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Portuondo-Jiménez J, Barrio I, España PP, García J, Villanueva A, Gascón M, Rodríguez L, Larrea N, García-Gutierrez S, Quintana JM. Clinical prediction rules for adverse evolution in patients with COVID-19 by the Omicron variant. Int J Med Inform 2023; 173:105039. [PMID: 36921481 PMCID: PMC9988314 DOI: 10.1016/j.ijmedinf.2023.105039] [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: 11/17/2022] [Revised: 02/03/2023] [Accepted: 03/01/2023] [Indexed: 03/08/2023]
Abstract
OBJECTIVE We identify factors related to SARS-CoV-2 infection linked to hospitalization, ICU admission, and mortality and develop clinical prediction rules. METHODS Retrospective cohort study of 380,081 patients with SARS-CoV-2 infection from March 1, 2020 to January 9, 2022, including a subsample of 46,402 patients who attended Emergency Departments (EDs) having data on vital signs. For derivation and external validation of the prediction rule, two different periods were considered: before and after emergence of the Omicron variant, respectively. Data collected included sociodemographic data, COVID-19 vaccination status, baseline comorbidities and treatments, other background data and vital signs at triage at EDs. The predictive models for the EDs and the whole samples were developed using multivariate logistic regression models using Lasso penalization. RESULTS In the multivariable models, common predictive factors of death among EDs patients were greater age; being male; having no vaccination, dementia; heart failure; liver and kidney disease; hemiplegia or paraplegia; coagulopathy; interstitial pulmonary disease; malignant tumors; use chronic systemic use of steroids, higher temperature, low O2 saturation and altered blood pressure-heart rate. The predictors of an adverse evolution were the same, with the exception of liver disease and the inclusion of cystic fibrosis. Similar predictors were found to be related to hospital admission, including liver disease, arterial hypertension, and basal prescription of immunosuppressants. Similarly, models for the whole sample, without vital signs, are presented. CONCLUSIONS We propose risk scales, based on basic information, easily-calculable, high-predictive that also function with the current Omicron variant and may help manage such patients in primary, emergency, and hospital care.
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Affiliation(s)
- Janire Portuondo-Jiménez
- Osakidetza Basque Health Service, Sub-Directorate for Primary Care Coordination, Vitoria-Gasteiz, Spain; Biocruces Bizkaia Health Research Institute, Barakaldo, Spain; Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), Spain
| | - Irantzu Barrio
- University of the Basque Country UPV/EHU, Department of Mathematics, Leioa, Spain; Basque Center for Applied Mathematics, BCAM, Spain.
| | - Pedro P España
- Biocruces Bizkaia Health Research Institute, Barakaldo, Spain; Osakidetza Basque Health Service, Galdakao-Usansolo University Hospital, Respiratory Unit, Galdakao, Spain
| | - Julia García
- Basque Government Department of Health, Office of Healthcare Planning, Organization and Evaluation, Basque Country, Spain
| | - Ane Villanueva
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), Spain; Osakidetza Basque Health Service, Galdakao-Usansolo University Hospital, Research Unit, Galdakao, Spain; Health Service Research Network on Chronic Diseases (REDISSEC), Bilbao, Spain; Kronikgune Institute for Health Services Research, Barakaldo, Spain
| | - María Gascón
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), Spain; Osakidetza Basque Health Service, Galdakao-Usansolo University Hospital, Research Unit, Galdakao, Spain; Health Service Research Network on Chronic Diseases (REDISSEC), Bilbao, Spain; Kronikgune Institute for Health Services Research, Barakaldo, Spain
| | | | - Nere Larrea
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), Spain; Osakidetza Basque Health Service, Galdakao-Usansolo University Hospital, Research Unit, Galdakao, Spain; Health Service Research Network on Chronic Diseases (REDISSEC), Bilbao, Spain; Kronikgune Institute for Health Services Research, Barakaldo, Spain
| | - Susana García-Gutierrez
