen (p<0.001), serum creatinine (p<0.008) and serum glutamic oxaloacetic transaminase (p=0.001) levels. There was a significant relationship between mRALE score and clinical outcome (survived vs deceased) (p=0.024; contingency coefficient of 0.184); and mRALE score of ≥2.5 served as a risk factor for mortality among COVID-19 patients (relative risk of 1.624). There was a significant negative correlation between the mRALE score and PaO2/FiO2 ratio based on the Spearman correlation test (r=-0.346; p<0.001). The findings highlight that the initial mRALE score may serve as an independent predictor of mortality among hospitalized COVID-19 patients as well as proves its potential prognostic role in the management of COVID-19.
Collapse
Affiliation(s)
- Dwi RP. Rahayu
- Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia
- Department of Internal Medicine, Dr. Soetomo General Academic Hospital, Surabaya, Indonesia
| | - Musofa Rusli
- Division of Tropical Medicine and Infectious Disease, Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia
- Division of Tropical Medicine and Infectious Disease, Dr. Soetomo General Academic Hospital, Surabaya, Indonesia
| | - Bramantono Bramantono
- Division of Tropical Medicine and Infectious Disease, Department of Internal Medicine, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia
- Division of Tropical Medicine and Infectious Disease, Dr. Soetomo General Academic Hospital, Surabaya, Indonesia
| | - Anita Widyoningroem
- Department of Radiology, Faculty of Medicine, Universitas Airlangga, Surabaya, Indonesia
- Department of Radiology, Dr. Soetomo General Academic Hospital, Surabaya, Indonesia
| |
Collapse
|
|
5
|
Fahrni G, Rocha AC, Gudmundsson L, Pozzessere C, Qanadli SD, Rotzinger DC. Impact of COVID-19 pneumonia on pulmonary vascular volume. Front Med (Lausanne) 2023; 10:1117151. [PMID: 37035332 PMCID: PMC10073514 DOI: 10.3389/fmed.2023.1117151] [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: 12/20/2022] [Accepted: 02/24/2023] [Indexed: 04/11/2023] Open
Abstract
Background Pulmonary manifestations of COVID-19 pneumonia are well known. However, COVID-19 is also associated with a range of vascular manifestations such as embolism, congestion, and perfusion changes. Regarding congestion, research from different groups has suggested arteriovenous anastomosis dysregulation as a contributing factor. In this study, we aim to better describe the changes in vascular volume in affected lung zones and to relate them to pathophysiological hypotheses. Methods We performed automatic vascular volume extraction in 10 chest CTs of patients, including 2 female and 8 male with a mean age of 63.5 ± 9.3 years, diagnosed with COVID-19 pneumonia. We compared the proportion of vascular volumes between manually segmented regions of lung parenchyma with and without signs of pneumonia. Results The proportion of vascular volume was significantly higher in COVID (CVasc) compared to non-COVID (NCVasc) areas. We found a mean difference (DVasc) of 5% and a mean ratio (RVasc) of 3.7 between the two compartments (p < 0.01). Conclusion Vascular volume in COVID-19 affected lung parenchyma is augmented relative to normal lung parenchyma, indicating venous congestion and supporting the hypothesis of pre-existing intra-pulmonary arteriovenous shunts.
Collapse
Affiliation(s)
- Guillaume Fahrni
- Cardiothoracic and Vascular Division, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Ana-Carolina Rocha
- Cardiothoracic and Vascular Division, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Louis Gudmundsson
- Cardiothoracic and Vascular Division, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Chiara Pozzessere
- Cardiothoracic and Vascular Division, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Salah D. Qanadli
- Riviera Chablais Hospital and University of Lausanne, Lausanne, Switzerland
| | - David C. Rotzinger
- Cardiothoracic and Vascular Division, Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| |
Collapse
|
|
6
|
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.
Collapse
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
| |
Collapse
|
|
7
|
Banazadeh M, Olangian-Tehrani S, Sharifi M, Malek-Ahmadi M, Nikzad F, Doozandeh-Nargesi N, Mohammadi A, Stephens GJ, Shabani M. Mechanisms of COVID-19-induced cerebellitis. Curr Med Res Opin 2022; 38:2109-2118. [PMID: 36305796 DOI: 10.1080/03007995.2022.2141963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The COVID-19 pandemic caused by SARS-CoV2 has raised several important health concerns, not least increased mortality and morbidity. SARS-CoV2 can infect the central nervous system via hematogenous or transneuronal routes, acting through different receptors including ACE2, DPP4, and neuropilin 1 and cause several issues, including the focus here, cerebellitis. The cerebellum is an essential part of the CNS located adjacent to the brainstem with a complex micro and macroscopic structure. The cerebellum plays several physiological roles, such as coordination, cognition, and executive functioning. Damage to the cerebellum can lead to incoordination and ataxia. In our narrative review, we searched different databases from 2021 to 2022 with the keywords cerebellum and COVID-19; 247 studies were identified and reviewed, focusing on clinical studies and excluding non-clinical studies; 56 studies were finally included for analysis. SARS-CoV2 infection of the cerebellum can be seen to be assessed through many methods such as MRI, PET, CT, postmortem studies, and histological findings. These methodological studies have demonstrated that cerebellar infection with COVID-19 can bring about several sequelae: thrombosis, microbleed, hemorrhage, stroke, autoantibody production, ataxia, and widespread inflammation in the cerebellum. Such central effects are likely to exacerbate the known multiorgan effects of SARS-CoV2 and should also be considered as part of disease prognosis.
