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Govindan S, Spicer A, Bearce M, Schaefer RS, Uhl A, Alterovitz G, Kim MJ, Carey KA, Shah NS, Winslow C, Gilbert E, Stey A, Weiss AM, Amin D, Karway G, Martin J, Edelson DP, Churpek MM. Development and Validation of a Machine Learning COVID-19 Veteran (COVet) Deterioration Risk Score. Crit Care Explor 2024; 6:e1116. [PMID: 39028867 PMCID: PMC11262818 DOI: 10.1097/cce.0000000000001116] [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] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND AND OBJECTIVE To develop the COVid Veteran (COVet) score for clinical deterioration in Veterans hospitalized with COVID-19 and further validate this model in both Veteran and non-Veteran samples. No such score has been derived and validated while incorporating a Veteran sample. DERIVATION COHORT Adults (age ≥ 18 yr) hospitalized outside the ICU with a diagnosis of COVID-19 for model development to the Veterans Health Administration (VHA) (n = 80 hospitals). VALIDATION COHORT External validation occurred in a VHA cohort of 34 hospitals, as well as six non-Veteran health systems for further external validation (n = 21 hospitals) between 2020 and 2023. PREDICTION MODEL eXtreme Gradient Boosting machine learning methods were used, and performance was assessed using the area under the receiver operating characteristic curve and compared with the National Early Warning Score (NEWS). The primary outcome was transfer to the ICU or death within 24 hours of each new variable observation. Model predictor variables included demographics, vital signs, structured flowsheet data, and laboratory values. RESULTS A total of 96,908 admissions occurred during the study period, of which 59,897 were in the Veteran sample and 37,011 were in the non-Veteran sample. During external validation in the Veteran sample, the model demonstrated excellent discrimination, with an area under the receiver operating characteristic curve of 0.88. This was significantly higher than NEWS (0.79; p < 0.01). In the non-Veteran sample, the model also demonstrated excellent discrimination (0.86 vs. 0.79 for NEWS; p < 0.01). The top three variables of importance were eosinophil percentage, mean oxygen saturation in the prior 24-hour period, and worst mental status in the prior 24-hour period. CONCLUSIONS We used machine learning methods to develop and validate a highly accurate early warning score in both Veterans and non-Veterans hospitalized with COVID-19. The model could lead to earlier identification and therapy, which may improve outcomes.
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Affiliation(s)
- Sushant Govindan
- MInDSET Service Line, Kansas City Veterans Affairs Hospital, Kansas City, MO
| | - Alexandra Spicer
- Division of Allergy, Pulmonary, and Critical Care Division, University of Wisconsin-Madison, Madison, WI
| | - Matthew Bearce
- MInDSET Service Line, Kansas City Veterans Affairs Hospital, Kansas City, MO
| | - Richard S. Schaefer
- MInDSET Service Line, Kansas City Veterans Affairs Hospital, Kansas City, MO
| | - Andrea Uhl
- MInDSET Service Line, Kansas City Veterans Affairs Hospital, Kansas City, MO
| | - Gil Alterovitz
- Harvard Medical School, Boston, MA
- Office of Research and Development, Department of Veterans Affairs, Washington, DC
| | - Michael J. Kim
- Office of Research and Development, Department of Veterans Affairs, Washington, DC
| | - Kyle A. Carey
- Section of General Internal Medicine, University of Chicago, Chicago, IL
| | - Nirav S. Shah
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL
| | | | - Emily Gilbert
- Department of Medicine, Loyola University Medical Center, Maywood, IL
| | - Anne Stey
- Department of Surgery, Northwestern University School of Medicine, Chicago, IL
| | - Alan M. Weiss
- Section of Critical Care, Baycare Health System, Clearwater, FL
| | - Devendra Amin
- Section of Critical Care, Baycare Health System, Clearwater, FL
| | - George Karway
- Division of Allergy, Pulmonary, and Critical Care Division, University of Wisconsin-Madison, Madison, WI
| | - Jennie Martin
- Division of Allergy, Pulmonary, and Critical Care Division, University of Wisconsin-Madison, Madison, WI
| | - Dana P. Edelson
- Section of Hospital Medicine, University of Chicago, Chicago, IL
| | - Matthew M. Churpek
- Division of Allergy, Pulmonary, and Critical Care Division, University of Wisconsin-Madison, Madison, WI
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI
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Sun Y, Salerno S, Pan Z, Yang E, Sujimongkol C, Song J, Wang X, Han P, Zeng D, Kang J, Christiani DC, Li Y. Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned. HARVARD DATA SCIENCE REVIEW 2024; 6:10.1162/99608f92.9d86a749. [PMID: 38974963 PMCID: PMC11225107 DOI: 10.1162/99608f92.9d86a749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024] Open
Abstract
Severe cases of COVID-19 often necessitate escalation to the Intensive Care Unit (ICU), where patients may face grave outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinical information and chest X-ray images for predicting patient outcomes. We propose an analytical workflow to address challenges such as the absence of standardized approaches for image pre-processing and data utilization. We then propose an ensemble learning approach designed to maximize the information derived from multiple prediction algorithms. This entails optimizing the weights within the ensemble and considering the common variability present in individual risk scores. Our simulations demonstrate the superior performance of this weighted ensemble averaging approach across various scenarios. We apply this refined ensemble methodology to analyze post-ICU COVID-19 mortality, an occurrence observed in 21% of COVID-19 patients admitted to the ICU at Michigan Medicine. Our findings reveal substantial performance improvement when incorporating imaging data compared to models trained solely on clinical risk factors. Furthermore, the addition of radiomic features yields even larger enhancements, particularly among older and more medically compromised patients. These results may carry implications for enhancing patient outcomes in similar clinical contexts.
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Affiliation(s)
- Yuming Sun
- Biostatistics, University of Michigan, Ann Arbor, MI
| | | | - Ziyang Pan
- Biostatistics, University of Michigan, Ann Arbor, MI
| | - Eileen Yang
- Biostatistics, University of Michigan, Ann Arbor, MI
| | | | - Jiyeon Song
- Biostatistics, University of Michigan, Ann Arbor, MI
| | - Xinan Wang
- Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA
| | - Peisong Han
- Biostatistics, University of Michigan, Ann Arbor, MI
| | - Donglin Zeng
- Biostatistics, University of Michigan, Ann Arbor, MI
| | - Jian Kang
- Biostatistics, University of Michigan, Ann Arbor, MI
| | - David C. Christiani
- Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA
| | - Yi Li
- Biostatistics, University of Michigan, Ann Arbor, MI
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Costa Mello VL, Americano do Basil PEA. Fully independent validation of eleven prognostic scores predicting progression to critically ill condition in hospitalized patients with COVID-19. Braz J Infect Dis 2024; 28:103721. [PMID: 38331391 PMCID: PMC10861835 DOI: 10.1016/j.bjid.2024.103721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/27/2023] [Accepted: 01/24/2024] [Indexed: 02/10/2024] Open
Abstract
INTRODUCTION COVID-19 remains an important threat to global health and maintains the challenge of COVID-19 hospital care. To assist decision making regarding COVID-19 hospital care many instruments to predict COVID-19 progression to critical condition were developed and validated. OBJECTIVE To validate eleven COVID-19 progression prediction scores for critically ill hospitalized patients in a Brazilian population. METHODOLOGY Observational study with retrospective follow-up, including 301 adults confirmed for COVID-19 sequentially. Participants were admitted to non-critical units for treatment of the disease, between January and April 2021 and between September 2021 and February 2022. Eleven prognostic scores were applied using demographic, clinical, laboratory and imaging data collected in the first 48 of the hospital admission. The outcomes of greatest interest were as originally defined for each score. The analysis plan was to apply the instruments, estimate the outcome probability reproducing the original development/validation of each score, then to estimate performance measures (discrimination and calibration) and decision thresholds for risk classification. RESULTS The overall outcome prevalence was 41.8 % on 301 participants. There was a greater risk of the occurrence of the outcomes in older and male patients, and a linear trend with increasing comorbidities. Most of the patients studied were not immunized against COVID-19. Presence of concomitant bacterial infection and consolidation on imaging increased the risk of outcomes. College of London COVID-19 severity score and the 4C Mortality Score were the only with reasonable discrimination (ROC AUC 0.647 and 0.798 respectively) and calibration. The risk groups (low, intermediate and high) for 4C score were updated with the following thresholds: 0.239 and 0.318 (https://pedrobrasil.shinyapps.io/INDWELL/). CONCLUSION The 4C score showed the best discrimination and calibration performance among the tested instruments. We suggest different limits for risk groups. 4C score use could improve decision making and early therapeutic management at hospital care.
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Affiliation(s)
- Vinicius Lins Costa Mello
- Instituto Nacional de Infectologia Evandro Chagas - Fundação Oswaldo Cruz, Rio de Janeiro, RJ, Brazil
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4
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Kang JY, Bae YS, Chie EK, Lee SB. Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19. SENSORS (BASEL, SWITZERLAND) 2023; 23:9597. [PMID: 38067970 PMCID: PMC10708735 DOI: 10.3390/s23239597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023]
Abstract
Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.
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Affiliation(s)
- Jin-Yeong Kang
- Department of Medical Informatics, Keimyung University, Daegu 42601, Republic of Korea;
- Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea
| | - Ye Seul Bae
- Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea;
- Department of Future Healthcare Planning, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University, Daegu 42601, Republic of Korea;
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5
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Cárdenas-Fuentes G, Bosch de Basea M, Cobo I, Subirana I, Ceresa M, Famada E, Gimeno-Santos E, Delgado-Ortiz L, Faner R, Molina-Molina M, Agustí À, Muñoz X, Sibila O, Gea J, Garcia-Aymerich J. Validity of prognostic models of critical COVID-19 is variable. A systematic review with external validation. J Clin Epidemiol 2023; 159:274-288. [PMID: 37142168 PMCID: PMC10152752 DOI: 10.1016/j.jclinepi.2023.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 01/26/2023] [Accepted: 04/25/2023] [Indexed: 05/06/2023]
Abstract
OBJECTIVES To identify prognostic models which estimate the risk of critical COVID-19 in hospitalized patients and to assess their validation properties. STUDY DESIGN AND SETTING We conducted a systematic review in Medline (up to January 2021) of studies developing or updating a model that estimated the risk of critical COVID-19, defined as death, admission to intensive care unit, and/or use of mechanical ventilation during admission. Models were validated in two datasets with different backgrounds (HM [private Spanish hospital network], n = 1,753, and ICS [public Catalan health system], n = 1,104), by assessing discrimination (area under the curve [AUC]) and calibration (plots). RESULTS We validated 18 prognostic models. Discrimination was good in nine of them (AUCs ≥ 80%) and higher in those predicting mortality (AUCs 65%-87%) than those predicting intensive care unit admission or a composite outcome (AUCs 53%-78%). Calibration was poor in all models providing outcome's probabilities and good in four models providing a point-based score. These four models used mortality as outcome and included age, oxygen saturation, and C-reactive protein among their predictors. CONCLUSION The validity of models predicting critical COVID-19 by using only routinely collected predictors is variable. Four models showed good discrimination and calibration when externally validated and are recommended for their use.
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Affiliation(s)
- Gabriela Cárdenas-Fuentes
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; School of Health Sciences, Blanquerna-Universitat Ramon Llull, Barcelona, Spain.
| | - Magda Bosch de Basea
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Inés Cobo
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Isaac Subirana
- Instituto Hospital del Mar de Investigaciones Médicas (IMIM), Barcelona, Spain; CIBER Enfermedades Cardiovasculares (CIBERCV), ISCIII, Spain
| | - Mario Ceresa
- BCNMedTech, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | | | - Elena Gimeno-Santos
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain; Respiratory Institute, Hospital Clinic, Barcelona, Spain
| | - Laura Delgado-Ortiz
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Rosa Faner
- Instituto de Investigaciones Biomédicas August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Universitat de Barcelona, Barcelona, Spain; CIBER Enfermedades Respiratorias (CIBERES), ISCIII, Spain
| | - María Molina-Molina
- CIBER Enfermedades Respiratorias (CIBERES), ISCIII, Spain; Servicio de Neumología, Hospital Universitario de Bellvitge, L'Hospitalet de Llobregat, Spain; Instituto de Investigación Biomédica de Bellvitge (IDIBELL), L'Hospitalet de Llobregat, Spain
| | - Àlvar Agustí
- Respiratory Institute, Hospital Clinic, Barcelona, Spain; Instituto de Investigaciones Biomédicas August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Universitat de Barcelona, Barcelona, Spain; CIBER Enfermedades Respiratorias (CIBERES), ISCIII, Spain
| | - Xavier Muñoz
- CIBER Enfermedades Respiratorias (CIBERES), ISCIII, Spain; Servicio de Neumología, Hospital Universitario Vall d'Hebron, Barcelona, Spain; Departamento de Biología celular, fisiología e inmunología, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Oriol Sibila
- Respiratory Institute, Hospital Clinic, Barcelona, Spain; Instituto de Investigaciones Biomédicas August Pi i Sunyer (IDIBAPS), Barcelona, Spain; CIBER Enfermedades Respiratorias (CIBERES), ISCIII, Spain
| | - Joaquim Gea
- Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Enfermedades Respiratorias (CIBERES), ISCIII, Spain; Servicio de Neumología, Hospital del Mar-IMIM, Barcelona, Spain; Fundació Barcelona Respiratory Network (BRN), Barcelona, Spain
| | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
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6
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Alablani IAL, Alenazi MJF. COVID-ConvNet: A Convolutional Neural Network Classifier for Diagnosing COVID-19 Infection. Diagnostics (Basel) 2023; 13:diagnostics13101675. [PMID: 37238159 DOI: 10.3390/diagnostics13101675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 04/25/2023] [Accepted: 04/28/2023] [Indexed: 05/28/2023] Open
Abstract
The novel coronavirus (COVID-19) pandemic still has a significant impact on the worldwide population's health and well-being. Effective patient screening, including radiological examination employing chest radiography as one of the main screening modalities, is an important step in the battle against the disease. Indeed, the earliest studies on COVID-19 found that patients infected with COVID-19 present with characteristic anomalies in chest radiography. In this paper, we introduce COVID-ConvNet, a deep convolutional neural network (DCNN) design suitable for detecting COVID-19 symptoms from chest X-ray (CXR) scans. The proposed deep learning (DL) model was trained and evaluated using 21,165 CXR images from the COVID-19 Database, a publicly available dataset. The experimental results demonstrate that our COVID-ConvNet model has a high prediction accuracy at 97.43% and outperforms recent related works by up to 5.9% in terms of prediction accuracy.