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), Spain; Osakidetza Basque Health Service, Galdakao-Usansolo University Hospital, Research Unit, Galdakao, Spain; Health Service Research Network on Chronic Diseases (REDISSEC), Bilbao, Spain; Kronikgune Institute for Health Services Research, Barakaldo, Spain
| | - José M Quintana
- Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS), Spain; Osakidetza Basque Health Service, Galdakao-Usansolo University Hospital, Research Unit, Galdakao, Spain; Health Service Research Network on Chronic Diseases (REDISSEC), Bilbao, Spain; Kronikgune Institute for Health Services Research, Barakaldo, Spain
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2
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Portuondo-Jimenez J, Bilbao-González A, Tíscar-González V, Garitano-Gutiérrez I, García-Gutiérrez S, Martínez-Mejuto A, Santiago-Garin J, Arribas-García S, García-Asensio J, Chart-Pascual J, Zorrilla-Martínez I, Quintana-Lopez JM. Modelling the risk of hospital admission of lab confirmed SARS-CoV-2-infected patients in primary care: a population-based study. Intern Emerg Med 2022; 17:1211-1221. [PMID: 35143022 PMCID: PMC8831017 DOI: 10.1007/s11739-022-02931-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 05/17/2021] [Accepted: 01/13/2022] [Indexed: 01/08/2023]
Abstract
The objectives of this study are to develop a predictive model of hospital admission for COVID-19 to help in the activation of emergency services, early referrals from primary care, and the improvement of clinical decision-making in emergency room services. The method is the retrospective cohort study of 49,750 patients with microbiological confirmation of SARS-CoV-2 infection. The sample was randomly divided into two subsamples, for the purposes of derivation and validation of the prediction rule (60% and 40%, respectively). Data collected for this study included sociodemographic data, baseline comorbidities, baseline treatments, and other background data. Multilevel analyses with generalized estimated equations were used to develop the predictive model. Male sex and the gradual effect of age were the main risk factors for hospital admission. Regarding baseline comorbidities, coagulopathies, cancer, cardiovascular diseases, diabetes with organ damage, and liver disease were among the five most notable. Flu vaccination was a risk factor for hospital admission. Drugs that increased risk were chronic systemic steroids, immunosuppressants, angiotensin-converting enzyme inhibitors, and NSAIDs. The AUC of the risk score was 0.821 and 0.828 in the derivation and validation samples, respectively. Based on the risk score, five risk groups were derived with hospital admission ranging from 2.94 to 51.87%. In conclusion, we propose a classification system for people with COVID-19 with a higher risk of hospitalization, and indirectly with it a greater severity of the disease, easy to be completed both in primary care, as well as in emergency services and in hospital emergency room to help in clinical decision-making.Registration: ClinicalTrials.gov Identifier: NCT04463706.
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Affiliation(s)
- Janire Portuondo-Jimenez
- Basque Government Department of Health, Office of Healthcare Planning, Organisation and Evaluation, Basque Country, Spain
- Osakidetza Basque Health Service, Sub-Directorate for Primary Care Coordination, Vitoria-Gasteiz, Spain
- Biocruces Bizkaia Health Research Institute, Clinical Nursing and Community Health Group, Barakaldo, Spain
| | - Amaia Bilbao-González
- Osakidetza Basque Health Service, Basurto University Hospital, Research Unit, Bilbao, Spain
- Health Service Research Network on Chronic Diseases (REDISSEC), Bilbao, Spain
- Kronikgune Institute for Health Services Research, Barakaldo, Spain
| | - Verónica Tíscar-González
- Biocruces Bizkaia Health Research Institute, Clinical Nursing and Community Health Group, Barakaldo, Spain
- Osakidetza Basque Health Service, Basurto University Hospital, Research Unit, Bilbao, Spain
- Osakidetza Basque Health Service, University School of Nursing, University of the Basque Country, Vitoria-Gasteiz, Spain
| | - Ignacio Garitano-Gutiérrez
- Bioaraba Health Research Institute, Clinical Nursing and Community Health Group, Vitoria-Gasteiz, Spain