Collapse
Affiliation(s)
- Mohammad Banazadeh
- Pharmaceutical Sciences and Cosmetic Products Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Sepehr Olangian-Tehrani
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Avicennet, Tehran, Iran
| | - Melika Sharifi
- Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
- Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | | | - Farhad Nikzad
- Avicennet, Tehran, Iran
- Student Research Committee, International Campus, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | | | - Alireza Mohammadi
- School of Pharmacy, Guilan University of Medical Science, Rasht, Iran
| | - Gary J Stephens
- School of Pharmacy, University of Reading, Whiteknights, Reading, UK
| | - Mohammad Shabani
- Neuroscience Research Center, Neuropharmacology Institute, Kerman University of Medical Sciences, Kerman, Iran
| |
Collapse
|
|
8
|
del Valle R, Ballesteros Á, Calvo C, Sainz T, Mendez A, Grasa C, Molina PR, Mellado MJ, Sanz‐Santaeufemia FJ, Herrero B, Calleja L, Soriano‐Arandes A, Melendo S, Rincón‐López E, Hernánz A, Epalza C, García‐Baeza C, Rupérez‐García E, Berzosa A, Ocaña A, Villarroya‐Villalba A, Barrios A, Otheo E, Galán JC, Rodríguez MJ, Mesa JM, Domínguez‐Rodríguez S, Moraleda C, Tagarro A. Comparison of pneumonia features in children caused by SARS-CoV-2 and other viral respiratory pathogens. Pediatr Pulmonol 2022; 57:2374-2382. [PMID: 35754093 PMCID: PMC9349806 DOI: 10.1002/ppul.26042] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 05/01/2022] [Accepted: 06/23/2022] [Indexed: 01/08/2023]
Abstract
BACKGROUND Pneumonia is a frequent manifestation of coronavirus disease 2019 (COVID-19) in hospitalized children. METHODS The study involved 80 hospitals in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spanish Pediatric National Cohort. Participants were children <18 years, hospitalized with SARS-CoV-2 community-acquired pneumonia (CAP). We compared the clinical and radiological characteristics of SARS-CoV-2-associated CAP with CAP due to other viral etiologies from ValsDance (retrospective) cohort. RESULTS In total, 151 children with SARS-CoV-2-associated CAP and 138 with other viral CAP were included. Main clinical features of SARS-CoV-2-associated CAP were cough, fever, or dyspnea. Lymphopenia was found in 43% patients and 15% required admission to the pediatric intensive care unit (PICU). Chest X-ray revealed condensation (42%) and other infiltrates (58%). Compared with CAP from other viral pathogens, COVID-19 patients were older, with lower C-reactive protein (CRP) levels, less wheezing, and greater need of mechanical ventilation (MV). There were no differences in the use of continuous positive airway pressure (CPAP) or HVF, or PICU admission between groups. CONCLUSION SARS-CoV-2-associated CAP in children presents differently to other virus-associated CAP: children are older and rarely have wheezing or high CRP levels; they need less oxygen but more CPAP or MV. However, several features overlap and differentiating the etiology may be difficult. The overall prognosis is good.
Collapse
Affiliation(s)
- Rut del Valle
- Pediatrics Department, Pediatrics Research Group, Hospital Universitario Infanta SofíaUniversidad Europea de MadridMadridSpain
| | - Álvaro Ballesteros
- Pediatric Research and Clinical Trials Unit (UPIC), Pediatrics Department, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Fundación de Investigación Biomédica Hospital 12 de OctubreRITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
| | - Cristina Calvo
- Pediatrics, Infectious and Tropical Diseases Department, Hospital Universitario La Paz, Instituto Investigación Hospital La Paz (IDIPaz)RITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
| | - Talía Sainz
- Pediatrics, Infectious and Tropical Diseases Department, Hospital Universitario La Paz, Instituto Investigación Hospital La Paz (IDIPaz)RITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
- Research Center, Centro de Investigación en Red en Enfermedades Infecciosas (CIBERINFEC)Instituto de Salud Carlos III, Madrid, SpainMadridSpain
| | - Ana Mendez
- Pediatrics, Infectious and Tropical Diseases Department, Hospital Universitario La Paz, Instituto Investigación Hospital La Paz (IDIPaz)RITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
| | - Carlos Grasa
- Pediatrics, Infectious and Tropical Diseases Department, Hospital Universitario La Paz, Instituto Investigación Hospital La Paz (IDIPaz)RITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
| | - Paula R. Molina
- Pediatrics, Infectious and Tropical Diseases Department, Hospital Universitario La Paz, Instituto Investigación Hospital La Paz (IDIPaz)RITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
| | - María J. Mellado
- Pediatrics, Infectious and Tropical Diseases Department, Hospital Universitario La Paz, Instituto Investigación Hospital La Paz (IDIPaz)RITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
| | | | - Blanca Herrero
- Pediatrics DepartmentHospital Universitario Niño JesúsMadridSpain
| | - Lourdes Calleja
- Pediatrics DepartmentHospital Universitario Niño JesúsMadridSpain
| | - Antoni Soriano‐Arandes
- Infectious Diseases and Pediatric Immunology Unit, Department of PediatricsHospital Universitario Vall d'HebronBarcelonaSpain
| | - Susana Melendo
- Infectious Diseases and Pediatric Immunology Unit, Department of PediatricsHospital Universitario Vall d'HebronBarcelonaSpain
| | - Elena Rincón‐López
- Pediatric Infectious Diseases Unit, Department of PediatricsHospital Universitario Gregorio MarañónMadridSpain
| | - Alicia Hernánz
- Pediatric Infectious Diseases Unit, Department of PediatricsHospital Universitario Gregorio MarañónMadridSpain
- Research CenterGregorio Marañón Research Institute (IiSGM)MadridSpain
| | - Cristina Epalza
- Pediatric Research and Clinical Trials Unit (UPIC), Pediatrics Department, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Fundación de Investigación Biomédica Hospital 12 de OctubreRITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
- Pediatric Infectious Diseases Unit, Department of PediatricsHospital Universitario 12 de OctubreMadridSpain
| | - Carmen García‐Baeza
- Pediatric Infectious Diseases Unit, Department of PediatricsHospital Universitario 12 de OctubreMadridSpain
| | | | - Arantxa Berzosa
- Pediatrics DepartmentHospital Universitario Clínico San CarlosMadridSpain
| | - Angustias Ocaña
- Pediatric Intensive Care Unit DepartmentHospital La MoralejaMadridSpain
| | - Alvaro Villarroya‐Villalba
- Pediatric Infectious Diseases Unit, Pediatrics DepartmentHospital Universitari i Politècnic La FeValenciaSpain
| | - Ana Barrios