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Affiliation(s)
- Ibtihal A L Alablani
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh P.O. Box 11451, Saudi Arabia
| | - Mohammed J F Alenazi
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh P.O. Box 11451, Saudi Arabia
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7
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Rahman T, Chowdhury MEH, Khandakar A, Mahbub ZB, Hossain MSA, Alhatou A, Abdalla E, Muthiyal S, Islam KF, Kashem SBA, Khan MS, Zughaier SM, Hossain M. BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data. Neural Comput Appl 2023; 35:1-23. [PMID: 37362565 PMCID: PMC10157130 DOI: 10.1007/s00521-023-08606-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 04/11/2023] [Indexed: 06/28/2023]
Abstract
Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March-June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-023-08606-w.
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Affiliation(s)
- Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | | | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Zaid Bin Mahbub
- Department of Physics and Mathematics, North South University, Dhaka, 1229 Bangladesh
| | | | - Abraham Alhatou
- Department of Biology, University of South Carolina (USC), Columbia, SC 29208 USA
| | - Eynas Abdalla
- Anesthesia Department, Hamad General Hospital, P.O. Box 3050, Doha, Qatar
| | - Sreekumar Muthiyal
- Department of Radiology, Hamad General Hospital, P.O. Box 3050, Doha, Qatar
| | | | - Saad Bin Abul Kashem
- Department of Computer Science, AFG College with the University of Aberdeen, Doha, Qatar
| | - Muhammad Salman Khan
- Department of Electrical Engineering, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Susu M. Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, P.O. Box 2713, Doha, Qatar
| | - Maqsud Hossain
- NSU Genome Research Institute (NGRI), North South University, Dhaka, 1229 Bangladesh
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8
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Buttia C, Llanaj E, Raeisi-Dehkordi H, Kastrati L, Amiri M, Meçani R, Taneri PE, Ochoa SAG, Raguindin PF, Wehrli F, Khatami F, Espínola OP, Rojas LZ, de Mortanges AP, Macharia-Nimietz EF, Alijla F, Minder B, Leichtle AB, Lüthi N, Ehrhard S, Que YA, Fernandes LK, Hautz W, Muka T. Prognostic models in COVID-19 infection that predict severity: a systematic review. Eur J Epidemiol 2023; 38:355-372. [PMID: 36840867 PMCID: PMC9958330 DOI: 10.1007/s10654-023-00973-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 01/28/2023] [Indexed: 02/26/2023]
Abstract
Current evidence on COVID-19 prognostic models is inconsistent and clinical applicability remains controversial. We performed a systematic review to summarize and critically appraise the available studies that have developed, assessed and/or validated prognostic models of COVID-19 predicting health outcomes. We searched six bibliographic databases to identify published articles that investigated univariable and multivariable prognostic models predicting adverse outcomes in adult COVID-19 patients, including intensive care unit (ICU) admission, intubation, high-flow nasal therapy (HFNT), extracorporeal membrane oxygenation (ECMO) and mortality. We identified and assessed 314 eligible articles from more than 40 countries, with 152 of these studies presenting mortality, 66 progression to severe or critical illness, 35 mortality and ICU admission combined, 17 ICU admission only, while the remaining 44 studies reported prediction models for mechanical ventilation (MV) or a combination of multiple outcomes. The sample size of included studies varied from 11 to 7,704,171 participants, with a mean age ranging from 18 to 93 years. There were 353 prognostic models investigated, with area under the curve (AUC) ranging from 0.44 to 0.99. A great proportion of studies (61.5%, 193 out of 314) performed internal or external validation or replication. In 312 (99.4%) studies, prognostic models were reported to be at high risk of bias due to uncertainties and challenges surrounding methodological rigor, sampling, handling of missing data, failure to deal with overfitting and heterogeneous definitions of COVID-19 and severity outcomes. While several clinical prognostic models for COVID-19 have been described in the literature, they are limited in generalizability and/or applicability due to deficiencies in addressing fundamental statistical and methodological concerns. Future large, multi-centric and well-designed prognostic prospective studies are needed to clarify remaining uncertainties.
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Affiliation(s)
- Chepkoech Buttia
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
- Epistudia, Bern, Switzerland
| | - Erand Llanaj
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal, Germany
- ELKH-DE Public Health Research Group of the Hungarian Academy of Sciences, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- Epistudia, Bern, Switzerland
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Hamidreza Raeisi-Dehkordi
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lum Kastrati
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mojgan Amiri
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Renald Meçani
- Department of Pediatrics, “Mother Teresa” University Hospital Center, Tirana, University of Medicine, Tirana, Albania
- Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Petek Eylul Taneri
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- HRB-Trials Methodology Research Network College of Medicine, Nursing and Health Sciences University of Galway, Galway, Ireland
| | | | - Peter Francis Raguindin
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Swiss Paraplegic Research, Nottwil, Switzerland
- Faculty of Health Sciences, University of Lucerne, Lucerne, Switzerland
| | - Faina Wehrli
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
| | - Farnaz Khatami
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
- Department of Community Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Octavio Pano Espínola
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Department of Preventive Medicine and Public Health, University of Navarre, Pamplona, Spain
- Navarra Institute for Health Research, IdiSNA, Pamplona, Spain
| | - Lyda Z. Rojas
- Research Group and Development of Nursing Knowledge (GIDCEN-FCV), Research Center, Cardiovascular Foundation of Colombia, Floridablanca, Santander, Colombia
| | | | | | - Fadi Alijla
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Graduate School for Health Sciences, University of Bern, Bern, Switzerland
| | - Beatrice Minder
- Public Health and Primary Care Library, University Library of Bern, University of Bern, Bern, Switzerland
| | - Alexander B. Leichtle
- University Institute of Clinical Chemistry, Inselspital, Bern University Hospital, and Center for Artificial Intelligence in Medicine (CAIM), University of Bern, Bern, Switzerland
| | - Nora Lüthi
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Simone Ehrhard
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Yok-Ai Que
- Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Laurenz Kopp Fernandes
- Deutsches Herzzentrum Berlin (DHZB), Berlin, Germany
- Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Wolf Hautz
- Emergency Department, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, 3010 Bern, Switzerland
| | - Taulant Muka
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland
- Epistudia, Bern, Switzerland
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Yagin FH, Cicek İB, Alkhateeb A, Yagin B, Colak C, Azzeh M, Akbulut S. Explainable artificial intelligence model for identifying COVID-19 gene biomarkers. Comput Biol Med 2023; 154:106619. [PMID: 36738712 PMCID: PMC9889119 DOI: 10.1016/j.compbiomed.2023.106619] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/11/2023] [Accepted: 01/28/2023] [Indexed: 02/04/2023]
Abstract
AIM COVID-19 has revealed the need for fast and reliable methods to assist clinicians in diagnosing the disease. This article presents a model that applies explainable artificial intelligence (XAI) methods based on machine learning techniques on COVID-19 metagenomic next-generation sequencing (mNGS) samples. METHODS In the data set used in the study, there are 15,979 gene expressions of 234 patients with COVID-19 negative 141 (60.3%) and COVID-19 positive 93 (39.7%). The least absolute shrinkage and selection operator (LASSO) method was applied to select genes associated with COVID-19. Support Vector Machine - Synthetic Minority Oversampling Technique (SVM-SMOTE) method was used to handle the class imbalance problem. Logistics regression (LR), SVM, random forest (RF), and extreme gradient boosting (XGBoost) methods were constructed to predict COVID-19. An explainable approach based on local interpretable model-agnostic explanations (LIME) and SHAPley Additive exPlanations (SHAP) methods was applied to determine COVID-19- associated biomarker candidate genes and improve the final model's interpretability. RESULTS For the diagnosis of COVID-19, the XGBoost (accuracy: 0.930) model outperformed the RF (accuracy: 0.912), SVM (accuracy: 0.877), and LR (accuracy: 0.912) models. As a result of the SHAP, the three most important genes associated with COVID-19 were IFI27, LGR6, and FAM83A. The results of LIME showed that especially the high level of IFI27 gene expression contributed to increasing the probability of positive class. CONCLUSIONS The proposed model (XGBoost) was able to predict COVID-19 successfully. The results show that machine learning combined with LIME and SHAP can explain the biomarker prediction for COVID-19 and provide clinicians with an intuitive understanding and interpretability of the impact of risk factors in the model.
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Affiliation(s)
- Fatma Hilal Yagin
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey.
| | - İpek Balikci Cicek
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey.
| | - Abedalrhman Alkhateeb
- Software Engineering Department, King Hussein School for Computing Sciences, Amman, Jordan.
| | - Burak Yagin
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey.
| | - Cemil Colak
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey.
| | - Mohammad Azzeh
- Data Science Department, King Hussein School for Computing Sciences, Amman, Jordan.
| | - Sami Akbulut
- Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, 44280, Malatya, Turkey; Inonu University, Faculty of Medicine, Department of Surgery, 44280, Malatya, Turkey; Inonu University, Faculty of Medicine, Department of Public Health, 44280, Malatya, Turkey.
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Cavallazzi R, Bradley J, Chandler T, Furmanek S, Ramirez JA. Severity of Illness Scores and Biomarkers for Prognosis of Patients with Coronavirus Disease 2019. Semin Respir Crit Care Med 2023; 44:75-90. [PMID: 36646087 DOI: 10.1055/s-0042-1759567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The spectrum of disease severity and the insidiousness of clinical presentation make it difficult to recognize patients with coronavirus disease 2019 (COVID-19) at higher risk of worse outcomes or death when they are seen in the early phases of the disease. There are now well-established risk factors for worse outcomes in patients with COVID-19. These should be factored in when assessing the prognosis of these patients. However, a more precise prognostic assessment in an individual patient may warrant the use of predictive tools. In this manuscript, we conduct a literature review on the severity of illness scores and biomarkers for the prognosis of patients with COVID-19. Several COVID-19-specific scores have been developed since the onset of the pandemic. Some of them are promising and can be integrated into the assessment of these patients. We also found that the well-known pneumonia severity index (PSI) and CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years) are good predictors of mortality in hospitalized patients with COVID-19. While neither the PSI nor the CURB-65 should be used for the triage of outpatient versus inpatient treatment, they can be integrated by a clinician into the assessment of disease severity and can be used in epidemiological studies to determine the severity of illness in patient populations. Biomarkers also provide valuable prognostic information and, importantly, may depict the main physiological derangements in severe disease. We, however, do not advocate the isolated use of severity of illness scores or biomarkers for decision-making in an individual patient. Instead, we suggest the use of these tools on a case-by-case basis with the goal of enhancing clinician judgment.
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Affiliation(s)
- Rodrigo Cavallazzi
- Division of Pulmonary, Critical Care Medicine, and Sleep Disorders, University of Louisville, Norton Healthcare, Louisville, Kentucky
| | - James Bradley
- Division of Pulmonary, Critical Care Medicine, and Sleep Disorders, University of Louisville, Norton Healthcare, Louisville, Kentucky
| | - Thomas Chandler
- Norton Infectious Diseases Institute, Norton Healthcare, Louisville, Kentucky
| | - Stephen Furmanek
- Norton Infectious Diseases Institute, Norton Healthcare, Louisville, Kentucky
| | - Julio A Ramirez
- Norton Infectious Diseases Institute, Norton Healthcare, Louisville, Kentucky
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11
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Al-Shami I, Hourani HM, Alkhatib B. The use of prognostic nutritional index (PNI) and selected inflammatory indicators for predicting malnutrition in COVID-19 patients: A retrospective study. J Infect Public Health 2023; 16:280-285. [PMID: 36623422 PMCID: PMC9800019 DOI: 10.1016/j.jiph.2022.12.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Malnutrition causes diverse alterations in the immune system, and COVID-19 is an infection affecting the immune system, consequently leading to malnutrition. AIMS This study aimed to investigate the use of prognostic nutritional index (PNI) and selected inflammatory indices for malnutrition screening among COVID-19 hospitalized patients. MATERIAL AND METHODOLOGY This is a single-center retrospective study that enrolled 289 hospitalized COVID-19 patients between 1st January to 30th April 2021, their median age was 59 years. Demographic and biochemical data were collected from patients' records. The PNI, platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and an early warning score to predict mortality risk (ANDC) were calculated. Univariate and multivariate logistic regression analyses were performed. A P-value of < 0.05 was considered statistically significant. RESULTS about 30 % of patients were admitted to the intensive care unit (ICU), and ICU patients had significantly higher levels of white blood cell (WBCs) count, neutrophils, C-reactive protein (C-RP), and D-dimer (P < 0.05). On the other hand, they had significantly lower levels of lymphocytes and serum albumin (P < 0.001; for both). Those with high ANDC scores were more likely to develop severe conditions affecting nutritional status compared to non-ICU (OR = 1.04, 95 % CI:1.014-1.057; P < 0.001). ANDC showed good discrimination ability with an AUC of 0.784 (cut-off value > 68.19 score). CONCLUSION It is suggested that ANDC could be used as a predictor for nutritional status and severity in COVID-19 hospitalized patients.