- Osakidetza Basque Health Service, Araba University Hospital, Vitoria-Gasteiz, Spain
| | - Susana García-Gutiérrez
- Health Service Research Network on Chronic Diseases (REDISSEC), Bilbao, Spain
- Kronikgune Institute for Health Services Research, Barakaldo, Spain
- Osakidetza Basque Health Service, Galdakao-Usansolo University Hospital, Research Unit, Galdakao, Spain
| | - Almudena Martínez-Mejuto
- Biocruces Bizkaia Health Research Institute, Clinical Nursing and Community Health Group, Barakaldo, Spain
- Osakidetza Basque Health Service, Cruces University Hospital, Barakaldo, Spain
| | - Jaione Santiago-Garin
- Bioaraba Health Research Institute, Clinical Nursing and Community Health Group, Vitoria-Gasteiz, Spain
- Osakidetza Basque Health Service, University School of Nursing, University of the Basque Country, Vitoria-Gasteiz, Spain
| | - Silvia Arribas-García
- Bioaraba Health Research Institute, Clinical Nursing and Community Health Group, Vitoria-Gasteiz, Spain
- Osakidetza Basque Health Service, University School of Nursing, University of the Basque Country, Vitoria-Gasteiz, Spain
| | - Julia García-Asensio
- Basque Government Department of Health, Office of Healthcare Planning, Organisation and Evaluation, Basque Country, Spain
| | - Johnny Chart-Pascual
- Osakidetza Basque Health Service, Araba IHO, Psychiatry Service, Vitoria-Gasteiz, Spain
| | - Iñaki Zorrilla-Martínez
- Bioaraba Health Research Institute, Clinical trials, Vitoria-Gasteiz, Spain.
- Osakidetza Basque Health Service, Araba IHO, Psychiatry Service, Vitoria-Gasteiz, Spain.
- Faculty of Medicine, University of the Basque Country, Vitoria-Gasteiz, Spain.
| | - Jose Maria Quintana-Lopez
- Health Service Research Network on Chronic Diseases (REDISSEC), Bilbao, Spain
- Kronikgune Institute for Health Services Research, Barakaldo, Spain
- Osakidetza Basque Health Service, Galdakao-Usansolo University Hospital, Research Unit, Galdakao, Spain
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Nuevo-Ortega P, Reina-Artacho C, Dominguez-Moreno F, Becerra-Muñoz VM, Ruiz-Del-Fresno L, Estecha-Foncea MA. Prognosis of COVID-19 pneumonia can be early predicted combining Age-adjusted Charlson Comorbidity Index, CRB score and baseline oxygen saturation. Sci Rep 2022; 12:2367. [PMID: 35149742 PMCID: PMC8837655 DOI: 10.1038/s41598-022-06199-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 12/30/2021] [Indexed: 12/21/2022] Open
Abstract
In potentially severe diseases in general and COVID-19 in particular, it is vital to early identify those patients who are going to progress to severe disease. A recent living systematic review dedicated to predictive models in COVID-19, critically appraises 145 models, 8 of them focused on prediction of severe disease and 23 on mortality. Unfortunately, in all 145 models, they found a risk of bias significant enough to finally "not recommend any for clinical use". Authors suggest concentrating on avoiding biases in sampling and prioritising the study of already identified predictive factors, rather than the identification of new ones that are often dependent on the database. Our objective is to develop a model to predict which patients with COVID-19 pneumonia are at high risk of developing severe illness or dying, using basic and validated clinical tools. We studied a prospective cohort of consecutive patients admitted in a teaching hospital during the "first wave" of the COVID-19 pandemic. Follow-up to discharge from hospital. Multiple logistic regression selecting variables according to clinical and statistical criteria. 404 consecutive patients were evaluated, 392 (97%) completed follow-up. Mean age was 61 years; 59% were men. The median burden of comorbidity was 2 points in the Age-adjusted Charlson Comorbidity Index, CRB was abnormal in 18% of patients and basal oxygen saturation on admission lower than 90% in 18%. A model composed of Age-adjusted Charlson Comorbidity Index, CRB score and basal oxygen saturation can predict unfavorable evolution or death with an area under the ROC curve of 0.85 (95% CI 0.80-0.89), and 0.90 (95% CI 0.86 to 0.94), respectively. Prognosis of COVID-19 pneumonia can be predicted without laboratory tests using two classic clinical tools and a pocket pulse oximeter.