- Pediatrics Department, Pediatrics Research Group, Hospital Universitario Infanta SofíaUniversidad Europea de MadridMadridSpain
| | - Enrique Otheo
- Pediatrics Department, Hospital Universitario Ramón y CajalUniversidad de Alcalá MadridMadridSpain
| | - Juan C. Galán
- Microbiology Department, Hospital Universitario Ramón y CajalInstituto Ramón y Cajal para la Investigación Sanitaria (IRYCIS)MadridSpain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP)MadridSpain
| | - Mario José Rodríguez
- Microbiology Department, Hospital Universitario Ramón y CajalInstituto Ramón y Cajal para la Investigación Sanitaria (IRYCIS)MadridSpain
- Centro de Investigación Biomédica en Red en Epidemiología y Salud Pública (CIBERESP)MadridSpain
| | - Juan M. Mesa
- Pediatrics Department, Pediatrics Research Group, Hospital Universitario Infanta SofíaUniversidad Europea de MadridMadridSpain
| | - Sara Domínguez‐Rodríguez
- Pediatric Research and Clinical Trials Unit (UPIC), Pediatrics Department, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Fundación de Investigación Biomédica Hospital 12 de OctubreRITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
| | - Cinta Moraleda
- Pediatric Research and Clinical Trials Unit (UPIC), Pediatrics Department, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Fundación de Investigación Biomédica Hospital 12 de OctubreRITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
| | - Alfredo Tagarro
- Pediatrics Department, Pediatrics Research Group, Hospital Universitario Infanta SofíaUniversidad Europea de MadridMadridSpain
- Pediatric Research and Clinical Trials Unit (UPIC), Pediatrics Department, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Fundación de Investigación Biomédica Hospital 12 de OctubreRITIP (Translational Research Network in Paediatric Infectious Diseases)MadridSpain
| |
Collapse
|
|
9
|
ACAR H, YAMANOĞLU A, ARIKAN C, BİLGİN S, AKYOL PY, KAYALI A, KARAKAYA Z. COVID-19 triajında CLUE protokolünün etkinliği. CUKUROVA MEDICAL JOURNAL 2022. [DOI: 10.17826/cumj.1086062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Purpose: The purpose of this study was to evaluate the effectiveness of the CLUE protocol in COVID-19 triage.
Materials and Methods: Patients who presented to the emergency department due to dyspnea with oxygen saturation below 95 % and were diagnosed with COVID-19 by reverse transcription polymerase chain reaction (RT-PCR) tests were included in this prospective, observational study. Patients included in the study underwent lung ultrasound (LUS) in the light of the CLUE protocol, and were accordingly given LUS scores of between 0 and 36, also within the scope of the protocol. Patients were placed under observation, and clinical outcomes of discharge from the emergency department, admission to the ward, and admission to intensive care or discharge were recorded. ROC analysis was applied in the calculation of threshold values for LUS scores predicting discharge, admission to intensive care, and mortality.
Results: Forty-five patients with a mean age of 63 ± 18 years were included in the study. Fifteen patients (33 %) were treated on an outpatient basis and discharged, while 12 (27 %) were admitted to the ward and 18 (40 %) to the intensive care unit. Mortality occurred in 15 (33 %) patients. An LUS score lower than 3 was 97 % sensitive and 80 % specific for discharge, a score greater than 10 was 94 % sensitive and 78 % specific for admission to the intensive care unit, and a score higher than 11 was 93 % sensitive and 87 % specific for mortality. Based on regression analysis, an LUS score higher than 10 emerged as an independent risk factor for intensive care requirement, a score lower than 3 for discharge, and a score over 11 for mortality.
Conclusion: The CLUE protocol may be a useful bedside test in COVID-19 triage, and one that does not involve radiation or require laboratory tests.
Collapse
Affiliation(s)
| | - Adnan YAMANOĞLU
- İzmir Katip Çelebi Üniversitesi, Atatürk Eğitim ve Araştırma Hastanesi
| | | | | | | | | | | |
Collapse
|
|
10
|
Vieira WDB, Franco KMVDS, Dias ARN, Falcão ASC, Falcão LFM, Quaresma JAS, de Sousa RCM. Chest Computed Tomography Is an Efficient Method for Initial Diagnosis of COVID-19: An Observational Study. Front Med (Lausanne) 2022; 9:848656. [PMID: 35492320 PMCID: PMC9039662 DOI: 10.3389/fmed.2022.848656] [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: 01/04/2022] [Accepted: 03/14/2022] [Indexed: 12/15/2022] Open
Abstract
Coronavirus disease (COVID-19) is an infectious disease that can lead to pneumonia, pulmonary oedema, acute respiratory distress syndrome, multiple organ and system dysfunction, and death. This study aimed to verify the efficacy of chest computed tomography (CT) for the initial diagnosis of COVID-19. This observational, retrospective, cross-sectional study included 259 individuals who underwent clinical evaluation, blood collection, chest CT, and a reverse transcription polymerase chain reaction (RT-PCR) diagnostic test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during their course of treatment at a reference hospital in Belém, Pará, Brazil between April and June 2020. Inclusion criteria were flu-like symptoms in adults of both sexes. Individuals with an inconclusive COVID-19 molecular test or who had artifacts in the chest CT images were excluded. Parametric data were analyzed using Student-t-test and non-parametric data were analyzed using average test and Fisher exact test. Participants were divided into two groups: Group 1 (COVID-19 positive), n = 211 (124 males, 87 females), 51.8 ± 17.9 years old and Group 2 (COVID-19 negative), n = 48 (22 males, 26 females), 47.6 ± 18.6 years old. Most frequent symptoms were cough [Group 1 n = 199 (94%)/Group 2 n = 46 (95%)], fever [Group 1 n = 154 (72%)/Group 2 n = 28 (58%)], myalgia [Group 1 n = 172 (81%)/Group 2 n = 38 (79%)], dyspnoea [Group 1 n = 169 (80%) / Group 2 n = 37 (77%)], headache [Group 1 n = 163 (77%)/Group 2 n = 32 (66%)], and anosmia [Group 1 n = 154 (73%)/Group 2 n = 29 (60%)]. Group 1 had a higher proportion of ground-glass opacity [Group 1 n = 175 (83%)/Group 2 n = 24 (50%), 0.00], vascular enhancement sign [Group 1 n = 128 (60%)/Group 2 n = 15 (31%), 0.00], septal thickening [Group 1 n = 99 (47%)/Group 2 n = 13 (27%), 0.01], crazy-paving pattern [Group 1 n = 98 (46%) / Group 2 n = 13 (27%), 0.01], consolidations [Group 1 n = 92 (43%)/Group 2 n = 8 (16%), 0.00], and CO-RADS 4 and 5 [Group 1 n = 163 (77.25%)/Group 2 n = 24 (50%), 0.00] categories in chest CT. Chest CT, when available, was found to be an efficient method for the initial diagnosis and better management of individuals with COVID-19.