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12
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Moslehi S, Mahjub H, Farhadian M, Soltanian AR, Mamani M. Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran. BMC Med Res Methodol 2022; 22:339. [PMID: 36585627 PMCID: PMC9803600 DOI: 10.1186/s12874-022-01827-y] [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: 02/21/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learning method to predict the COVID-19 mortality of patients. METHODS This cohort study included 2181 COVID-19 patients admitted from February 2020 to July 2021 in Sina and Besat hospitals in Hamadan, west of Iran. A total of 22 baseline features including patients' demographic information and clinical biomarkers were collected. Four strategies including removing missing values, mean, K-Nearest Neighbor (KNN), and Multivariate Imputation by Chained Equations (MICE) imputation methods were used to deal with missing data. Firstly, the important features for predicting binary outcome (1: death, 0: recovery) were selected using the Random Forest (RF) method. Also, synthetic minority over-sampling technique (SMOTE) method was used for handling imbalanced data. Next, considering the selected features, the predictive performance of GNAM for predicting mortality outcome was compared with logistic regression, RF, generalized additive model (GAMs), gradient boosting decision tree (GBDT), and deep neural networks (DNNs) classification models. Each model trained on fifty different subsets of a train-test dataset to ensure a model performance. The average accuracy, F1-score and area under the curve (AUC) evaluation indices were used for comparison of the predictive performance of the models. RESULTS Out of the 2181 COVID-19 patients, 624 died during hospitalization and 1557 recovered. The missing rate was 3 percent for each patient. The mean age of dead patients (71.17 ± 14.44 years) was statistically significant higher than recovered patients (58.25 ± 16.52 years). Based on RF, 10 features with the highest relative importance were selected as the best influential features; including blood urea nitrogen (BUN), lymphocytes (Lym), age, blood sugar (BS), serum glutamic-oxaloacetic transaminase (SGOT), monocytes (Mono), blood creatinine (CR), neutrophils (NUT), alkaline phosphatase (ALP) and hematocrit (HCT). The results of predictive performance comparisons showed GNAM with the mean accuracy, F1-score, and mean AUC in the test dataset of 0.847, 0.691, and 0.774, respectively, had the best performance. The smooth function graphs learned from the GNAM were descending for the Lym and ascending for the other important features. CONCLUSIONS Interpretable GNAM can perform well in predicting the mortality of COVID-19 patients. Therefore, the use of such a reliable model can help physicians to prioritize some important demographic and clinical biomarkers by identifying the effective features and the type of predictive trend in disease progression.
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Affiliation(s)
- Samad Moslehi
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Hossein Mahjub
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health, Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Maryam Farhadian
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health, Research Center for Health Sciences, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Ali Reza Soltanian
- grid.411950.80000 0004 0611 9280Department of Biostatistics, School of Public Health, Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Mojgan Mamani
- grid.411950.80000 0004 0611 9280Brucellosis Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
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Maestre-Muñiz MM, Arias Á, Lucendo AJ. Predicting In-Hospital Mortality in Severe COVID-19: A Systematic Review and External Validation of Clinical Prediction Rules. Biomedicines 2022; 10:biomedicines10102414. [PMID: 36289676 PMCID: PMC9599062 DOI: 10.3390/biomedicines10102414] [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] [Received: 07/17/2022] [Revised: 09/02/2022] [Accepted: 09/05/2022] [Indexed: 12/03/2022] Open
Abstract
Multiple prediction models for risk of in-hospital mortality from COVID-19 have been developed, but not applied, to patient cohorts different to those from which they were derived. The MEDLINE, EMBASE, Scopus, and Web of Science (WOS) databases were searched. Risk of bias and applicability were assessed with PROBAST. Nomograms, whose variables were available in a well-defined cohort of 444 patients from our site, were externally validated. Overall, 71 studies, which derived a clinical prediction rule for mortality outcome from COVID-19, were identified. Predictive variables consisted of combinations of patients′ age, chronic conditions, dyspnea/taquipnea, radiographic chest alteration, and analytical values (LDH, CRP, lymphocytes, D-dimer); and markers of respiratory, renal, liver, and myocardial damage, which were mayor predictors in several nomograms. Twenty-five models could be externally validated. Areas under receiver operator curve (AUROC) in predicting mortality ranged from 0.71 to 1 in derivation cohorts; C-index values ranged from 0.823 to 0.970. Overall, 37/71 models provided very-good-to-outstanding test performance. Externally validated nomograms provided lower predictive performances for mortality in their respective derivation cohorts, with the AUROC being 0.654 to 0.806 (poor to acceptable performance). We can conclude that available nomograms were limited in predicting mortality when applied to different populations from which they were derived.
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Affiliation(s)
- Modesto M. Maestre-Muñiz
- Department of Internal Medicine, Hospital General de Tomelloso, 13700 Ciudad Real, Spain
- Department of Medicine and Medical Specialties, Universidad de Alcalá, 28801 Alcalá de Henares, Spain
| | - Ángel Arias
- Hospital General La Mancha Centro, Research Unit, Alcázar de San Juan, 13600 Ciudad Real, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28006 Madrid, Spain
- Instituto de Investigación Sanitaria La Princesa, 28006 Madrid, Spain
- Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 13700 Tomelloso, Spain
| | - Alfredo J. Lucendo
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28006 Madrid, Spain
- Instituto de Investigación Sanitaria La Princesa, 28006 Madrid, Spain
- Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 13700 Tomelloso, Spain
- Department of Gastroenterology, Hospital General de Tomelloso, 13700 Ciudad Real, Spain
- Correspondence: ; Tel.: +34-926-525-927
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Islam KR, Kumar J, Tan TL, Reaz MBI, Rahman T, Khandakar A, Abbas T, Hossain MSA, Zughaier SM, Chowdhury MEH. Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning. Diagnostics (Basel) 2022; 12:diagnostics12092144. [PMID: 36140545 PMCID: PMC9498213 DOI: 10.3390/diagnostics12092144] [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] [Received: 08/05/2022] [Revised: 08/22/2022] [Accepted: 08/26/2022] [Indexed: 11/18/2022] Open
Abstract
With the onset of the COVID-19 pandemic, the number of critically sick patients in intensive care units (ICUs) has increased worldwide, putting a burden on ICUs. Early prediction of ICU requirement is crucial for efficient resource management and distribution. Early-prediction scoring systems for critically ill patients using mathematical models are available, but are not generalized for COVID-19 and Non-COVID patients. This study aims to develop a generalized and reliable prognostic model for ICU admission for both COVID-19 and non-COVID-19 patients using best feature combination from the patient data at admission. A retrospective cohort study was conducted on a dataset collected from the pulmonology department of Moscow City State Hospital between 20 April 2020 and 5 June 2020. The dataset contains ten clinical features for 231 patients, of whom 100 patients were transferred to ICU and 131 were stable (non-ICU) patients. There were 156 COVID positive patients and 75 non-COVID patients. Different feature selection techniques were investigated, and a stacking machine learning model was proposed and compared with eight different classification algorithms to detect risk of need for ICU admission for both COVID-19 and non-COVID patients combined and COVID patients alone. C-reactive protein (CRP), chest computed tomography (CT), lung tissue affected (%), age, admission to hospital, and fibrinogen parameters at hospital admission were found to be important features for ICU-requirement risk prediction. The best performance was produced by the stacking approach, with weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 84.45%, 84.48%, 83.64%, 84.47%, and 84.48%, respectively, for both types of patients, and 85.34%, 85.35%, 85.11%, 85.34%, and 85.35%, respectively, for COVID-19 patients only. The proposed work can help doctors to improve management through early prediction of the risk of need for ICU admission of patients during the COVID-19 pandemic, as the model can be used for both types of patients.
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Affiliation(s)
- Khandaker Reajul Islam
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Jaya Kumar
- Department of Physiology, Faculty of Medicine, University Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Correspondence: (J.K.); (M.E.H.C.)
| | - Toh Leong Tan
- Department of Emergency Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar
| | - Tariq Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha P.O. Box 26999, Qatar
| | | | - Susu M. Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha P.O. Box 2713, Qatar
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar
- Correspondence: (J.K.); (M.E.H.C.)
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O Balin S, Kazanci U, Demirdag K, Akbulut A. What is the role of prognostic indexes in COVID-19 patients with diabetes mellitus? Data of patients from Turkey. Biomark Med 2022; 16:971-979. [PMID: 36006030 DOI: 10.2217/bmm-2022-0250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Aim: We aimed to determine the prognostic performance of the Glasgow Prognostic Score (GPS), systemic immune-inflammation index and early warning score (the 'ANDC' system) in patients with diabetes mellitus who had COVID-19. Patients & methods: Patients were divided into two groups: with and without diabetes mellitus. Results: In the diabetic patient group, the rates of in-hospital mortality, intensive care unit hospitalization and corticosteroid treatment were higher compared with the nondiabetic patient group (p < 0.05). A GPS of 2 was useful for predicting in-hospital mortality in diabetic patients (p < 0.05). The ANDC score was significantly higher in diabetic patients (p < 0.05) and in diabetic patients with mortality and those who needed ICU hospitalization (p < 0.05). Conclusion: The presence of a GPS of 2 at the time of admission and a high ANDC value were associated with poor prognosis in diabetic COVID-19 patients.
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Affiliation(s)
- Safak O Balin
- Department of Infectious Diseases & Clinical Microbiology, Faculty of Medicine, Firat University, Elazig, Turkey
| | - Ulku Kazanci
- Department of Pathology, Necip Fazil City Hospital, Kahramanmaras, Turkey
| | - Kutbeddin Demirdag
- Department of Infectious Diseases & Clinical Microbiology, Faculty of Medicine, Firat University, Elazig, Turkey
| | - Ayhan Akbulut
- Department of Infectious Diseases & Clinical Microbiology, Faculty of Medicine, Firat University, Elazig, Turkey
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Tong-Minh K, van der Does Y, van Rosmalen J, Ramakers C, Gommers D, van Gorp E, Rizopoulos D, Endeman H. Joint Modeling of Repeated Measurements of Different Biomarkers Predicts Mortality in COVID-19 Patients in the Intensive Care Unit. Biomark Insights 2022; 17:11772719221112370. [PMID: 35859926 PMCID: PMC9290097 DOI: 10.1177/11772719221112370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 06/21/2022] [Indexed: 01/28/2023] Open
Abstract
Introduction: Predicting disease severity is important for treatment decisions in patients with COVID-19 in the intensive care unit (ICU). Different biomarkers have been investigated in COVID-19 as predictor of mortality, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and soluble urokinase-type plasminogen activator receptor (suPAR). Using repeated measurements in a prediction model may result in a more accurate risk prediction than the use of single point measurements. The goal of this study is to investigate the predictive value of trends in repeated measurements of CRP, PCT, IL-6, and suPAR on mortality in patients admitted to the ICU with COVID-19. Methods: This was a retrospective single center cohort study. Patients were included if they tested positive for SARS-CoV-2 by PCR test and if IL-6, PCT, suPAR was measured during any of the ICU admission days. There were no exclusion criteria for this study. We used joint models to predict ICU-mortality. This analysis was done using the framework of joint models for longitudinal and survival data. The reported hazard ratios express the relative change in the risk of death resulting from a doubling or 20% increase of the biomarker’s value in a day compared to no change in the same period. Results: A total of 107 patients were included, of which 26 died during ICU admission. Adjusted for sex and age, a doubling in the next day in either levels of PCT, IL-6, and suPAR were significantly predictive of in-hospital mortality with HRs of 1.523 (1.012-6.540), 75.25 (1.116-6247), and 24.45 (1.696-1057) respectively. With a 20% increase in biomarker value in a subsequent day, the HR of PCT, IL-6, and suPAR were 1.117 (1.03-1.639), 3.116 (1.029-9.963), and 2.319 (1.149-6.243) respectively. Conclusion: Joint models for the analysis of repeated measurements of PCT, suPAR, and IL-6 are a useful method for predicting mortality in COVID-19 patients in the ICU. Patients with an increasing trend of biomarker levels in consecutive days are at increased risk for mortality.
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Affiliation(s)
- Kirby Tong-Minh
- Department of Emergency Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Yuri van der Does
- Department of Emergency Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Joost van Rosmalen
- Department of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Christian Ramakers
- Department of Clinical Chemistry, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Diederik Gommers
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Eric van Gorp
- Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Viroscience, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Dimitris Rizopoulos
- Department of Biostatistics, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Henrik Endeman
- Department of Intensive Care, Erasmus University Medical Center, Rotterdam, The Netherlands
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Raschke RA, Rangan P, Agarwal S, Uppalapu S, Sher N, Curry SC, Heise CW. COVID-19 Time of Intubation Mortality Evaluation (C-TIME): A system for predicting mortality of patients with COVID-19 pneumonia at the time they require mechanical ventilation. PLoS One 2022; 17:e0270193. [PMID: 35793312 PMCID: PMC9258832 DOI: 10.1371/journal.pone.0270193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 06/06/2022] [Indexed: 11/18/2022] Open
Abstract
Background An accurate system to predict mortality in patients requiring intubation for COVID-19 could help to inform consent, frame family expectations and assist end-of-life decisions. Research objective To develop and validate a mortality prediction system called C-TIME (COVID-19 Time of Intubation Mortality Evaluation) using variables available before intubation, determine its discriminant accuracy, and compare it to acute physiology and chronic health evaluation (APACHE IVa) and sequential organ failure assessment (SOFA). Methods A retrospective cohort was set in 18 medical-surgical ICUs, enrolling consecutive adults, positive by SARS-CoV 2 RNA by reverse transcriptase polymerase chain reaction or positive rapid antigen test, and undergoing endotracheal intubation. All were followed until hospital discharge or death. The combined outcome was hospital mortality or terminal extubation with hospice discharge. Twenty-five clinical and laboratory variables available 48 hours prior to intubation were entered into multiple logistic regression (MLR) and the resulting model was used to predict mortality of validation cohort patients. Area under the receiver operating curve (AUROC) was calculated for C-TIME, APACHE IVa and SOFA. Results The median age of the 2,440 study patients was 66 years; 61.6 percent were men, and 50.5 percent were Hispanic, Native American or African American. Age, gender, COPD, minimum mean arterial pressure, Glasgow Coma scale score, and PaO2/FiO2 ratio, maximum creatinine and bilirubin, receiving factor Xa inhibitors, days receiving non-invasive respiratory support and days receiving corticosteroids prior to intubation were significantly associated with the outcome variable. The validation cohort comprised 1,179 patients. C-TIME had the highest AUROC of 0.75 (95%CI 0.72–0.79), vs 0.67 (0.64–0.71) and 0.59 (0.55–0.62) for APACHE and SOFA, respectively (Chi2 P<0.0001). Conclusions C-TIME is the only mortality prediction score specifically developed and validated for COVID-19 patients who require mechanical ventilation. It has acceptable discriminant accuracy and goodness-of-fit to assist decision-making just prior to intubation. The C-TIME mortality prediction calculator can be freely accessed on-line at https://phoenixmed.arizona.edu/ctime.