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Affiliation(s)
- Pilar Nuevo-Ortega
- Intensive Care Unit, Hospital Universitario Virgen de la Victoria, Málaga, Spain.
- Instituto de Investigación Biomédica de Málaga, Málaga, Spain.
| | - Carmen Reina-Artacho
- Intensive Care Unit, Hospital Universitario Virgen de la Victoria, Málaga, Spain
- Instituto de Investigación Biomédica de Málaga, Málaga, Spain
| | - Francisco Dominguez-Moreno
- Intensive Care Unit, Hospital Universitario Virgen de la Victoria, Málaga, Spain
- Instituto de Investigación Biomédica de Málaga, Málaga, Spain
| | - Victor Manuel Becerra-Muñoz
- Intensive Care Unit, Hospital Universitario Virgen de la Victoria, Málaga, Spain
- Instituto de Salud Carlos III, Madrid, Spain
| | - Luis Ruiz-Del-Fresno
- Intensive Care Unit, Hospital Universitario Virgen de la Victoria, Málaga, Spain
- Instituto de Investigación Biomédica de Málaga, Málaga, Spain
| | - Maria Antonia Estecha-Foncea
- Intensive Care Unit, Hospital Universitario Virgen de la Victoria, Málaga, Spain
- Instituto de Investigación Biomédica de Málaga, Málaga, Spain
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Effects of hydroxychloroquine and its metabolites in patients with connective tissue diseases. Inflammopharmacology 2021; 29:1795-1805. [PMID: 34743268 PMCID: PMC8572531 DOI: 10.1007/s10787-021-00887-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 10/24/2021] [Indexed: 11/24/2022]
Abstract
Hydroxychloroquine has attracted attention in the treatment of COVID-19. Many conflicting findings have been reported regarding the efficacy and safety of this drug, which has been used safely in the rheumatological diseases for years. However, these studies lacked measurement methods that allow accurate assessment of hydroxychloroquine and its metabolite levels. The aim of this study was to measure hydroxychloroquine and its metabolite levels in whole blood samples of patients with rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), Sjogren’s syndrome (SS) and scleroderma (Scl) by a robust, simple and accurate validated tandem mass spectrometric method, and to investigate the relationship between these levels with drug-related adverse effects and disease activity scores. The validated LC–MS/MS method was applied to measure blood hydroxychloroquine and its metabolite levels of patients with RA, SLE, SS, Scl. Various haematological and biochemical parameters were measured with Beckman-Coulter AU 5800 and Beckman Coulter LH 780 analyzers, respectively. QTc intervals were calculated with Bazett’s formula, and the patients were followed up by clinicians in terms of clinical findings and adverse effects. Hydroxychloroquine levels of patients were similar to previous studies. There was a negative correlation between disease activity scores and hydroxychloroquine levels, while the highest correlation was between QTc interval, creatinine and GFR levels with desethylchloroquine. Bidetylchloroquine had the highest correlation with RBC count and liver function tests. Our findings showed that hydroxychloroquine and its metabolite levels were associated with disease activity scores, renal, hepatic function, QTc prolongation, and hematological parameters.