Collapse
Affiliation(s)
| | | | - Apio Ricardo Nazareth Dias
- Núcleo de Medicina Tropical, Universidade Federal do Pará, Belém, Brazil.,Centro de Ciências Biológicas e da Saúde, Universidade do Estado do Pará, Belém, Brazil
| | | | | | - Juarez Antonio Simões Quaresma
- Núcleo de Medicina Tropical, Universidade Federal do Pará, Belém, Brazil.,Centro de Ciências Biológicas e da Saúde, Universidade do Estado do Pará, Belém, Brazil
| | | |
Collapse
|
|
11
|
Ortiz S, Rojas F, Valenzuela O, Herrera LJ, Rojas I. Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System. J Pers Med 2022; 12:535. [PMID: 35455654 PMCID: PMC9027976 DOI: 10.3390/jpm12040535] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/21/2022] [Accepted: 03/24/2022] [Indexed: 12/18/2022] Open
Abstract
The coronavirus disease 2019 (COVID-19) has caused millions of deaths and one of the greatest health crises of all time. In this disease, one of the most important aspects is the early detection of the infection to avoid the spread. In addition to this, it is essential to know how the disease progresses in patients, to improve patient care. This contribution presents a novel method based on a hierarchical intelligent system, that analyzes the application of deep learning models to detect and classify patients with COVID-19 using both X-ray and chest computed tomography (CT). The methodology was divided into three phases, the first being the detection of whether or not a patient suffers from COVID-19, the second step being the evaluation of the percentage of infection of this disease and the final phase is to classify the patients according to their severity. Stratification of patients suffering from COVID-19 according to their severity using automatic systems based on machine learning on medical images (especially X-ray and CT of the lungs) provides a powerful tool to help medical experts in decision making. In this article, a new contribution is made to a stratification system with three severity levels (mild, moderate and severe) using a novel histogram database (which defines how the infection is in the different CT slices for a patient suffering from COVID-19). The first two phases use CNN Densenet-161 pre-trained models, and the last uses SVM with LDA supervised learning algorithms as classification models. The initial stage detects the presence of COVID-19 through X-ray multi-class (COVID-19 vs. No-Findings vs. Pneumonia) and the results obtained for accuracy, precision, recall, and F1-score values are 88%, 91%, 87%, and 89%, respectively. The following stage manifested the percentage of COVID-19 infection in the slices of the CT-scans for a patient and the results in the metrics evaluation are 0.95 in Pearson Correlation coefficient, 5.14 in MAE and 8.47 in RMSE. The last stage finally classifies a patient in three degrees of severity as a function of global infection of the lungs and the results achieved are 95% accurate.
Collapse
Affiliation(s)
- Sergio Ortiz
- School of Technology and Telecommunications Engineering, University of Granada, 18071 Granada, Spain; (F.R.); (L.J.H.)
| | - Fernando Rojas
- School of Technology and Telecommunications Engineering, University of Granada, 18071 Granada, Spain; (F.R.); (L.J.H.)
| | - Olga Valenzuela
- Department of Applied Mathematics, University of Granada, 18071 Granada, Spain;
| | - Luis Javier Herrera
- School of Technology and Telecommunications Engineering, University of Granada, 18071 Granada, Spain; (F.R.); (L.J.H.)
| | - Ignacio Rojas
- School of Technology and Telecommunications Engineering, University of Granada, 18071 Granada, Spain; (F.R.); (L.J.H.)
| |
Collapse
|
|
12
|
Wang J, Yang X, Zhou B, Sohn JJ, Zhou J, Jacob JT, Higgins KA, Bradley JD, Liu T. Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic. J Imaging 2022; 8:65. [PMID: 35324620 PMCID: PMC8952297 DOI: 10.3390/jimaging8030065] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 12/25/2022] Open
Abstract
Ultrasound imaging of the lung has played an important role in managing patients with COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.
Collapse
Affiliation(s)
- Jing Wang
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Xiaofeng Yang
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Boran Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - James J. Sohn
- Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23219, USA;
| | - Jun Zhou
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Jesse T. Jacob
- Division of Infectious Diseases, Department of Medicine, Emory University, Atlanta, GA 30322, USA;
| | - Kristin A. Higgins
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Jeffrey D. Bradley
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| | - Tian Liu
- Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA; (J.W.); (X.Y.); (B.Z.); (J.Z.); (K.A.H.); (J.D.B.)
| |
Collapse
|
|
13
|
de Carvalho Lima EN, Octaviano ALM, Piqueira JRC, Diaz RS, Justo JF. Coronavirus and Carbon Nanotubes: Seeking Immunological Relationships to Discover Immunotherapeutic Possibilities. Int J Nanomedicine 2022; 17:751-781. [PMID: 35241912 PMCID: PMC8887185 DOI: 10.2147/ijn.s341890] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/31/2022] [Indexed: 12/11/2022] Open
Abstract
Since December 2019, the world has faced an unprecedented pandemic crisis due to a new coronavirus disease, coronavirus disease-2019 (COVID-19), which has instigated intensive studies on prevention and treatment possibilities. Here, we investigate the relationships between the immune activation induced by three coronaviruses associated with recent outbreaks, with special attention to SARS-CoV-2, the causative agent of COVID-19, and the immune activation induced by carbon nanotubes (CNTs) to understand the points of convergence in immune induction and modulation. Evidence suggests that CNTs are among the most promising materials for use as immunotherapeutic agents. Therefore, this investigation explores new possibilities of effective immunotherapies for COVID-19. This study aimed to raise interest and knowledge about the use of CNTs as immunotherapeutic agents in coronavirus treatment. Thus, we summarize the most important immunological aspects of various coronavirus infections and describe key advances and challenges in using CNTs as immunotherapeutic agents against viral infections and the activation of the immune response induced by CNTs, which can shed light on the immunotherapeutic possibilities of CNTs.