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Affiliation(s)
- Robert A. Raschke
- The Division of Clinical Data Analytics and Decision Support, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States of America
- University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States of America
- * E-mail:
| | - Pooja Rangan
- University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States of America
- Department of Internal Medicine, Banner—University Medical Center Phoenix, Phoenix, AZ, United States of America
| | - Sumit Agarwal
- University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States of America
- Department of Internal Medicine, Banner—University Medical Center Phoenix, Phoenix, AZ, United States of America
| | - Suresh Uppalapu
- University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States of America
- Department of Internal Medicine, Banner—University Medical Center Phoenix, Phoenix, AZ, United States of America
| | - Nehan Sher
- University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States of America
| | - Steven C. Curry
- The Division of Clinical Data Analytics and Decision Support, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States of America
- Department of Medical Toxicology, Banner–University Medical Center Phoenix, Phoenix, AZ, United States of America
| | - C. William Heise
- The Division of Clinical Data Analytics and Decision Support, University of Arizona College of Medicine-Phoenix, Phoenix, AZ, United States of America
- Department of Medical Toxicology, Banner–University Medical Center Phoenix, Phoenix, AZ, United States of America
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Rahman T, Khandakar A, Abir FF, Faisal MAA, Hossain MS, Podder KK, Abbas TO, Alam MF, Kashem SB, Islam MT, Zughaier SM, Chowdhury MEH. QCovSML: A reliable COVID-19 detection system using CBC biomarkers by a stacking machine learning model. Comput Biol Med 2022; 143:105284. [PMID: 35180500 PMCID: PMC8839805 DOI: 10.1016/j.compbiomed.2022.105284] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/26/2022] [Accepted: 02/02/2022] [Indexed: 12/31/2022]
Abstract
The reverse transcription-polymerase chain reaction (RT-PCR) test is considered the current gold standard for the detection of coronavirus disease (COVID-19), although it suffers from some shortcomings, namely comparatively longer turnaround time, higher false-negative rates around 20-25%, and higher cost equipment. Therefore, finding an efficient, robust, accurate, and widely available, and accessible alternative to RT-PCR for COVID-19 diagnosis is a matter of utmost importance. This study proposes a complete blood count (CBC) biomarkers-based COVID-19 detection system using a stacking machine learning (SML) model, which could be a fast and less expensive alternative. This study used seven different publicly available datasets, where the largest one consisting of fifteen CBC biomarkers collected from 1624 patients (52% COVID-19 positive) admitted at San Raphael Hospital, Italy from February to May 2020 was used to train and validate the proposed model. White blood cell count, monocytes (%), lymphocyte (%), and age parameters collected from the patients during hospital admission were found to be important biomarkers for COVID-19 disease prediction using five different feature selection techniques. Our stacking model produced the best performance with weighted precision, sensitivity, specificity, overall accuracy, and F1-score of 91.44%, 91.44%, 91.44%, 91.45%, and 91.45%, respectively. The stacking machine learning model improved the performance in comparison to other state-of-the-art machine learning classifiers. Finally, a nomogram-based scoring system (QCovSML) was constructed using this stacking approach to predict the COVID-19 patients. The cut-off value of the QCovSML system for classifying COVID-19 and Non-COVID patients was 4.8. Six datasets from three different countries were used to externally validate the proposed model to evaluate its generalizability and robustness. The nomogram demonstrated good calibration and discrimination with the area under the curve (AUC) of 0.961 for the internal cohort and average AUC of 0.967 for all external validation cohort, respectively. The external validation shows an average weighted precision, sensitivity, F1-score, specificity, and overall accuracy of 92.02%, 95.59%, 93.73%, 90.54%, and 93.34%, respectively.
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Affiliation(s)
- Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Farhan Fuad Abir
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md Ahasan Atick Faisal
- Department of Electrical and Electronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Md Shafayet Hossain
- Dept. of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
| | - Kanchon Kanti Podder
- Department of Biomedical Physics & Technology, University of Dhaka, Dhaka, 1000, Bangladesh
| | - Tariq O Abbas
- Urology Division, Surgery Department, Sidra Medicine, Doha, 26999, Qatar
| | - Mohammed Fasihul Alam
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, 2713, Qatar
| | - Saad Bin Kashem
- Department of Computing Science, AFG College with the University of Aberdeen, Doha, Qatar
| | - Mohammad Tariqul Islam
- Dept. of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
| | - Susu M Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha, 2713, Qatar
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19
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Clinical features and prognostic factors of adults with COVID-19 admitted to intensive care units in Colombia: A multicentre retrospective study during the first wave of the pandemic. ACTA COLOMBIANA DE CUIDADO INTENSIVO 2022. [PMCID: PMC7985962 DOI: 10.1016/j.acci.2021.02.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Background A significant number of COVID-19 patients require intensive care unit (ICU) admission. However, ICU mortality in COVID-19 patients varies considerably between studies. Objectives To determine the clinical features and outcomes of adults with COVID-19 admitted to ICU in Colombia during the first wave of the pandemic. Material and methods A multicentre retrospective study was carried out in 8 ICUs. Adult patients admitted to the ICU with confirmed SARS-CoV-2 infection from March to July of 2020 were included. Results During the study period, 229 adults with COVID-19 were admitted to ICU. Most patients (54.5%) were older than 65 years. Comorbid conditions were documented in 146 (64%) patients, mainly arterial hypertension and diabetes mellitus. The median value of the SOFA score on ICU admission was 5 (interquartile range, 2–12). Regarding complications, 118 (51.5%) underwent mechanical ventilation, 51 (22.4%) required renal replacement therapy, and 85 (35%), vasopressor use. Mortality was 38.4% (88 out of 219 patients). Mortality increased with age (20% in those younger than 40 years and 54.1% in those older than 65 years; p < .001). In the multivariate analysis, independent factors associated with mortality were age ≥ 65 years (OR, 11.9; 95% CI, 3.20–44.23), SOFA score (OR, 1.21; 95% CI, 1.05–1.39), vasopressor use (OR, 12.8; 95% CI, 3.45–48.17), and renal replacement therapy (OR, 9.0; 95% CI 2.37–34.42). Conclusions Critically ill patients with COVID-19 had high mortality mainly related to advanced age, the severity of the disease on admission to the ICU, the use of vasopressors and renal replacement therapy.
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20
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Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques. SENSORS 2022; 22:s22031211. [PMID: 35161958 PMCID: PMC8838072 DOI: 10.3390/s22031211] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 01/27/2023]
Abstract
Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.
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21
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Miller JL, Tada M, Goto M, Chen H, Dang E, Mohr NM, Lee S. Prediction models for severe manifestations and mortality due to COVID-19: A systematic review. Acad Emerg Med 2022; 29:206-216. [PMID: 35064988 DOI: 10.1111/acem.14447] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Throughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available. OBJECTIVE This systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19. METHODS Searches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and May 2021 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized. RESULTS A primary review found 445 articles relevant based on title and abstract. After further review, 366 were excluded based on the defined inclusion and exclusion criteria. Seventy-nine articles were included in the qualitative analysis. Inter observer agreement on inclusion 0.84 (95%CI 0.78-0.89). When the PROBAST tool was applied, 70 of the 79 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Nine studies reported prediction models that were rated as low risk of bias and low concerns for applicability. CONCLUSION Several prognostic models for COVID-19 were identified, with varying clinical score performance. Nine studies that had a low risk of bias and low concern for applicability, one from a general public population and hospital setting. The most promising and well-validated scores include Clift et al.,15 and Knight et al.,18 which seem to have accurate prediction models that clinicians can use in the public health and emergency department setting.
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Affiliation(s)
- Jamie L. Miller
- University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Masafumi Tada
- Department of Health Promotion and Human Behavior School of Public Health, Kyoto University Graduate School of Medicine Kyoto Japan
| | - Michihiko Goto
- Division of Infectious Diseases, Department of Internal Medicine University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Hao Chen
- University of Iowa Iowa City Iowa USA
| | | | - Nicholas M. Mohr
- Department of Emergency Medicine, Department of Anesthesia, Department of Epidemiology University of Iowa Carver College of Medicine Iowa City Iowa USA
| | - Sangil Lee
- Department of Emergency Medicine The University of Iowa Carver College of Medicine Iowa City Iowa USA
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22
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De Vuono S, Cianci P, Berisha S, Pierini P, Baccarini G, Balducci F, Lignani A, Settimi L, Taliani MR, Groff P. The PaCO2/FiO2 ratio as outcome predictor in SARS-COV-2 related pneumonia: a retrospective study. ACTA BIO-MEDICA : ATENEI PARMENSIS 2022; 93:e2022256. [PMID: 36300224 PMCID: PMC9686167 DOI: 10.23750/abm.v93i5.13229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/16/2022] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND AIM Respiratory failure in SARS-CoV-2 patients is characterized by the presence of hypoxemia and hypocapnia without relevant dyspnea. To date, the use of respiratory parameters other than PaO2/FiO2 ratio to stratify the risk of worsening of these patients has not been sufficiently studied. Aim of this work was to evaluate whether the ratio between partial pressure levels of carbon dioxide (PaCO2) and the fraction of inspired oxygen (FiO2) measured at emergency department (ED) admission is predictive of the clinical course of patients suffering from SARS-CoV-2 pneumonia. METHODS We retrospectively studied 236 patients with SARS-CoV-2 pneumonia evaluated at the ED of the Perugia Hospital. The end-points were: in-hospital mortality, need for invasive mechanical ventilation (IMV) and length of in-hospital stay (LOS). Clinical, blood gas and laboratory data were collected at ED admission. RESULTS Of the 236 patients 157 were male, the mean age was 64 ± 16. Thirtythree patients (14%) needed IMV, 49 died (21%). In the univariate analysis, the PaCO2/FiO2 ratio was inversely associated with the need for IMV (p <0.001), mortality (p <0.001) and LOS (p = 0.005). At the multivariate analysis the PaCO2/FiO2 ratio was found to be predictive of the need for IMV, independently from age, gender, number of comorbidities, neutrophils, lymphocytes, glomerular filtrate, d-dimer, LDH and CRP. CONCLUSIONS the PaCO2/FiO2 ratio is predictive of the risk of respiratory failure worsening in patients with SARS-CoV-2 pneumonia, independently from other several confounding factors.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Paolo Groff
- a:1:{s:5:"en_US";s:34:"ED, Azienda Ospedaliera di Perugia";}.
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23
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Cyprian FS, Suleman M, Abdelhafez I, Doudin A, Masud Danjuma IM, Mir FA, Parray A, Yousaf Z, Siddiqui MYA, Abdelmajid A, Mulhim M, Al-Shokri S, Abukhattab M, Shaheen R, Elkord E, Al-khal AL, Elzouki AN, Girardi G. Complement C5a and Clinical Markers as Predictors of COVID-19 Disease Severity and Mortality in a Multi-Ethnic Population. Front Immunol 2021; 12:707159. [PMID: 34966381 PMCID: PMC8710484 DOI: 10.3389/fimmu.2021.707159] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 11/19/2021] [Indexed: 12/24/2022] Open
Abstract
Coronavirus disease-2019 (COVID-19) was declared as a pandemic by WHO in March 2020. SARS-CoV-2 causes a wide range of illness from asymptomatic to life-threatening. There is an essential need to identify biomarkers to predict disease severity and mortality during the earlier stages of the disease, aiding treatment and allocation of resources to improve survival. The aim of this study was to identify at the time of SARS-COV-2 infection patients at high risk of developing severe disease associated with low survival using blood parameters, including inflammation and coagulation mediators, vital signs, and pre-existing comorbidities. This cohort included 89 multi-ethnic COVID-19 patients recruited between July 14th and October 20th 2020 in Doha, Qatar. According to clinical severity, patients were grouped into severe (n=33), mild (n=33) and asymptomatic (n=23). Common routine tests such as complete blood count (CBC), glucose, electrolytes, liver and kidney function parameters and markers of inflammation, thrombosis and endothelial dysfunction including complement component split product C5a, Interleukin-6, ferritin and C-reactive protein were measured at the time COVID-19 infection was confirmed. Correlation tests suggest that C5a is a predictive marker of disease severity and mortality, in addition to 40 biological and physiological parameters that were found statistically significant between survivors and non-survivors. Survival analysis showed that high C5a levels, hypoalbuminemia, lymphopenia, elevated procalcitonin, neutrophilic leukocytosis, acute anemia along with increased acute kidney and hepatocellular injury markers were associated with a higher risk of death in COVID-19 patients. Altogether, we created a prognostic classification model, the CAL model (C5a, Albumin, and Lymphocyte count) to predict severity with significant accuracy. Stratification of patients using the CAL model could help in the identification of patients likely to develop severe symptoms in advance so that treatments can be targeted accordingly.