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5
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Huang HF, Liu Y, Li JX, Dong H, Gao S, Huang ZY, Fu SZ, Yang LY, Lu HZ, Xia LY, Cao S, Gao Y, Yu XX. Validated tool for early prediction of intensive care unit admission in COVID-19 patients. World J Clin Cases 2021; 9:8388-8403. [PMID: 34754848 PMCID: PMC8554435 DOI: 10.12998/wjcc.v9.i28.8388] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/12/2021] [Accepted: 08/16/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The novel coronavirus disease 2019 (COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2. AIM To develop and validate a risk stratification tool for the early prediction of intensive care unit (ICU) admission among COVID-19 patients at hospital admission. METHODS The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital. We selected 13 of 65 baseline laboratory results to assess ICU admission risk, which were used to develop a risk prediction model with the random forest (RF) algorithm. A nomogram for the logistic regression model was built based on six selected variables. The predicted models were carefully calibrated, and the predictive performance was evaluated and compared with two previously published models. RESULTS There were 681 and 296 patients in the training and validation cohorts, respectively. The patients in the training cohort were older than those in the validation cohort (median age: 63.0 vs 49.0 years, P < 0.001), and the percentages of male gender were similar (49.6% vs 49.3%, P = 0.958). The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio, age, lactate dehydrogenase, C-reactive protein, creatinine, D-dimer, albumin, procalcitonin, glucose, platelet, total bilirubin, lactate and creatine kinase. The accuracy, sensitivity and specificity for the RF model were 91%, 88% and 93%, respectively, higher than those for the logistic regression model. The area under the receiver operating characteristic curve of our model was much better than those of two other published methods (0.90 vs 0.82 and 0.75). Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%, whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata. Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A. CONCLUSION Our model can identify ICU admission risk in COVID-19 patients at admission, who can then receive prompt care, thus improving medical resource allocation.
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Affiliation(s)
- Hao-Fan Huang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China
| | - Yong Liu
- Expert Panel of Shenzhen 2019-nCoV Pneumonia, Shenzhen Hospital, Southern Medical University, Shenzhen 518000, Guangdong Province, China
| | - Jin-Xiu Li
- Department of Critical Care Medicine, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518112, Guangdong Province, China
| | - Hui Dong
- Department of ICU/Emergency, Wuhan Third Hospital, Wuhan University, Wuhan 430000, Hubei Province, China
| | - Shan Gao
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China
| | - Zheng-Yang Huang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China
| | - Shou-Zhi Fu
- Department of ICU/Emergency, Wuhan Third Hospital, Wuhan University, Wuhan 430000, Hubei Province, China
| | - Lu-Yu Yang
- Department of ICU/Emergency, Wuhan Third Hospital, Wuhan University, Wuhan 430000, Hubei Province, China
| | - Hui-Zhi Lu
- Department of ICU/Emergency, Wuhan Third Hospital, Wuhan University, Wuhan 430000, Hubei Province, China
| | - Liao-You Xia
- Department of ICU/Emergency, Wuhan Third Hospital, Wuhan University, Wuhan 430000, Hubei Province, China
| | - Song Cao
- Department of ICU/Emergency, Wuhan Third Hospital, Wuhan University, Wuhan 430000, Hubei Province, China
| | - Yi Gao
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China
| | - Xia-Xia Yu
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China
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Leslie D, Mazumder A, Peppin A, Wolters MK, Hagerty A. Does "AI" stand for augmenting inequality in the era of covid-19 healthcare? BMJ 2021; 372:n304. [PMID: 33722847 PMCID: PMC7958301 DOI: 10.1136/bmj.n304] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
| | | | | | | | - Alexa Hagerty
- Centre for the Study of Existential Risk and Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK
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Abstract
Daniel E Ho and colleagues explore the legal implications of using artificial intelligence in the response to covid-19 and call for more robust evaluation frameworks
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Affiliation(s)
- Mark Krass
- Stanford Law School, Stanford University, Stanford, CA, USA
- Department of Political Science, Stanford University School of Humanities and Sciences, Stanford, CA, USA
| | - Peter Henderson
- Stanford Law School, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University School of Engineering, Stanford, CA, USA
| | - Michelle M Mello
- Stanford Law School, Stanford University, Stanford, CA, USA
- Stanford Health Policy and Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - David M Studdert
- Stanford Law School, Stanford University, Stanford, CA, USA
- Stanford Health Policy and Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniel E Ho
- Stanford Law School, Stanford University, Stanford, CA, USA
- Department of Political Science, Stanford University School of Humanities and Sciences, Stanford, CA, USA
- Stanford Institute for Human-Centered Artificial Intelligence, Stanford, CA, USA
- Stanford Institute for Economic Policy Research, Stanford, CA, USA