Collapse
Affiliation(s)
- Elidamar Nunes de Carvalho Lima
- Telecommunication and Control Engineering Department, Polytechnic School of the University of São Paulo, São Paulo, Brazil
- Infectious Diseases Division, Department of Medicine, Federal University of São Paulo, São Paulo, Brazil
- Electronic Systems Engineering Department, Polytechnic School of the University of São Paulo, São Paulo, SP, CEP 05508-010, Brazil
| | - Ana Luiza Moraes Octaviano
- Telecommunication and Control Engineering Department, Polytechnic School of the University of São Paulo, São Paulo, Brazil
| | - José Roberto Castilho Piqueira
- Telecommunication and Control Engineering Department, Polytechnic School of the University of São Paulo, São Paulo, Brazil
| | - Ricardo Sobhie Diaz
- Infectious Diseases Division, Department of Medicine, Federal University of São Paulo, São Paulo, Brazil
| | - João Francisco Justo
- Electronic Systems Engineering Department, Polytechnic School of the University of São Paulo, São Paulo, SP, CEP 05508-010, Brazil
| |
Collapse
|
|
14
|
De Lucia F, Amer Ouali R, Devriendt A, Sanoussi S, Cannie M. Comparison of Chest Computed Tomography Between the Two Waves of Coronavirus Disease 2019 in Belgium Using Artificial Intelligence. Cureus 2022; 14:e22203. [PMID: 35308674 PMCID: PMC8926029 DOI: 10.7759/cureus.22203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2022] [Indexed: 12/05/2022] Open
Abstract
Background In this study, we aimed to compare two outbreaks of coronavirus disease 2019 (COVID-19) in Belgium in tomographic and biological-clinical aspects with artificial intelligence (AI). Methodology We performed an observational retrospective study. Adult patients who were symptomatic in the first seven days with COVID-19 infection, diagnosed by chest computed tomography (CT) and/or reverse transcription-polymerase chain reaction, were included in this study. The first wave of the pandemic lasted from March 25, 2020, to May 25, 2020, and the second wave lasted from October 7, 2020, to December 7, 2020. For each wave, two subgroups were defined depending on whether respiratory failure occurred during the course of the disease. The quantitative estimation of COVID-19 lung lesions was performed by AI, radiologists, and radiology residents. The chest CT severity score was calculated by AI. Results In the 202 patients included in this study, we found statistically significant differences for obesity, hypertension, and asthma. The differences were predominant in the second wave. Moreover, a mixed distribution (central and peripherical) of pulmonary lesions was noted in the second wave, but no differences were noted regarding mortality, respiratory failure, complications, and other radiological and biological elements. Chest CT severity score was among the risk factors of mortality and respiratory failure. There was a mild agreement between AI and visual evaluation of pulmonary lesion extension (K = 0.4). Conclusions Between March and December 2020, in our cohort, for the majority of the parameters analyzed, we did not record significant changes between the two waves. AI can reduce the experience and performance gap of radiologists and better establish a hospitalization criterion.
Collapse
|
|
15
|
Dhont J, Wolfs C, Verhaegen F. Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias? Med Phys 2022; 49:978-987. [PMID: 34951033 PMCID: PMC9015341 DOI: 10.1002/mp.15419] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 11/11/2022] Open
Abstract
PURPOSE Over the last 2 years, the artificial intelligence (AI) community has presented several automatic screening tools for coronavirus disease 2019 (COVID-19) based on chest radiography (CXR), with reported accuracies often well over 90%. However, it has been noted that many of these studies have likely suffered from dataset bias, leading to overly optimistic results. The purpose of this study was to thoroughly investigate to what extent biases have influenced the performance of a range of previously proposed and promising convolutional neural networks (CNNs), and to determine what performance can be expected with current CNNs on a realistic and unbiased dataset. METHODS Five CNNs for COVID-19 positive/negative classification were implemented for evaluation, namely VGG19, ResNet50, InceptionV3, DenseNet201, and COVID-Net. To perform both internal and cross-dataset evaluations, four datasets were created. The first dataset Valencian Region Medical Image Bank (BIMCV) followed strict reverse transcriptase-polymerase chain reaction (RT-PCR) test criteria and was created from a single reliable open access databank, while the second dataset (COVIDxB8) was created through a combination of six online CXR repositories. The third and fourth datasets were created by combining the opposing classes from the BIMCV and COVIDxB8 datasets. To decrease inter-dataset variability, a pre-processing workflow of resizing, normalization, and histogram equalization were applied to all datasets. Classification performance was evaluated on unseen test sets using precision and recall. A qualitative sanity check was performed by evaluating saliency maps displaying the top 5%, 10%, and 20% most salient segments in the input CXRs, to evaluate whether the CNNs were using relevant information for decision making. In an additional experiment and to further investigate the origin of potential dataset bias, all pixel values outside the lungs were set to zero through automatic lung segmentation before training and testing. RESULTS When trained and evaluated on the single online source dataset (BIMCV), the performance of all CNNs is relatively low (precision: 0.65-0.72, recall: 0.59-0.71), but remains relatively consistent during external evaluation (precision: 0.58-0.82, recall: 0.57-0.72). On the contrary, when trained and internally evaluated on the combinatory datasets, all CNNs performed well across all metrics (precision: 0.94-1.00, recall: 0.77-1.00). However, when subsequently evaluated cross-dataset, results dropped substantially (precision: 0.10-0.61, recall: 0.04-0.80). For all datasets, saliency maps revealed the CNNs rarely focus on areas inside the lungs for their decision-making. However, even when setting all pixel values outside the lungs to zero, classification performance does not change and dataset bias remains. CONCLUSIONS Results in this study confirm that when trained on a combinatory dataset, CNNs tend to learn the origin of the CXRs rather than the presence or absence of disease, a behavior known as short-cut learning. The bias is shown to originate from differences in overall pixel values rather than embedded text or symbols, despite consistent image pre-processing. When trained on a reliable, and realistic single-source dataset in which non-lung pixels have been masked, CNNs currently show limited sensitivity (<70%) for COVID-19 infection in CXR, questioning their use as a reliable automatic screening tool.