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Affiliation(s)
- Farhan S. Cyprian
- Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha, Qatar
- Department of Basic Medical Sciences, College of Medicine, Member of QU Health, Qatar University, Doha, Qatar
| | - Muhammad Suleman
- Institute of Microbiology, University of Veterinary and Animal Sciences, Lahore, Pakistan
| | - Ibrahim Abdelhafez
- Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha, Qatar
| | - Asmma Doudin
- Department of Math and Science, Community College of Qatar, Doha, Qatar
| | - Ibn Mohammed Masud Danjuma
- Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha, Qatar
- Internal Medicine Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | - Fayaz Ahmad Mir
- Qatar Metabolic Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Aijaz Parray
- The Neuroscience Institute, Academic Health System, Hamad Medical Corporation, Doha, Qatar
| | - Zohaib Yousaf
- Internal Medicine Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | | | | | - Mohammad Mulhim
- Communicable Diseases Center, Hamad Medical Corporation, Doha, Qatar
| | - Shaikha Al-Shokri
- Internal Medicine Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | | | - Ranad Shaheen
- Natural and Medical Sciences Research Centre, University of Nizwa, Nizwa, Oman
| | - Eyad Elkord
- Natural and Medical Sciences Research Centre, University of Nizwa, Nizwa, Oman
- Biomedical Research Centre, School of Science, Engineering and Environment, University of Salford, Manchester, United Kingdom
| | | | - Abdel-Naser Elzouki
- Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha, Qatar
- Internal Medicine Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | - Guillermina Girardi
- Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha, Qatar
- Department of Basic Medical Sciences, College of Medicine, Member of QU Health, Qatar University, Doha, Qatar
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24
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Wolfisberg S, Gregoriano C, Struja T, Kutz A, Koch D, Bernasconi L, Hammerer-Lercher A, Mohr C, Haubitz S, Conen A, Fux CA, Mueller B, Schuetz P. Call, chosen, HA 2T 2, ANDC: validation of four severity scores in COVID-19 patients. Infection 2021; 50:651-659. [PMID: 34799814 PMCID: PMC8604199 DOI: 10.1007/s15010-021-01728-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Accepted: 11/04/2021] [Indexed: 01/16/2023]
Abstract
Purpose To externally validate four previously developed severity scores (i.e., CALL, CHOSEN, HA2T2 and ANDC) in patients with COVID-19 hospitalised in a tertiary care centre in Switzerland. Methods This observational analysis included adult patients with a real-time reverse-transcription polymerase chain reaction or rapid-antigen test confirmed severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection hospitalised consecutively at the Cantonal Hospital Aarau from February to December 2020. The primary endpoint was all-cause in-hospital mortality. The secondary endpoint was disease progression, defined as needing invasive ventilation, ICU admission or death. Results From 399 patients (mean age 66.6 years ± 13.4 SD, 68% males), we had complete data for calculating the CALL, CHOSEN, HA2T2 and ANDC scores in 297, 380, 151 and 124 cases, respectively. Odds ratios for all four scores showed significant associations with mortality. The discriminative power of the HA2T2 score was higher compared to CALL, CHOSEN and ANDC scores [area under the curve (AUC) 0.78 vs. 0.65, 0.69 and 0.66, respectively]. Negative predictive values (NPV) for mortality were high, particularly for the CALL score (≥ 6 points: 100%, ≥ 9 points: 95%). For disease progression, discriminative power was lower, with the CHOSEN score showing the best performance (AUC 0.66). Conclusion In this external validation study, the four analysed scores had a lower performance compared to the original cohorts regarding prediction of mortality and disease progression. However, all scores were significantly associated with mortality and the NPV of the CALL and CHOSEN scores in particular allowed reliable identification of patients at low risk, making them suitable for outpatient management. Supplementary Information The online version contains supplementary material available at 10.1007/s15010-021-01728-0.
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Affiliation(s)
- Selina Wolfisberg
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland
| | - Claudia Gregoriano
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland
| | - Tristan Struja
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland
| | - Alexander Kutz
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland
| | - Daniel Koch
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland
| | - Luca Bernasconi
- Institute of Laboratory Medicine, Kantonsspital Aarau, Aarau, Switzerland
| | | | - Christine Mohr
- Department of Infectious Diseases and Hospital Hygiene, Kantonsspital Aarau, Aarau, Switzerland
| | - Sebastian Haubitz
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland.,Department of Infectious Diseases and Hospital Hygiene, Kantonsspital Aarau, Aarau, Switzerland
| | - Anna Conen
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland.,Department of Infectious Diseases and Hospital Hygiene, Kantonsspital Aarau, Aarau, Switzerland
| | - Christoph A Fux
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland.,Department of Infectious Diseases and Hospital Hygiene, Kantonsspital Aarau, Aarau, Switzerland
| | - Beat Mueller
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland.,Medical Faculty, University of Basel, Basel, Switzerland
| | - Philipp Schuetz
- Medical University Department of Medicine, Kantonsspital Aarau, Tellstrasse, 5001, Aarau, Switzerland. .,Medical Faculty, University of Basel, Basel, Switzerland.
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25
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He J, Song C, Liu E, Liu X, Wu H, Lin H, Liu Y, Li Q, Xu Z, Ren X, Zhang C, Zhang W, Duan W, Tian Y, Li P, Hu M, Yang S, Xu Y. Establishment of Routine Clinical Indicators-Based Nomograms for Predicting the Mortality in Patients With COVID-19. Front Med (Lausanne) 2021; 8:706380. [PMID: 34733858 PMCID: PMC8558233 DOI: 10.3389/fmed.2021.706380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 09/09/2021] [Indexed: 01/08/2023] Open
Abstract
This study aimed to establish and validate the nomograms to predict the mortality risk of patients with coronavirus disease 2019 (COVID-19) using routine clinical indicators. This retrospective study included a development cohort enrolled 2,119 hospitalized patients with COVID-19 and a validation cohort included 1,504 patients with COVID-19. The demographics, clinical manifestations, vital signs, and laboratory tests of the patients at admission and outcome of in-hospital death were recorded. The independent factors associated with death were identified by a forward stepwise multivariate logistic regression analysis and used to construct the two prognostic nomograms. The nomogram 1 was a full model to include nine factors identified in the multivariate logistic regression and nomogram 2 was built by selecting four factors from nine to perform as a reduced model. The nomogram 1 and nomogram 2 showed better performance in discrimination and calibration than the Multilobular infiltration, hypo-Lymphocytosis, Bacterial coinfection, Smoking history, hyper-Tension and Age (MuLBSTA) score in training. In validation, nomogram 1 performed better than nomogram 2 for calibration. We recommend the application of nomogram 1 in general hospitals which provide robust prognostic performance though more cumbersome; nomogram 2 in the out-patient, emergency department, and mobile cabin hospitals, which depend on less laboratory examinations to make the assessment more convenient. Both the nomograms can help the clinicians to identify the patients at risk of death with routine clinical indicators at admission, which may reduce the overall mortality of COVID-19.
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Affiliation(s)
- Jialin He
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Gastroenterology, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Caiping Song
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - En Liu
- Department of Gastroenterology, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Xi Liu
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Gastroenterology, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Hao Wu
- Xinqiao Hospital, The Army Medical University, Chongqing, China.,Taikang Tongji Hospital, Wuhan, China
| | - Hui Lin
- Department of Gastroenterology, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Yuliang Liu
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qi Li
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Respiratory and Critical Care Medicine, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Zhi Xu
- Taikang Tongji Hospital, Wuhan, China.,Department of Respiratory and Critical Care Medicine, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - XiaoBao Ren
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Emergency, Xinan Hospital, The Army Medical University, Chongqing, China
| | - Cheng Zhang
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Hematology, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Wenjing Zhang
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Respiratory and Critical Care Medicine, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Wei Duan
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Neurology, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Yongfeng Tian
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Endocrinology, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Ping Li
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Cardiology, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Mingdong Hu
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Respiratory and Critical Care Medicine, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Shiming Yang
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Gastroenterology, Xinqiao Hospital, The Army Medical University, Chongqing, China
| | - Yu Xu
- Huo-Shen-Shan Hospital, Wuhan, China.,Jin Yin-tan Hospital, The Medical Team of the Army Medical University, Wuhan, China.,Department of Respiratory and Critical Care Medicine, Xinqiao Hospital, The Army Medical University, Chongqing, China
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26
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Ulloque‐Badaracco JR, Ivan Salas‐Tello W, Al‐kassab‐Córdova A, Alarcón‐Braga EA, Benites‐Zapata VA, Maguiña JL, Hernandez AV. Prognostic value of neutrophil-to-lymphocyte ratio in COVID-19 patients: A systematic review and meta-analysis. Int J Clin Pract 2021; 75:e14596. [PMID: 34228867 PMCID: PMC9614707 DOI: 10.1111/ijcp.14596] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 07/01/2021] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Neutrophil-to-lymphocyte ratio (NLR) is an accessible and widely used biomarker. NLR may be used as an early marker of poor prognosis in patients with COVID-19. OBJECTIVE To evaluate the prognostic value of the NLR in patients diagnosed with COVID-19. METHODS We conducted a systematic review and meta-analysis. Observational studies that reported the association between baseline NLR values (ie, at hospital admission) and severity or all-cause mortality in COVID-19 patients were included. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS). Random effects models and inverse variance method were used for meta-analyses. The effects were expressed as odds ratios (ORs) and their 95% confidence intervals (CIs). Small study effects were assessed with the Egger's test. RESULTS We analysed 61 studies (n = 15 522 patients), 58 cohorts, and 3 case-control studies. An increase of one unit of NLR was associated with higher odds of severity (OR 6.22; 95%CI 4.93 to 7.84; P < .001) and higher odds of all-cause mortality (OR 12.6; 95%CI 6.88 to 23.06; P < .001). In our sensitivity analysis, we found that 41 studies with low risk of bias and moderate heterogeneity (I2 = 53% and 58%) maintained strong association between NLR values and both outcomes (severity: OR 5.36; 95% CI 4.45 to 6.45; P < .001; mortality: OR 10.42 95% CI 7.73 to 14.06; P = .005). CONCLUSIONS Higher values of NLR were associated with severity and all-cause mortality in hospitalised COVID-19 patients.
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Affiliation(s)
| | | | | | | | - Vicente A. Benites‐Zapata
- Vicerrectorado de Investigación Unidad de Investigación para la Generación y Síntesis de Evidencias en Salud, Vicerrectorado de InvestigaciónUniversidad San Ignacio de LoyolaLimaPeru
| | - Jorge L. Maguiña
- Escuela de MedicinaUniversidad Peruana de Ciencias AplicadasLimaPeru
- Instituto de Evaluación de Tecnologías en Salud e Investigación — IETSI, EsSaludLimaPeru
| | - Adrian V. Hernandez
- Unidad de Revisiones Sistemáticas y Meta‐análisis, Guías de Práctica Clínica y Evaluaciones de Tecnología Sanitaria, Vicerrectorado de InvestigaciónUniversidad San Ignacio de LoyolaLimaPeru
- Health OutcomesPolicy, and Evidence Synthesis (HOPES) Group, University of Connecticut School of PharmacyMansfieldCTUSA
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Lombardi Y, Azoyan L, Szychowiak P, Bellamine A, Lemaitre G, Bernaux M, Daniel C, Leblanc J, Riller Q, Steichen O. External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals. Intensive Care Med 2021; 47:1426-1439. [PMID: 34585270 PMCID: PMC8478265 DOI: 10.1007/s00134-021-06524-w] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 08/30/2021] [Indexed: 12/21/2022]
Abstract
Purpose The Coronavirus disease 2019 (COVID-19) has led to an unparalleled influx of patients. Prognostic scores could help optimizing healthcare delivery, but most of them have not been comprehensively validated. We aim to externally validate existing prognostic scores for COVID-19. Methods We used “COVID-19 Evidence Alerts” (McMaster University) to retrieve high-quality prognostic scores predicting death or intensive care unit (ICU) transfer from routinely collected data. We studied their accuracy in a retrospective multicenter cohort of adult patients hospitalized for COVID-19 from January 2020 to April 2021 in the Greater Paris University Hospitals. Areas under the receiver operating characteristic curves (AUC) were computed for the prediction of the original outcome, 30-day in-hospital mortality and the composite of 30-day in-hospital mortality or ICU transfer. Results We included 14,343 consecutive patients, 2583 (18%) died and 5067 (35%) died or were transferred to the ICU. We examined 274 studies and found 32 scores meeting the inclusion criteria: 19 had a significantly lower AUC in our cohort than in previously published validation studies for the original outcome; 25 performed better to predict in-hospital mortality than the composite of in-hospital mortality or ICU transfer; 7 had an AUC > 0.75 to predict in-hospital mortality; 2 had an AUC > 0.70 to predict the composite outcome. Conclusion Seven prognostic scores were fairly accurate to predict death in hospitalized COVID-19 patients. The 4C Mortality Score and the ABCS stand out because they performed as well in our cohort and their initial validation cohort, during the first epidemic wave and subsequent waves, and in younger and older patients. Supplementary Information The online version contains supplementary material available at 10.1007/s00134-021-06524-w.
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Affiliation(s)
- Yannis Lombardi
- Faculty of Medicine, AP-HP, Sorbonne Université, Paris, France
| | - Loris Azoyan
- Faculty of Medicine, AP-HP, Sorbonne Université, Paris, France
| | - Piotr Szychowiak
- Médecine Intensive-Réanimation, Centre Hospitalier Régional Universitaire de Tours, Tours, France.,Université de Tours, Tours, France
| | | | | | - Mélodie Bernaux
- Strategy and Transformation Department, AP-HP, Paris, France
| | | | - Judith Leblanc
- Institut Pierre Louis d'Épidémiologie et de Santé Publique, UMR-S 1136 , Sorbonne Université, INSERM, Paris, France.,Clinical Research Platform, Saint Antoine Hospital, AP-HP, Paris, France
| | - Quentin Riller
- Faculty of Medicine, AP-HP, Sorbonne Université, Paris, France
| | - Olivier Steichen
- Institut Pierre Louis d'Épidémiologie et de Santé Publique, UMR-S 1136 , Sorbonne Université, INSERM, Paris, France. .,Internal Medicine Department, Tenon Hospital, AP-HP, Sorbonne Université, Paris, France. .,Service de Médecine Interne, Hôpital Tenon, 4 rue de la Chine, 75020, Paris, France.
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Guner R, Kayaaslan B, Hasanoglu I, Aypak A, Bodur H, Ates I, Akinci E, Erdem D, Eser F, Izdes S, Kalem AK, Bastug A, Karalezli A, Surel AA, Ayhan M, Karaahmetoglu S, Turan IO, Arguder E, Ozdemir B, Mutlu MN, Bilir YA, Sarıcaoglu EM, Gokcinar D, Gunay S, Dinc B, Gemcioglu E, Bilmez R, Aydos O, Asilturk D, Inan O, Buzgan T. Development and validation of nomogram to predict severe illness requiring intensive care follow up in hospitalized COVID-19 cases. BMC Infect Dis 2021; 21:1004. [PMID: 34563117 PMCID: PMC8467006 DOI: 10.1186/s12879-021-06656-w] [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: 08/07/2020] [Accepted: 09/03/2021] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Early identification of severe COVID-19 patients who will need intensive care unit (ICU) follow-up and providing rapid, aggressive supportive care may reduce mortality and provide optimal use of medical resources. We aimed to develop and validate a nomogram to predict severe COVID-19 cases that would need ICU follow-up based on available and accessible patient values. METHODS Patients hospitalized with laboratory-confirmed COVID-19 between March 15, 2020, and June 15, 2020, were enrolled in this retrospective study with 35 variables obtained upon admission considered. Univariate and multivariable logistic regression models were constructed to select potential predictive parameters using 1000 bootstrap samples. Afterward, a nomogram was developed with 5 variables selected from multivariable analysis. The nomogram model was evaluated by Area Under the Curve (AUC) and bias-corrected Harrell's C-index with 95% confidence interval, Hosmer-Lemeshow Goodness-of-fit test, and calibration curve analysis. RESULTS Out of a total of 1022 patients, 686 cases without missing data were used to construct the nomogram. Of the 686, 104 needed ICU follow-up. The final model includes oxygen saturation, CRP, PCT, LDH, troponin as independent factors for the prediction of need for ICU admission. The model has good predictive power with an AUC of 0.93 (0.902-0.950) and a bias-corrected Harrell's C-index of 0.91 (0.899-0.947). Hosmer-Lemeshow test p-value was 0.826 and the model is well-calibrated (p = 0.1703). CONCLUSION We developed a simple, accessible, easy-to-use nomogram with good distinctive power for severe illness requiring ICU follow-up. Clinicians can easily predict the course of COVID-19 and decide the procedure and facility of further follow-up by using clinical and laboratory values of patients available upon admission.