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Development of a prognostic model for mortality in COVID-19 infection using machine learning. Mod Pathol 2021; 34:522-531. [PMID: 33067522 PMCID: PMC7567420 DOI: 10.1038/s41379-020-00700-x] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Revised: 09/28/2020] [Accepted: 09/28/2020] [Indexed: 02/04/2023]
Abstract
Coronavirus disease 2019 (COVID-19) is a novel disease resulting from infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has quickly risen since the beginning of 2020 to become a global pandemic. As a result of the rapid growth of COVID-19, hospitals are tasked with managing an increasing volume of these cases with neither a known effective therapy, an existing vaccine, nor well-established guidelines for clinical management. The need for actionable knowledge amidst the COVID-19 pandemic is dire and yet, given the urgency of this illness and the speed with which the healthcare workforce must devise useful policies for its management, there is insufficient time to await the conclusions of detailed, controlled, prospective clinical research. Thus, we present a retrospective study evaluating laboratory data and mortality from patients with positive RT-PCR assay results for SARS-CoV-2. The objective of this study is to identify prognostic serum biomarkers in patients at greatest risk of mortality. To this end, we develop a machine learning model using five serum chemistry laboratory parameters (c-reactive protein, blood urea nitrogen, serum calcium, serum albumin, and lactic acid) from 398 patients (43 expired and 355 non-expired) for the prediction of death up to 48 h prior to patient expiration. The resulting support vector machine model achieved 91% sensitivity and 91% specificity (AUC 0.93) for predicting patient expiration status on held-out testing data. Finally, we examine the impact of each feature and feature combination in light of different model predictions, highlighting important patterns of laboratory values that impact outcomes in SARS-CoV-2 infection.
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9
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Sang S, Sun R, Coquet J, Carmichael H, Seto T, Hernandez-Boussard T. Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study. J Med Internet Res 2021; 23:e23026. [PMID: 33534724 PMCID: PMC7901593 DOI: 10.2196/23026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 10/09/2020] [Accepted: 02/01/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. OBJECTIVE This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. METHODS Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. CONCLUSIONS We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.
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Affiliation(s)
- Shengtian Sang
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
| | - Ran Sun
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
| | - Jean Coquet
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
| | | | - Tina Seto
- Technology and Digital Solutions, Stanford University, Stanford, CA, United States
| | - Tina Hernandez-Boussard
- Department of Medicine, Biomedical Informatics, Stanford University, Stanford, CA, United States
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McRae MP, Dapkins IP, Sharif I, Anderman J, Fenyo D, Sinokrot O, Kang SK, Christodoulides NJ, Vurmaz D, Simmons GW, Alcorn TM, Daoura MJ, Gisburne S, Zar D, McDevitt JT. Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation. J Med Internet Res 2020; 22:e22033. [PMID: 32750010 PMCID: PMC7446714 DOI: 10.2196/22033] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/22/2020] [Accepted: 07/23/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The coronavirus disease (COVID-19) pandemic has resulted in significant morbidity and mortality; large numbers of patients require intensive care, which is placing strain on health care systems worldwide. There is an urgent need for a COVID-19 disease severity assessment that can assist in patient triage and resource allocation for patients at risk for severe disease. OBJECTIVE The goal of this study was to develop, validate, and scale a clinical decision support system and mobile app to assist in COVID-19 severity assessment, management, and care. METHODS Model training data from 701 patients with COVID-19 were collected across practices within the Family Health Centers network at New York University Langone Health. A two-tiered model was developed. Tier 1 uses easily available, nonlaboratory data to help determine whether biomarker-based testing and/or hospitalization is necessary. Tier 2 predicts the probability of mortality using biomarker measurements (C-reactive protein, procalcitonin, D-dimer) and age. Both the Tier 1 and Tier 2 models were validated using two external datasets from hospitals in Wuhan, China, comprising 160 and 375 patients, respectively. RESULTS All biomarkers were measured at significantly higher levels in patients who died vs those who were not hospitalized or discharged (P<.001). The Tier 1 and Tier 2 internal validations had areas under the curve (AUCs) of 0.79 (95% CI 0.74-0.84) and 0.95 (95% CI 0.92-0.98), respectively. The Tier 1 and Tier 2 external validations had AUCs of 0.79 (95% CI 0.74-0.84) and 0.97 (95% CI 0.95-0.99), respectively. CONCLUSIONS Our results demonstrate the validity of the clinical decision support system and mobile app, which are now ready to assist health care providers in making evidence-based decisions when managing COVID-19 patient care. The deployment of these new capabilities has potential for immediate impact in community clinics and sites, where application of these tools could lead to improvements in patient outcomes and cost containment.