Collapse
Affiliation(s)
- Jennifer Dhont
- Department of Radiation Oncology (Maastro)GROW School for OncologyMaastricht University Medical Centre+Maastrichtthe Netherlands
| | - Cecile Wolfs
- Department of Radiation Oncology (Maastro)GROW School for OncologyMaastricht University Medical Centre+Maastrichtthe Netherlands
| | - Frank Verhaegen
- Department of Radiation Oncology (Maastro)GROW School for OncologyMaastricht University Medical Centre+Maastrichtthe Netherlands
| |
Collapse
|
|
16
|
Fayadoglu M, Ekinci İB, Fayadoglu E, Arslan H, Uzunkulaoğlu T. Analysis and classification of radiological results and epidemiology of patients with COVID-19 pneumonia. Medicine (Baltimore) 2021; 100:e28154. [PMID: 34941065 PMCID: PMC8701962 DOI: 10.1097/md.0000000000028154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 11/18/2021] [Indexed: 01/05/2023] Open
Abstract
The coronavirus disease-2019 (COVID-19) pneumonia which is caused by the severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) virus is the current urgent issue world over. According to the Health Ministry of Turkey, the first COVID-19 patient was diagnosed on March 11, 2020. Since then, approximately 5.5 million patients have been confirmed to be positive SARS CoV-2 virus. In this retrospective study, we aimed at analyzing the epidemiological and radiological findings of COVID-19 cases at the Hospital of Grand National Assembly of Turkey from April 1, 2020 to December 31, 2020.A total of 130 patients (84 male, 25-87 years) were diagnosed as positive with High Resolution Computed Tomography (HRCT) scans and 71 of them confirmed with positive Real Time Polymerase Chain Reaction using the patients' nasopharyngeal and throat samples.HRCT scans were classified into 4 stages. Stage I (39.2%) patients' group; the HRCT results were found to be mosaic perfusion, whereas Stage II (39.2%) were found to be Ground Glass Opacity. Also, consolidation was detected in Stage III (20%). Finally, Stage IV, considered the most severe lung findings, and named as a crazy paving pattern was determined in 2 patients (1.53%). Furthermore, 20% of patients were found to be positive using IgG antibody against to SARS CoV-2 virus.Our findings showed that HRCT could be most prominent technique compared to real time polymerase chain reaction for the diagnosis of COVID-19 pneumonia. The novel classification of HRCT findings will be helpful to early diagnosis of the disease, time saving and eventually cost-effective.
Collapse
Affiliation(s)
- Mustafa Fayadoglu
- Health Institutes of Turkey, Hospital of Grand National Assembly of Turkey COVID-19 Diagnosis Center, Çankaya, Ankara, Turkey
- Eskişehir Technical University, Faculty of Advanced Technology, Department of Biotechnology, Tepebaşi, Eskişehir, Turkey
- Contributed equally
| | - İlksen Berfin Ekinci
- Health Institutes of Turkey, Hospital of Grand National Assembly of Turkey COVID-19 Diagnosis Center, Çankaya, Ankara, Turkey
- Contributed equally
| | - Elif Fayadoglu
- Health Institutes of Turkey, Hospital of Grand National Assembly of Turkey COVID-19 Diagnosis Center, Çankaya, Ankara, Turkey
- Contributed equally
- Eskişehir Technical University, Faculty of Science, Department of Molecular Biology, Tepebaşi, Eskişehir, Turkey
| | - Hüseyin Arslan
- Hospital of Grand National Assembly of Turkey, Çankaya, Ankara, Turkey
| | | |
Collapse
|
|
17
|
Baysal B, Dogan MB, Gulbay M, Sorkun M, Koksal M, Bastug A, Kazancıoglu S, Ozbay BO, Icten S, Arslan F, Cag Y, Bodur H, Vahaboglu H. Predictive performance of CT for adverse outcomes among COVID-19 suspected patients: a two-center retrospective study. Bosn J Basic Med Sci 2021; 21:739-745. [PMID: 33577443 PMCID: PMC8554695 DOI: 10.17305/bjbms.2020.5466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Accepted: 02/08/2021] [Indexed: 11/16/2022] Open
Abstract
The aim of the study was to compare the performance of various computed tomography (CT) reporting tools, including zonal CT visual score (ZCVS), the number of involved lobes, and Radiological Society of North America (RSNA) categorization in predicting adverse outcomes among patients hospitalized due to the lower respiratory symptoms during the coronavirus disease 2019 (COVID-19) pandemic. A total of 405 patients admitted with severe respiratory symptoms who underwent a chest CT were enrolled. The primary adverse outcome was intensive care unit (ICU) admission of patients. Predictive performances of reporting tools were compared using the area under the receiver operating characteristic curves (AUC ROC). Among the 405 patients, 39 (9.63%) required ICU support during their hospital stay. At least two or more observers reported a typical and indeterminate COVID-19 pneumonia CT pattern according to RSNA categorization in 70% (285/405) of patients. Among these, 63% (179/285) had a positive polymerase chain reaction (PCR test for the SARS-CoV-2 virus. The median number of lobes involved according to CT was higher in patients who required ICU support (median interquartile range [IQR], 5[3; 5] vs. 3[0; 5]). The median ZCVS score was higher among the patients that subsequently required ICU support (median [IQR], 4[0; 12] vs. 13[5.75; 24]). The bootstrap comparisons of AUC ROC showed significant differences between reporting tools, and the ZCVS was found to be superior (AUC ROC, 71-75%). The ZCVS score at the first admission showed a linear and significant association with adverse outcomes among patients with the lower respiratory tract symptoms during the COVID-19 pandemic.