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Affiliation(s)
- Rahmet Guner
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
| | - Bircan Kayaaslan
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey.
| | - Imran Hasanoglu
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
| | - Adalet Aypak
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Hurrem Bodur
- Department of Infectious Disease and Clinical Microbiology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Ihsan Ates
- Department of Internal Medicine, Ankara City Hospital, Ankara, Turkey
| | - Esragul Akinci
- Department of Infectious Disease and Clinical Microbiology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Deniz Erdem
- Department of Anesthesiology and Reanimation, Ankara City Hospital, Ankara, Turkey
| | - Fatma Eser
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
| | - Seval Izdes
- Department of Anesthesiology and Reanimation and Intensive Care Unıt, Ankara Yildirim Beyazit University, Ankara City Hospital, Ankara, Turkey
| | - Ayse Kaya Kalem
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
| | - Aliye Bastug
- Department of Infectious Disease and Clinical Microbiology, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Aysegul Karalezli
- Department of Pulmonary Diseases, Ankara Yildirim Beyazit University, Ankara City Hospital, Ankara, Turkey
| | - Aziz Ahmet Surel
- Department of General Surgery, Ankara City Hospital, Ankara, Turkey
| | - Muge Ayhan
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | | | - Isıl Ozkocak Turan
- Department of Anesthesiology and Reanimation, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Emine Arguder
- Department of Anesthesiology and Reanimation and Intensive Care Unıt, Ankara Yildirim Beyazit University, Ankara City Hospital, Ankara, Turkey
| | - Burcu Ozdemir
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Mehmet Nevzat Mutlu
- Department of Anesthesiology and Reanimation, Ankara City Hospital, Ankara, Turkey
| | - Yesim Aybar Bilir
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Elif Mukime Sarıcaoglu
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Derya Gokcinar
- Department of Anesthesiology and Reanimation, University of Health Sciences, Ankara City Hospital, Ankara, Turkey
| | - Sibel Gunay
- Department of Pulmonary Diseases, Ankara City Hospital, Ankara, Turkey
| | - Bedia Dinc
- Department of Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Emin Gemcioglu
- Department of Internal Medicine, Ankara City Hospital, Ankara, Turkey
| | - Ruveyda Bilmez
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Omer Aydos
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Dilek Asilturk
- Department of Infectious Disease and Clinical Microbiology, Ankara City Hospital, Ankara, Turkey
| | - Osman Inan
- Department of Internal Medicine, Ankara City Hospital, Ankara, Turkey
| | - Turan Buzgan
- Department of Infectious Disease and Clinical Microbiology, Ankara Yildirim Beyazit University, Ankara City Hospital, Bilkent Street no:1, Ankara, 06800, Turkey
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Chu K, Alharahsheh B, Garg N, Guha P. Evaluating risk stratification scoring systems to predict mortality in patients with COVID-19. BMJ Health Care Inform 2021; 28:bmjhci-2021-100389. [PMID: 34521623 PMCID: PMC8441221 DOI: 10.1136/bmjhci-2021-100389] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 08/24/2021] [Indexed: 12/23/2022] Open
Abstract
Background The COVID-19 pandemic has necessitated efficient and accurate triaging of patients for more effective allocation of resources and treatment. Objectives The objectives are to investigate parameters and risk stratification tools that can be applied to predict mortality within 90 days of hospital admission in patients with COVID-19. Methods A literature search of original studies assessing systems and parameters predicting mortality of patients with COVID-19 was conducted using MEDLINE and EMBASE. Results 589 titles were screened, and 76 studies were found investigating the prognostic ability of 16 existing scoring systems (area under the receiving operator curve (AUROC) range: 0.550–0.966), 38 newly developed COVID-19-specific prognostic systems (AUROC range: 0.6400–0.9940), 15 artificial intelligence (AI) models (AUROC range: 0.840–0.955) and 16 studies on novel blood parameters and imaging. Discussion Current scoring systems generally underestimate mortality, with the highest AUROC values found for APACHE II and the lowest for SMART-COP. Systems featuring heavier weighting on respiratory parameters were more predictive than those assessing other systems. Cardiac biomarkers and CT chest scans were the most commonly studied novel parameters and were independently associated with mortality, suggesting potential for implementation into model development. All types of AI modelling systems showed high abilities to predict mortality, although none had notably higher AUROC values than COVID-19-specific prediction models. All models were found to have bias, including lack of prospective studies, small sample sizes, single-centre data collection and lack of external validation. Conclusion The single parameters established within this review would be useful to look at in future prognostic models in terms of the predictive capacity their combined effect may harness.
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Affiliation(s)
- Kelly Chu
- Faculty of Medicine, Imperial College London, London, UK
| | | | - Naveen Garg
- Faculty of Medicine, Imperial College London, London, UK
| | - Payal Guha
- Faculty of Medicine, Imperial College London, London, UK
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30
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Bilge M, Akilli IK, Karaayvaz EB, Yesilova A, Kart Yasar K. Comparison of systemic immune-inflammation index (SII), early warning score (ANDC) and prognostic nutritional index (PNI) in hospitalized patients with malignancy, and their influence on mortality from COVID-19. Infect Agent Cancer 2021; 16:60. [PMID: 34526045 PMCID: PMC8441248 DOI: 10.1186/s13027-021-00400-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 08/24/2021] [Indexed: 12/18/2022] Open
Abstract
Introduction We evaluated several biological indicators based on inflammation and/or nutritional status, such as systemic immune-inflammation index (SII), early warning score (ANDC) and prognostic nutritional index (PNI) in hospitalized COVID-19 patients with and without malignancies for a prognostic significance. Methodology This is a retrospective and observational study on 186 patients with SARS-CoV-2, who were diagnosed with COVID-19 by real-time PCR testing and hospitalized due to COVID-19 pneumonia. 75 patients had various malignancies, and the rest (111), having a similar age and comorbidity profile based on propensity score matching, had no malignancy. Results None of the measures as neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, monocyte to lymphocyte ratio, SII, PNI or ANDC was found to be significantly different between two groups. Odds ratio for the mortality, OR 2.39 (%95 CI 1.80–3.16) was found to be significantly higher for the malignancy group, even though the duration of hospitalization was statistically similar for both groups. PNI was found to be significantly lower for deceased patients compared with survivors in the malignancy group. Contrarily, ANDC was found to be significantly higher for deceased patients in the malignancy group. Conclusions PNI and ANDC have independent predictive power on determining the in-hospital death in COVID-19 malignancy cases. It is suggested that ANDC seems to be a more sensitive score than SII in COVID-19 cases with malignancies.
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Affiliation(s)
- Muge Bilge
- Department of Internal Medicine, Prof. Dr. Cemil Tascioglu City Hospital, University of Health Sciences, Darulaceze Street, No: 27 Sisli, 34384, Istanbul, Turkey.
| | - Isil Kibar Akilli
- Department of Pulmonary Disease, Sisli Hamidiye Etfal Training and Research Hospital, University of Health Sciences, Halaskargazi Street, 34371, Istanbul, Turkey
| | - Ekrem Bilal Karaayvaz
- Department of Cardiology, Istanbul Medical Faculty, University of Istanbul, Turgut Ozal Millet Street, Fatih, 34093, Istanbul, Turkey
| | - Aylia Yesilova
- Department of Internal Medicine, Prof. Dr. Cemil Tascioglu City Hospital, University of Health Sciences, Darulaceze Street, No: 27 Sisli, 34384, Istanbul, Turkey
| | - Kadriye Kart Yasar
- Department of Infectious Disease, Bakirkoy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences, Dr. Tevfik Saglam Street, No: 11, Bakirkoy, 34147, Istanbul, Turkey
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Moon HJ, Kim K, Kang EK, Yang HJ, Lee E. Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram. J Korean Med Sci 2021; 36:e248. [PMID: 34490756 PMCID: PMC8422041 DOI: 10.3346/jkms.2021.36.e248] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 08/22/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. METHODS This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. RESULTS Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/. CONCLUSION The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.
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Affiliation(s)
- Hui Jeong Moon
- SCH Biomedical Informatics Research Unit, Soonchunhyang University Seoul Hospital, Seoul, Korea
- STAT Team, C&R Research Inc., Seoul, Korea
| | - Kyunghoon Kim
- Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Eun Kyeong Kang
- Department of Pediatrics, Dongguk University Ilsan Hospital, Goyang, Korea
| | - Hyeon-Jong Yang
- Department of Pediatrics, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Seoul, Korea.
| | - Eun Lee
- Department of Pediatrics, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea.
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Rahman T, Al-Ishaq FA, Al-Mohannadi FS, Mubarak RS, Al-Hitmi MH, Islam KR, Khandakar A, Hssain AA, Al-Madeed S, Zughaier SM, Chowdhury MEH. Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique. Diagnostics (Basel) 2021; 11:1582. [PMID: 34573923 PMCID: PMC8469072 DOI: 10.3390/diagnostics11091582] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/10/2021] [Accepted: 08/25/2021] [Indexed: 12/24/2022] Open
Abstract
Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.
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Affiliation(s)
- Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (K.R.I.); (A.K.)
| | - Fajer A. Al-Ishaq
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar; (F.A.A.-I.); (F.S.A.-M.); (R.S.M.); (M.H.A.-H.)
| | - Fatima S. Al-Mohannadi
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar; (F.A.A.-I.); (F.S.A.-M.); (R.S.M.); (M.H.A.-H.)
| | - Reem S. Mubarak
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar; (F.A.A.-I.); (F.S.A.-M.); (R.S.M.); (M.H.A.-H.)
| | - Maryam H. Al-Hitmi
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar; (F.A.A.-I.); (F.S.A.-M.); (R.S.M.); (M.H.A.-H.)
| | - Khandaker Reajul Islam
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (K.R.I.); (A.K.)
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (K.R.I.); (A.K.)
| | | | - Somaya Al-Madeed
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar;
| | - Susu M. Zughaier
- Department of Basic Medical Sciences, College of Medicine, QU Health, Qatar University, Doha 2713, Qatar; (F.A.A.-I.); (F.S.A.-M.); (R.S.M.); (M.H.A.-H.)
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (T.R.); (K.R.I.); (A.K.)
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Alunno A, Najm A, Machado PM, Bertheussen H, Burmester GR, Carubbi F, De Marco G, Giacomelli R, Hermine O, Isaacs JD, Koné-Paut I, Magro-Checa C, McInnes I, Meroni PL, Quartuccio L, Ramanan AV, Ramos-Casals M, Rodríguez Carrio J, Schulze-Koops H, Stamm TA, Tas SW, Terrier B, McGonagle DG, Mariette X. EULAR points to consider on pathophysiology and use of immunomodulatory therapies in COVID-19. Ann Rheum Dis 2021; 80:698-706. [PMID: 33547062 PMCID: PMC7871226 DOI: 10.1136/annrheumdis-2020-219724] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/11/2021] [Accepted: 01/27/2021] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Severe systemic inflammation associated with some stages of COVID-19 and in fatal cases led therapeutic agents developed or used frequently in Rheumatology being at the vanguard of experimental therapeutics strategies. The aim of this project was to elaborate EULAR Points to consider (PtCs) on COVID-19 pathophysiology and immunomodulatory therapies. METHODS PtCs were developed in accordance with EULAR standard operating procedures for endorsed recommendations, led by an international multidisciplinary Task Force, including rheumatologists, translational immunologists, haematologists, paediatricians, patients and health professionals, based on a systemic literature review up to 15 December 2020. Overarching principles (OPs) and PtCs were formulated and consolidated by formal voting. RESULTS Two OPs and fourteen PtCs were developed. OPs highlight the heterogeneous clinical spectrum of SARS-CoV-2 infection and the need of a multifaceted approach to target the different pathophysiological mechanisms. PtCs 1-6 encompass the pathophysiology of SARS-CoV-2 including immune response, endothelial dysfunction and biomarkers. PtCs 7-14 focus on the management of SARS-CoV-2 infection with immunomodulators. There was evidence supporting the use of glucocorticoids, especially dexamethasone, in COVID-19 cases requiring oxygen therapy. No other immunomodulator demonstrated efficacy on mortality to date, with however inconsistent results for tocilizumab. Immunomodulatory therapy was not associated with higher infection rates. CONCLUSIONS Multifactorial pathophysiological mechanisms, including immune abnormalities, play a key role in COVID-19. The efficacy of glucocorticoids in cases requiring oxygen therapy suggests that immunomodulatory treatment might be effective in COVID-19 subsets. Involvement of rheumatologists, as systemic inflammatory diseases experts, should continue in ongoing clinical trials delineating optimal immunomodulatory therapy utilisation in COVID-19.