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Affiliation(s)
- Michael P McRae
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, United States
| | - Isaac P Dapkins
- Department of Population Health and Internal Medicine, Family Health Centers at NYU Langone, New York University School of Medicine, New York, NY, United States
| | - Iman Sharif
- Departments of Pediatrics and Population Health, Family Health Centers at NYU Langone, New York University School of Medicine, New York, NY, United States
| | - Judd Anderman
- Family Health Centers at NYU Langone, New York, NY, United States
| | - David Fenyo
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY, United States
| | - Odai Sinokrot
- Department of Medicine, New York University School of Medicine, New York, NY, United States
| | - Stella K Kang
- Department of Radiology, New York University School of Medicine, New York, NY, United States
- Department of Population Health, New York University School of Medicine, New York, NY, United States
| | - Nicolaos J Christodoulides
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, United States
| | - Deniz Vurmaz
- Department of Chemical and Biomolecular Engineering, NYU Tandon School of Engineering, New York University, New York, NY, United States
| | - Glennon W Simmons
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, United States
| | | | | | | | | | - John T McDevitt
- Department of Biomaterials, Bioengineering Institute, New York University College of Dentistry, New York, NY, United States
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11
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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: 1661] [Impact Index Per Article: 415.3] [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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12
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Das S, Bhowmick S, Tiwari S, Sen S. An Updated Systematic Review of the Therapeutic Role of Hydroxychloroquine in Coronavirus Disease-19 (COVID-19). Clin Drug Investig 2020; 40:591-601. [PMID: 32468425 PMCID: PMC7255448 DOI: 10.1007/s40261-020-00927-1] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND OBJECTIVE The world is currently experiencing the Coronavirus Disease-19 (COVID-19) pandemic. There is no approved drug for the definitive treatment of the disease. Various drugs are being tried for the treatment of COVID-19, including hydroxychloroquine (HCQ). This study was performed to systematically review the therapeutic role of HCQ in COVID-19 from the available literature. METHODS PubMed, Embase, ClinicalTrials.gov, ICTRP (WHO), Cochrane Library databases, and two pre-print servers (medRxiv.org and Research Square) were searched for clinical studies that evaluated the therapeutic role of HCQ on COVID-19 until 10 May 2020. The available studies were critically analyzed and the data were extracted. RESULTS A total of 663 articles were screened and 12 clinical studies (seven peer-reviewed and published studies and five non-peer-reviewed studies from pre-print servers) with a total sample size of 3543 patients were included. Some of the clinical studies demonstrated good virological and clinical outcomes with HCQ alone or in combination with azithromycin in COVID-19 patients, although the studies had major methodological limitations. Some of the other studies showed negative results with HCQ therapy along with the risk of adverse reactions. CONCLUSION The results of efficacy and safety of HCQ in COVID-19, as obtained from the clinical studies, are not satisfactory, although many of these studies had major methodological limitations. Stronger evidence from well-designed robust randomized clinical trials is required before conclusively determining the role of HCQ in the treatment of COVID-19. Clinical prudence is required in advocating HCQ as a therapeutic armamentarium in COVID-19.
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Affiliation(s)
- Saibal Das
- grid.414953.e0000000417678301Department of Clinical Pharmacology, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, 605 006 India
| | - Subhrojyoti Bhowmick
- grid.415509.c0000 0004 1763 8190Department of Pharmacology, KPC Medical College and Hospital, Kolkata, 700 032 India
| | - Sayali Tiwari
- grid.415652.10000 0004 1767 1265Department of Community Medicine, Lokmanya Tilak Municipal Medical College and General Hospital, Mumbai, 400 022 India
| | - Sukanta Sen
- grid.464903.cDepartment of Pharmacology, ICARE Institute of Medical Sciences and Research, Haldia, 721 645 India
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