Collapse
Affiliation(s)
- Begumhan Baysal
- Department of Radiology, Istanbul Medeniyet University Goztepe Education and Research Hospital, Istanbul, Turkey
| | - Mahmut Bilal Dogan
- Department of Radiology, Istanbul Medeniyet University Goztepe Education and Research Hospital, Istanbul, Turkey
| | - Mutlu Gulbay
- Department of Radiology, University of Health Sciences Ankara City Hospital, Ankara, Turkey
| | - Mine Sorkun
- Department of Radiology, Istanbul Medeniyet University Goztepe Education and Research Hospital, Istanbul, Turkey
| | - Murathan Koksal
- Department of Radiology, University of Health Sciences Ankara City Hospital, Ankara, Turkey
| | - Aliye Bastug
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Ankara City Hospital, Ankara, Turkey
| | - Sumeyye Kazancıoglu
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Ankara City Hospital, Ankara, Turkey
| | - Bahadir Orkun Ozbay
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Ankara City Hospital, Ankara, Turkey
| | - Sacit Icten
- Department of Chest Disease, Istanbul Medenıyet Unıversıty Goztepe Education And Research Hospital, Istanbul, Turkey
| | - Ferhat Arslan
- Department of Infectious Diseases and Clinical Microbiology, Istanbul Medeniyet University Goztepe Education and Research Hospital, Istanbul, Turkey
| | - Yasemin Cag
- Department of Infectious Diseases and Clinical Microbiology, Istanbul Medeniyet University Goztepe Education and Research Hospital, Istanbul, Turkey
| | - Hurrem Bodur
- Department of Infectious Diseases and Clinical Microbiology, University of Health Sciences Ankara City Hospital, Ankara, Turkey
| | - Haluk Vahaboglu
- Department of Infectious Diseases and Clinical Microbiology, Istanbul Medeniyet University Goztepe Education and Research Hospital, Istanbul, Turkey
| |
Collapse
|
|
18
|
Alsheikh SH, Ibrahim K, AlFaraj D. The Impact of False Positive COVID-19 Result. Cureus 2021; 13:e20375. [PMID: 35036208 PMCID: PMC8752407 DOI: 10.7759/cureus.20375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/12/2021] [Indexed: 11/22/2022] Open
Abstract
The novel Coronavirus disease 2019 (COVID-19) pandemic has resulted in many adverse outcomes and challenges, and a false-positive result is one of them. Despite that this issue has a substantial impact, there is a scarcity in the literature of its prevalence or impact, and more knowledge is needed. This case report will present the case of a 54-years-old female who was misdiagnosed as COVID-19. The misleading COVID-19 diagnosis can result in significant consequences such as delaying surgeries, unnecessary quarantine and treatments, transplant lists omission, and unnecessary sick leaves. Moreover, as seen in our case, it delayed the other investigations and admitted a healthy patient to a COVID-19 isolation ward. Therefore, physicians should consider the possibility of false-positive results and utilize other investigation tools to further diagnose suspicious cases.
Collapse
Affiliation(s)
- Shahad H Alsheikh
- Medicine and Surgery, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Khaled Ibrahim
- Emergency Medicine, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| | - Dunya AlFaraj
- Emergency Medicine, Imam Abdulrahman Bin Faisal University, Dammam, SAU
| |
Collapse
|
|
19
|
Churruca M, Martínez-Besteiro E, Couñago F, Landete P. COVID-19 pneumonia: A review of typical radiological characteristics. World J Radiol 2021; 13:327-343. [PMID: 34786188 PMCID: PMC8567439 DOI: 10.4329/wjr.v13.i10.327] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/08/2021] [Accepted: 09/17/2021] [Indexed: 02/06/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) was first discovered after unusual cases of severe pneumonia emerged by the end of 2019 in Wuhan (China) and was declared a global public health emergency by the World Health Organization in January 2020. The new pathogen responsible for the infection, genetically similar to the beta-coronavirus family, is known as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and the current gold standard diagnostic tool for its detection in respiratory samples is the reverse transcription-polymerase chain reaction test. Imaging findings on COVID-19 have been widely described in studies published throughout last year, 2020. In general, ground-glass opacities and consolidations, with a bilateral and peripheral distribution, are the most typical patterns found in COVID-19 pneumonia. Even though much of the literature focuses on chest computed tomography (CT) and X-ray imaging and their findings, other imaging modalities have also been useful in the assessment of COVID-19 patients. Lung ultrasonography is an emerging technique with a high sensitivity, and thus useful in the initial evaluation of SARS-CoV-2 infection. In addition, combined positron emission tomography-CT enables the identification of affected areas and follow-up treatment responses. This review intends to clarify the role of the imaging modalities available and identify the most common radiological manifestations of COVID-19.