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Affiliation(s)
- Alessia Alunno
- Rheumatology Unit, Department of Medicine, University of Perugia, Perugia, Italy
| | - Aurélie Najm
- Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Pedro M Machado
- Centre for Rheumatology & Department of Neuromuscular Diseases, University College London, London, UK
- National Institute for Health Research (NIHR) Biomedical Research Centre (BRC), University College London Hospitals NHS Foundation Trust, London, UK
- Department of Rheumatology, Northwick Park Hospital, London North West University Healthcare NHS Trust, London, UK
| | | | - Gerd R Burmester
- Department of Rheumatology and Clinical Immunology, Charité - Universitätsmedizin Berlin, Freie Universität und Humboldt-Universität Berlin, Berlin, Germany
| | - Francesco Carubbi
- Department of Medicine, ASL 1 Avezzano-Sulmona-L'Aquila, Internal Medicine and Nephrology Unit, Department of Life, Health & Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Gabriele De Marco
- The Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
| | - Roberto Giacomelli
- Rheumatology and Clinical Immunology Unit, University of Rome "Campus Biomedico" School of Medicine Rome, Rome, Italy
| | - Olivier Hermine
- Department of Haematology, Hôpital Necker, Assistance Publique - Hôpitaux de Paris, Paris, France
- INSERM UMR1183, Institut Imagine, Université de Paris, Paris, France
| | - John D Isaacs
- Translational and Clinical Research Institute, Newcastle University and Musculoskeletal Unit, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Isabelle Koné-Paut
- Service de Rhumatologie Pédiatrique, Centre de Référence des Maladies Auto-Inflammatoires de l'enfant, Hôpital Bicêtre, AP HP, Université Paris Sud, Bicètre, France
| | - César Magro-Checa
- Department of Rheumatology, Zuyderland Medical Centre Heerlen, Heerlen, The Netherlands
| | - Iain McInnes
- Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Pier Luigi Meroni
- Experimental Laboratory of Immunological and Rheumatologic Researches, Istituto Auxologico Italiano, IRCCS, Milan, Italy
| | - Luca Quartuccio
- Department of Medicine, Rheumatology Clinic, University of Udine, ASUFC Udine, Udine, Italy
| | - Athimalaipet V Ramanan
- University Hospitals Bristol NHS Foundation Trust, Bristol, UK
- University of Bristol Translational Health Sciences, Bristol, UK
| | - Manuel Ramos-Casals
- Department of Autoimmune Diseases, ICMiD, Laboratory of Autoimmune Diseases Josep Font, IDIBAPS-CELLEX, Department of Autoimmune Diseases, ICMiD, University of Barcelona, Hospital Clínic, Barcelona, Spain
| | - Javier Rodríguez Carrio
- Department of Functional Biology, Immunology Area, Faculty of Medicine, University of Oviedo, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
| | - Hendrik Schulze-Koops
- Division of Rheumatology and Clinical Immunology, Department of Internal Medicine IV, Ludwig-Maximilians University of Munich, Munchen, Germany
| | - Tanja A Stamm
- Section for Outcomes Research, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna and Ludwig Boltzmann Institute for Arthritis and Rehabilitation, Wien, Austria
| | - Sander W Tas
- Department of Rheumatology and Clinical Immunology, Amsterdam Rheumatology and Immunology Center, Amsterdam University Medical Centres, Amsterdam, The Netherlands
| | - Benjamin Terrier
- Department of Internal Medicine, Cochin University Hospital, Paris, France; National Referral Centre for Systemic and Autoimmune Diseases, University Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Dennis G McGonagle
- The Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK
| | - Xavier Mariette
- Assistance Publique-Hôpitaux de Paris, Hôpital Bicêtre, INSERM UMR1184, Department of Rheumatology, Université Paris-Saclay, Le Kremlin Bicêtre, France
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Martín MC, Jurado A, Abad-Molina C, Orduña A, Yarce O, Navas AM, Cunill V, Escobar D, Boix F, Burillo-Sanz S, Vegas-Sánchez MC, Jiménez-de Las Pozas Y, Melero J, Aguilar M, Sobieschi OI, López-Hoyos M, Ocejo-Vinyals G, San Segundo D, Almeida D, Medina S, Fernández L, Vergara E, Quirant B, Martínez-Cáceres E, Boiges M, Alonso M, Esparcia-Pinedo L, López-Sanz C, Muñoz-Vico J, López-Palmero S, Trujillo A, Álvarez P, Prada Á, Monzón D, Ontañón J, Marco FM, Mora S, Rojo R, González-Martínez G, Martínez-Saavedra MT, Gil-Herrera J, Cantenys-Molina S, Hernández M, Perurena-Prieto J, Rodríguez-Bayona B, Martínez A, Ocaña E, Molina J. The age again in the eye of the COVID-19 storm: evidence-based decision making. IMMUNITY & AGEING 2021; 18:24. [PMID: 34016150 PMCID: PMC8134808 DOI: 10.1186/s12979-021-00237-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/11/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND One hundred fifty million contagions, more than 3 million deaths and little more than 1 year of COVID-19 have changed our lives and our health management systems forever. Ageing is known to be one of the significant determinants for COVID-19 severity. Two main reasons underlie this: immunosenescence and age correlation with main COVID-19 comorbidities such as hypertension or dyslipidaemia. This study has two aims. The first is to obtain cut-off points for laboratory parameters that can help us in clinical decision-making. The second one is to analyse the effect of pandemic lockdown on epidemiological, clinical, and laboratory parameters concerning the severity of the COVID-19. For these purposes, 257 of SARSCoV2 inpatients during pandemic confinement were included in this study. Moreover, 584 case records from a previously analysed series, were compared with the present study data. RESULTS Concerning the characteristics of lockdown series, mild cases accounted for 14.4, 54.1% were moderate and 31.5%, severe. There were 32.5% of home contagions, 26.3% community transmissions, 22.5% nursing home contagions, and 8.8% corresponding to frontline worker contagions regarding epidemiological features. Age > 60 and male sex are hereby confirmed as severity determinants. Equally, higher severity was significantly associated with higher IL6, CRP, ferritin, LDH, and leukocyte counts, and a lower percentage of lymphocyte, CD4 and CD8 count. Comparing this cohort with a previous 584-cases series, mild cases were less than those analysed in the first moment of the pandemic and dyslipidaemia became more frequent than before. IL-6, CRP and LDH values above 69 pg/mL, 97 mg/L and 328 U/L respectively, as well as a CD4 T-cell count below 535 cells/μL, were the best cut-offs predicting severity since these parameters offered reliable areas under the curve. CONCLUSION Age and sex together with selected laboratory parameters on admission can help us predict COVID-19 severity and, therefore, make clinical and resource management decisions. Demographic features associated with lockdown might affect the homogeneity of the data and the robustness of the results.
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Affiliation(s)
- María C Martín
- Centro de Hemoterapia y Hemodonación de Castilla y León, Valladolid, Spain
| | - Aurora Jurado
- Department of Immunology and Allergology, Hospital Universitario Reina Sofía-Instituto de Investigación Biomédica de Córdoba (IMIBIC), Avd. Menéndez Pidal s/n, 14004, Córdoba, Spain.
| | - Cristina Abad-Molina
- Department of Microbiology and Immunology, Hospital Clínico Universitario, Valladolid, Spain
| | - Antonio Orduña
- Department of Microbiology and Immunology, Hospital Clínico Universitario, Valladolid, Spain
| | - Oscar Yarce
- Department of Immunology and Allergology, Hospital Universitario Reina Sofía-Instituto de Investigación Biomédica de Córdoba (IMIBIC), Avd. Menéndez Pidal s/n, 14004, Córdoba, Spain
| | - Ana M Navas
- Department of Immunology and Allergology, Hospital Universitario Reina Sofía-Instituto de Investigación Biomédica de Córdoba (IMIBIC), Avd. Menéndez Pidal s/n, 14004, Córdoba, Spain
| | - Vanesa Cunill
- Department of Immunology, Hospital Universitario Son Espases-Human Immunopathology Research Laboratory, Institut d'Investigació Sanitària de les Illes Balears (IdISBa), Palma de Mallorca, Spain
| | - Danilo Escobar
- Department of Immunology, Hospital Universitario Son Espases-Human Immunopathology Research Laboratory, Institut d'Investigació Sanitària de les Illes Balears (IdISBa), Palma de Mallorca, Spain
| | - Francisco Boix
- Department of Immunology, Hospital Clínico Universitario, Salamanca, Spain
| | | | | | | | - Josefa Melero
- Department of Immunology, Hospital Universitario de Badajoz, Badajoz, Spain
| | - Marta Aguilar
- Department of Immunology, Hospital Universitario de Badajoz, Badajoz, Spain
| | | | - Marcos López-Hoyos
- Department of Immunology, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Gonzalo Ocejo-Vinyals
- Department of Immunology, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - David San Segundo
- Department of Immunology, Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - Delia Almeida
- Laboratory of Immunology, Complejo Hospitalario Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, Spain
| | - Silvia Medina
- Laboratory of Immunology, Complejo Hospitalario Nuestra Señora de la Candelaria, Santa Cruz de Tenerife, Spain
| | - Luis Fernández
- Laboratoy of Immunology and Genetics, Hospital San Pedro de Alcántara, Cáceres, Spain
| | - Esther Vergara
- Laboratoy of Immunology and Genetics, Hospital San Pedro de Alcántara, Cáceres, Spain
| | - Bibiana Quirant
- Department of Immunology, Hospital Germans Trias i Pujols, Barcelona, Spain
| | | | - Marc Boiges
- Department of Immunology, Hospital Germans Trias i Pujols, Barcelona, Spain
| | - Marta Alonso
- Department of Immunology, Hospital de Cruces, Baracaldo, Spain
| | | | - Celia López-Sanz
- Department of Immunology, Hospital Universitario La Princesa, Madrid, Spain
| | | | | | - Antonio Trujillo
- Department of Immunology and Allergology, Hospital Universitario Reina Sofía-Instituto de Investigación Biomédica de Córdoba (IMIBIC), Avd. Menéndez Pidal s/n, 14004, Córdoba, Spain
| | - Paula Álvarez
- Department of Immunology and Allergology, Hospital Universitario Reina Sofía-Instituto de Investigación Biomédica de Córdoba (IMIBIC), Avd. Menéndez Pidal s/n, 14004, Córdoba, Spain
| | - Álvaro Prada
- Department of Immunology, Hospital de Donostia, San Sebastián, Spain
| | - David Monzón
- Department of Immunology, Hospital de Donostia, San Sebastián, Spain
| | - Jesús Ontañón
- Unit of Immunology, Hospital General Universitario, Albacete, Spain
| | | | - Sergio Mora
- Laboratory Unit, Hospital General, Alicante, Spain
| | - Ricardo Rojo
- Department of Immunology, Complejo Hospitalario, La Coruña, Spain
| | - Gema González-Martínez
- Unit of Immunology, Hospital Universitario Insular-Materno Infantil, Las Palmas de Gran Canaria, Spain
| | - María T Martínez-Saavedra
- Unit of Immunology, Hospital Universitario Insular-Materno Infantil, Las Palmas de Gran Canaria, Spain
| | - Juana Gil-Herrera
- Department of Immunology, Hospital General Universitario e Instituto de Investigación Sanitaria, "Gregorio Marañón", Madrid, Spain
| | - Sergi Cantenys-Molina
- Department of Immunology, Hospital General Universitario e Instituto de Investigación Sanitaria, "Gregorio Marañón", Madrid, Spain
| | - Manuel Hernández
- Department of Immunology, Hospital Universitario Vall d'Hebron, Barcelona, Spain
| | | | | | | | - Esther Ocaña
- Laboratory Unit, Complejo Hospitalario, Jaén, Spain
| | - Juan Molina
- Department of Immunology and Allergology, Hospital Universitario Reina Sofía-Instituto de Investigación Biomédica de Córdoba (IMIBIC), Avd. Menéndez Pidal s/n, 14004, Córdoba, Spain
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Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Abul Kashem SB, Islam MT, Al Maadeed S, Zughaier SM, Khan MS, Chowdhury ME. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Comput Biol Med 2021; 132:104319. [PMID: 33799220 PMCID: PMC7946571 DOI: 10.1016/j.compbiomed.2021.104319] [Citation(s) in RCA: 234] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 03/03/2021] [Accepted: 03/04/2021] [Indexed: 02/06/2023]
Abstract
Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.
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Affiliation(s)
- Tawsifur Rahman
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Yazan Qiblawey
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Anas Tahir
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar
| | - Saad Bin Abul Kashem
- Faculty of Robotics and Advanced Computing, Qatar Armed Forces Academic Bridge Program, Qatar Foundation, Doha, 24404, Qatar
| | - Mohammad Tariqul Islam
- Dept. of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia
| | - Somaya Al Maadeed
- Department of Computer Science and Engineering, Qatar University, Doha, 2713, Qatar
| | - Susu M. Zughaier
- Department of Basic Medical Sciences, College of Medicine, Biomedical and Pharmaceutical Research Unit, QU Health, Qatar University, Doha, 2713, Qatar
| | - Muhammad Salman Khan
- Department of Electrical Engineering (JC), University of Engineering and Technology, Peshawar, Pakistan
| | - Muhammad E.H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha, 2713, Qatar,Corresponding author
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Chowdhury MEH, Rahman T, Khandakar A, Al-Madeed S, Zughaier SM, Doi SAR, Hassen H, Islam MT. An Early Warning Tool for Predicting Mortality Risk of COVID-19 Patients Using Machine Learning. Cognit Comput 2021:1-16. [PMID: 33897907 PMCID: PMC8058759 DOI: 10.1007/s12559-020-09812-7] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Accepted: 12/28/2020] [Indexed: 01/08/2023]
Abstract
COVID-19 pandemic has created an extreme pressure on the global healthcare services. Fast, reliable, and early clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. In order to study the important blood biomarkers for predicting disease mortality, a retrospective study was conducted on a dataset made public by Yan et al. in [1] of 375 COVID-19 positive patients admitted to Tongji Hospital (China) from January 10 to February 18, 2020. Demographic and clinical characteristics and patient outcomes were investigated using machine learning tools to identify key biomarkers to predict the mortality of individual patient. A nomogram was developed for predicting the mortality risk among COVID-19 patients. Lactate dehydrogenase, neutrophils (%), lymphocyte (%), high-sensitivity C-reactive protein, and age (LNLCA)-acquired at hospital admission-were identified as key predictors of death by multi-tree XGBoost model. The area under curve (AUC) of the nomogram for the derivation and validation cohort were 0.961 and 0.991, respectively. An integrated score (LNLCA) was calculated with the corresponding death probability. COVID-19 patients were divided into three subgroups: low-, moderate-, and high-risk groups using LNLCA cutoff values of 10.4 and 12.65 with the death probability less than 5%, 5-50%, and above 50%, respectively. The prognostic model, nomogram, and LNLCA score can help in early detection of high mortality risk of COVID-19 patients, which will help doctors to improve the management of patient stratification.
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Affiliation(s)
| | - Tawsifur Rahman
- Department of Biomedical Physics & Technology, University of Dhaka, 1000 Dhaka, Bangladesh
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, 2713 Doha, Qatar
| | - Somaya Al-Madeed
- Department of Computer Science and Engineering, Qatar University, 2713 Doha, Qatar
| | - Susu M. Zughaier
- Department of Basic Medical Sciences, College of Medicine, Qatar University, 2713 Doha, Qatar
| | - Suhail A. R. Doi
- Department of Population Medicine, College of Medicine, Qatar University, 2713 Doha, Qatar
| | - Hanadi Hassen
- Department of Computer Science and Engineering, Qatar University, 2713 Doha, Qatar
| | - Mohammad T. Islam
- Department of Electrical, Electronics and Systems Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia
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Early Warning Scores in Patients with Suspected COVID-19 Infection in Emergency Departments. J Pers Med 2021; 11:jpm11030170. [PMID: 33801375 PMCID: PMC8001393 DOI: 10.3390/jpm11030170] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
Early warning scores (EWSs) help prevent and recognize and thereby act as the first signs of clinical and physiological deterioration. The objective of this study is to evaluate different EWSs (National Early Warning Score 2 (NEWS2), quick sequential organ failure assessment score (qSOFA), Modified Rapid Emergency Medicine Score (MREMS) and Rapid Acute Physiology Score (RAPS)) to predict mortality within the first 48 h in patients suspected to have Coronavirus disease 2019 (COVID-19). We conducted a retrospective observational study in patients over 18 years of age who were treated by the advanced life support units and transferred to the emergency departments between March and July of 2020. Each patient was followed for two days registering their final diagnosis and mortality data. A total of 663 patients were included in our study. Early mortality within the first 48 h affected 53 patients (8.3%). The scale with the best capacity to predict early mortality was the National Early Warning Score 2 (NEWS2), with an area under the curve of 0.825 (95% CI: 0.75–0.89). The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients presented an area under the curve (AUC) of 0.804 (95% CI: 0.71–0.89), and the negative ones with an AUC of 0.863 (95% CI: 0.76–0.95). Among the EWSs, NEWS2 presented the best predictive power, even when it was separately applied to patients who tested positive and negative for SARS-CoV-2.
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Najm A, Alunno A, Mariette X, Terrier B, De Marco G, Emmel J, Mason L, McGonagle DG, Machado PM. Pathophysiology of acute respiratory syndrome coronavirus 2 infection: a systematic literature review to inform EULAR points to consider. RMD Open 2021; 7:e001549. [PMID: 33574116 PMCID: PMC7880117 DOI: 10.1136/rmdopen-2020-001549] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/08/2021] [Accepted: 01/14/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The SARS-CoV-2 pandemic is a global health problem. Beside the specific pathogenic effect of SARS-CoV-2, incompletely understood deleterious and aberrant host immune responses play critical roles in severe disease. Our objective was to summarise the available information on the pathophysiology of COVID-19. METHODS Two reviewers independently identified eligible studies according to the following PICO framework: P (population): patients with SARS-CoV-2 infection; I (intervention): any intervention/no intervention; C (comparator): any comparator; O (outcome) any clinical or serological outcome including but not limited to immune cell phenotype and function and serum cytokine concentration. RESULTS Of the 55 496 records yielded, 84 articles were eligible for inclusion according to question-specific research criteria. Proinflammatory cytokine expression, including interleukin-6 (IL-6), was increased, especially in severe COVID-19, although not as high as other states with severe systemic inflammation. The myeloid and lymphoid compartments were differentially affected by SARS-CoV-2 infection depending on disease phenotype. Failure to maintain high interferon (IFN) levels was characteristic of severe forms of COVID-19 and could be related to loss-of-function mutations in the IFN pathway and/or the presence of anti-IFN antibodies. Antibody response to SARS-CoV-2 infection showed a high variability across individuals and disease spectrum. Multiparametric algorithms showed variable diagnostic performances in predicting survival, hospitalisation, disease progression or severity, and mortality. CONCLUSIONS SARS-CoV-2 infection affects both humoral and cellular immunity depending on both disease severity and individual parameters. This systematic literature review informed the EULAR 'points to consider' on COVID-19 pathophysiology and immunomodulatory therapies.
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Affiliation(s)
- Aurélie Najm
- Institute of Infection, Immunity and Inflammation, College of Medical Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Alessia Alunno
- Department of Medicine, Rheumatology Unit, University of Perugia, Perugia, Italy
| | - Xavier Mariette
- INSERM U1184, Center for Immunology of Viral Infections and Autoimmune Diseases, Paris-Sud University, Paris-Saclay University, Le Kremlin-Bicêtre, France
- Department of Rheumatology, AP-HP, Paris-Sud University Hospitals, Le Kremlin Bicêtre Hospital, Le Kremlin-Bicêtre, France
| | - Benjamin Terrier
- University of Paris, Assistance Publique-Hôpitaux de Paris, Cochin Hospital, Paris, France
- INSERM U970, PARCC, Paris, Île-de-France, France
| | - Gabriele De Marco
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, NIHR Leeds Biomedical Research Centre, Leeds, West Yorkshire, UK
- Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, West Yorkshire, UK
| | - Jenny Emmel
- Medical Education, Library & Evidence Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Laura Mason
- Medical Education, Library & Evidence Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Dennis G McGonagle
- Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, NIHR Leeds Biomedical Research Centre, Leeds, West Yorkshire, UK
- Chapel Allerton Hospital, Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, NIHR Leeds Biomedical Research Centre, Leeds, UK
| | - Pedro M Machado
- Centre for Rheumatology, National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre (BRC), University College London Hospitals (UCLH) NHS Foundation Trus, London, UK
- Department of Rheumatology, Northwick Park Hospital, London North West University Healthcare NHS Trust, London, UK
- Centre for Rheumatology & Department of Neuromuscular Diseases, University College London, London, UK
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Rahman T, Khandakar A, Hoque ME, Ibtehaz N, Kashem SB, Masud R, Shampa L, Hasan MM, Islam MT, Al-Maadeed S, Zughaier SM, Badran S, Doi SAR, Chowdhury MEH. Development and Validation of an Early Scoring System for Prediction of Disease Severity in COVID-19 Using Complete Blood Count Parameters. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:120422-120441. [PMID: 34786318 PMCID: PMC8545188 DOI: 10.1109/access.2021.3105321] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 08/07/2021] [Indexed: 05/08/2023]
Abstract
The coronavirus disease 2019 (COVID-19) after outbreaking in Wuhan increasingly spread throughout the world. Fast, reliable, and easily accessible clinical assessment of the severity of the disease can help in allocating and prioritizing resources to reduce mortality. The objective of the study was to develop and validate an early scoring tool to stratify the risk of death using readily available complete blood count (CBC) biomarkers. A retrospective study was conducted on twenty-three CBC blood biomarkers for predicting disease mortality for 375 COVID-19 patients admitted to Tongji Hospital, China from January 10 to February 18, 2020. Machine learning based key biomarkers among the CBC parameters as the mortality predictors were identified. A multivariate logistic regression-based nomogram and a scoring system was developed to categorize the patients in three risk groups (low, moderate, and high) for predicting the mortality risk among COVID-19 patients. Lymphocyte count, neutrophils count, age, white blood cell count, monocytes (%), platelet count, red blood cell distribution width parameters collected at hospital admission were selected as important biomarkers for death prediction using random forest feature selection technique. A CBC score was devised for calculating the death probability of the patients and was used to categorize the patients into three sub-risk groups: low (<=5%), moderate (>5% and <=50%), and high (>50%), respectively. The area under the curve (AUC) of the model for the development and internal validation cohort were 0.961 and 0.88, respectively. The proposed model was further validated with an external cohort of 103 patients of Dhaka Medical College, Bangladesh, which exhibits in an AUC of 0.963. The proposed CBC parameter-based prognostic model and the associated web-application, can help the medical doctors to improve the management by early prediction of mortality risk of the COVID-19 patients in the low-resource countries.
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Affiliation(s)
- Tawsifur Rahman
- Department of Electrical EngineeringQatar University Doha Qatar
| | - Amith Khandakar
- Department of Electrical EngineeringQatar University Doha Qatar
| | - Md Enamul Hoque
- Department of Biomedical EngineeringMilitary Institute of Science and Technology Dhaka 1216 Bangladesh
| | - Nabil Ibtehaz
- Department of Computer Science and EngineeringBangladesh University of Engineering and Technology Dhaka 1205 Bangladesh
| | - Saad Bin Kashem
- Faculty of Robotics and Advanced ComputingQatar Armed Forces-Academic Bridge Program, Qatar Foundation Doha Qatar
| | - Reehum Masud
- COVID Isolation UnitUnited Hospitals, Ltd. Dhaka 1212 Bangladesh
| | - Lutfunnahar Shampa
- Department of Obstetrics and GynecologyDhaka Medical College Hospital (COVID UNIT) Dhaka 1000 Bangladesh
| | | | - Mohammad Tariqul Islam
- Department of Electrical, Electronics and Systems EngineeringUniversiti Kebangsaan Malaysia Bangi Selangor 43600 Malaysia
| | - Somaya Al-Maadeed
- Department of Computer Science and EngineeringQatar University Doha Qatar
| | - Susu M Zughaier
- Department of Basic Medical SciencesCollege of MedicineQU Health, Qatar University Doha Qatar
| | - Saif Badran
- Department of Plastic SurgeryHamad Medical Corporation Doha Qatar
- Department of Population MedicineCollege of MedicineQU Health, Qatar University Doha Qatar
| | - Suhail A R Doi
- Department of Population MedicineCollege of MedicineQU Health, Qatar University Doha Qatar
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Del Giorno R, Quarenghi M, Stefanelli K, Capelli S, Giagulli A, Quarleri L, Stehrenberger D, Ossola N, Monotti R, Gabutti L. Nutritional Risk Screening and Body Composition in COVID-19 Patients Hospitalized in an Internal Medicine Ward. Int J Gen Med 2020; 13:1643-1651. [PMID: 33380822 PMCID: PMC7767704 DOI: 10.2147/ijgm.s286484] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 11/19/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Malnutrition in patients hospitalized in internal medicine wards is highly prevalent and represents a prognostic factor of worse outcomes. Previous evidence suggested the prognostic role of the nutritional status in patients affected by the coronavirus disease 2019 (COVID-19). We aim to investigate the nutritional risk in patients with COVID-19 hospitalized in an internal medicine ward and their clinical outcomes using the Nutritional Risk Screening 2002 (NRS-2002) and parameters derived from bioelectrical impedance analysis (BIA). METHODS Retrospective analysis of patients with COVID-19 aimed at exploring: 1) the prevalence of nutritional risk with NRS-2002 and BIA; 2) the relationship between NRS-2002, BIA parameters and selected outcomes: length of hospital stay (LOS); death and need of intensive care unit (ICU); prolonged LOS; and loss of appetite. RESULTS Data of 90 patients were analyzed. Patients at nutritional risk were 92% with NRS-2002, with BIA-derived parameters: 88% by phase angle; 86% by body cell mass; 84% by fat-free mass and 84% by fat mass (p-value ≤0.001). In ROC analysis, NRS had the maximum sensitivity in predicting the risk of death and need of ICU and a prolonged hospitalization showing moderate-low specificity; phase angle showed a good predictive power in terms of AUC. NRS-2002 was significantly associated with LOS (β 12.62, SE 5.79). In a multivariate analysis, blood glucose level and the early warning score are independent predictors of death and need of ICU (OR 2.79, p ≤0.001; 1.59, p-0.029, respectively). CONCLUSION Present findings confirm the clinical utility of NRS-2002 to assess nutritional risk in patients with COVID-19 at hospital admission and in predicting LOS, and that bioimpedance does not seem to add further predictive value. An early detection of nutritional risk has to be systematically included in the management of COVID-19 patients hospitalized in internal medicine wards.
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Affiliation(s)
- Rosaria Del Giorno
- Department of Internal Medicine, Clinical Research Unit, Regional Hospital of Bellinzona and Valli, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Institute of Biomedicine, University of Southern Switzerland, Lugano, Switzerland
| | - Massimo Quarenghi
- Section of Clinical Nutrition and Dietetics, Department of Internal Medicine, Ente Ospedaliero Cantonale, Locarno, Switzerland
| | - Kevyn Stefanelli
- Department of Social Sciences and Economics, Sapienza University of Rome, Rome, Italy
| | - Silvia Capelli
- Section of Clinical Nutrition and Dietetics, Department of Internal Medicine, Ente Ospedaliero Cantonale, Locarno, Switzerland
| | - Antonella Giagulli
- Section of Clinical Nutrition and Dietetics, Department of Internal Medicine, Ente Ospedaliero Cantonale, Locarno, Switzerland
| | - Lara Quarleri
- Section of Clinical Nutrition and Dietetics, Department of Internal Medicine, Ente Ospedaliero Cantonale, Locarno, Switzerland
| | - Daniela Stehrenberger
- Section of Clinical Nutrition and Dietetics, Department of Internal Medicine, Ente Ospedaliero Cantonale, Locarno, Switzerland
| | - Nicola Ossola
- Section of Clinical Nutrition and Dietetics, Department of Internal Medicine, Ente Ospedaliero Cantonale, Mendrisio, Switzerland
| | - Rita Monotti
- Department of Internal Medicine, Ospedale La Carità, Locarno, Switzerland
| | - Luca Gabutti
- Department of Internal Medicine, Clinical Research Unit, Regional Hospital of Bellinzona and Valli, Ente Ospedaliero Cantonale, Bellinzona, Switzerland
- Institute of Biomedicine, University of Southern Switzerland, Lugano, Switzerland
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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: 1647] [Impact Index Per Article: 411.8] [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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