Collapse
Affiliation(s)
- María Churruca
- Pulmonology Department, Hospital Universitario de La Princesa, Madrid 28006, Spain
| | | | - Felipe Couñago
- Department of Radiation Oncology, Hospital Universitario Quirónsalud Madrid, Madrid 28223, Spain
- Department of Radiation Oncology, Hospital La Luz, Madrid 28003, Spain
- Clinical Department, Faculty of Biomedicine,Universidad Europea de Madrid, Madrid 28670, Spain
| | - Pedro Landete
- Department of Pneumology, Hospital Universitario de la Princesa, Universidad Autónoma de Madrid, Madrid 28006, Spain
| |
Collapse
|
|
20
|
Clinical Characteristics and Outcomes of Liver Transplantation Recipients With COVID-19 Pneumonia. Transplant Proc 2021; 53:2481-2489. [PMID: 34261580 PMCID: PMC8214214 DOI: 10.1016/j.transproceed.2021.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 06/15/2021] [Indexed: 01/31/2023]
Abstract
BACKGROUND We aimed to evaluate the clinical characteristics and outcomes of mild-severe COVID-19 pneumonia cases in liver transplant (LT) recipients. METHODS Ten LT recipients diagnosed as having COVID-19 pneumonia in a 6-month period in our transplantation center were included. Demographic and medical data of the recipients were retrospectively collected; clinical courses, treatment responses, and outcomes were evaluated. RESULTS Ten LT recipients were male, had a median age of 57 years (min-max, 36-69 years; interquartile range [IQR], 13 years), and had right lobe from living donor LT performed in a median of 11 months (min-max, 1-72 months; IQR, 12 months). Five patients had severe pneumonia, and the remaining patients had mild/moderate pneumonia. The most frequent symptoms were fever (90%) and cough (70%). Favipiravir, enoxaparin sodium, and corticosteroid were initiated at the time of the diagnosis; immunosuppressive drug doses were reduced or discontinued in 3 cases. Lymphopenia median: 510/mL (min-max, 90-1400 mL; IQR, 610 mL), increased levels of C-reactive protein median: 4.72 (min-max, 0.31-23.4; IQR, 8.5), and ferritin median: 641 (min-max, 40 to ≥ 1650; IQR, 1108) were frequent. Four patients required antibacterial treatments because of emerging bacterial pneumonia and/or sepsis. All patients were hospitalized for a median of 10 days. One patient with sepsis died on the 26th day after intensive care unit admission, and the remaining 9 survived. No further complication was recorded for 1-month follow-up. CONCLUSIONS Commencing favipiravir, enoxaparin sodium, and corticosteroid treatments; close follow-up of the developing complications; the temporary reduction or cessation of immunosuppression; a multidisciplinary approach; early awareness of the bacterial infections; and the initiation appropriate antibiotic treatments can contribute to success.
Collapse
|
|
21
|
Rocha ASCD, Volschan A, Campos LAA, Coelho RPDS, Thielmann DCDA, Ferreira CALC, Colafranceschi AS. Predictive Value of Myocardial injury in Patients with COVID-19 Admitted to a Quaternary Hospital in the City of Rio de Janeiro. INTERNATIONAL JOURNAL OF CARDIOVASCULAR SCIENCES 2021. [DOI: 10.36660/ijcs.20200352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
|
|
22
|
Fonseca EKUN, Loureiro BMC, Strabelli DG, de Farias LDPG, Garcia JVR, Gama VAA, Ferreira LC, Chate RC, Assunção AN, Sawamura MVY, Nomura CH. Evaluation of the RSNA and CORADS classifications for COVID-19 on chest computed tomography in the Brazilian population. Clinics (Sao Paulo) 2021; 76:e2476. [PMID: 33787655 PMCID: PMC7979034 DOI: 10.6061/clinics/2021/e2476] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 01/22/2021] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE To determine the correlation between the two tomographic classifications for coronavirus disease (COVID-19), COVID-19 Reporting and Data System (CORADS) and Radiological Society of North America Expert Consensus Statement on Reporting Chest Computed Tomography (CT) Findings Related to COVID-19 (RSNA), in the Brazilian population and to assess the agreement between reviewers with different experience levels. METHODS Chest CT images of patients with reverse transcriptase-polymerase chain reaction (RT-PCR)-positive COVID-19 were categorized according to the CORADS and RSNA classifications by radiologists with different levels of experience and who were initially unaware of the RT-PCR results. The inter- and intra-observer concordances for each of the classifications were calculated, as were the concordances between classifications. RESULTS A total of 100 patients were included in this study. The RSNA classification showed an almost perfect inter-observer agreement between reviewers with similar experience levels, with a kappa coefficient of 0.892 (95% confidence interval [CI], 0.788-0.995). CORADS showed substantial agreement among reviewers with similar experience levels, with a kappa coefficient of 0.642 (95% CI, 0.491-0.793). There was inter-observer variation when comparing less experienced reviewers with more experienced reviewers, with the highest kappa coefficient of 0.396 (95% CI, 0.255-0.588). There was a significant correlation between both classifications, with a Kendall coefficient of 0.899 (p<0.001) and substantial intra-observer agreement for both classifications. CONCLUSION The RSNA and CORADS classifications showed excellent inter-observer agreement for reviewers with the same level of experience, although the agreement between less experience reviewers and the reviewer with the most experience was only reasonable. Combined analysis of both classifications with the first RT-PCR results did not reveal any false-negative results for detecting COVID-19 in patients.
Collapse
Affiliation(s)
| | | | | | | | - José Vitor Rassi Garcia
- Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | | | - Lorena Carneiro Ferreira
- Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Rodrigo Caruso Chate
- Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | | | | | - Cesar Higa Nomura
- Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| |
Collapse
|
|
23
|
Rosa MEE, Matos MJRD, Furtado RSODP, Brito VM, Amaral LTW, Beraldo GL, Fonseca EKUN, Chate RC, Passos RBD, Teles GBDS, Silva MMA, Yokoo P, Yanata E, Shoji H, Szarf G, Funari MBDG. Reply to: Temporal evolution of tomographic findings of pulmonary infection in COVID-19. EINSTEIN-SAO PAULO 2020; 18:eCE6040. [PMID: 33053021 PMCID: PMC7531896 DOI: 10.31744/einstein_journal/2020ce6040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 07/28/2020] [Indexed: 11/05/2022] Open
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Patrícia Yokoo
- Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
| | - Elaine Yanata
- Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
| | - Hamilton Shoji
- Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
| | - Gilberto Szarf
- Hospital Israelita Albert Einstein, São Paulo, SP, Brazil
| | | |
Collapse
|
|
24
|
Affiliation(s)
- Miguel José Francisco Neto
- Faculdade Israelita de Ciências da Saúde Albert Einstein (FICSAE) - Hospital Albert Einstein, São Paulo, SP, Faculdade de Medicina da Universidade de São Paulo (FMUSP), São Paulo, SP, Brasil. E-mail:
| | - Marcos Roberto Gomes de Queiroz
- Faculdade Israelita de Ciências da Saúde Albert Einstein (FICSAE) - Hospital Albert Einstein, São Paulo, SP, Brasil. E-mail:
| |
Collapse
|