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      Predictors of in-hospital COVID-19 mortality: A comprehensive systematic review and meta-analysis exploring differences by age, sex and health conditions

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          Abstract

          Objective

          Risk factors for in-hospital mortality in confirmed COVID-19 patients have been summarized in numerous meta-analyses, but it is still unclear whether they vary according to the age, sex and health conditions of the studied populations. This study explored these variables as potential mortality predictors.

          Methods

          A systematic review was conducted by searching the MEDLINE, Scopus, and Web of Science databases of studies available through July 27, 2020. The pooled risk was estimated with the odds ratio (p-OR) or effect size (p-ES) obtained through random-effects meta-analyses. Subgroup analyses and meta-regression were applied to explore differences by age, sex and health conditions. The MOOSE guidelines were strictly followed.

          Results

          The meta-analysis included 60 studies, with a total of 51,225 patients (12,458 [24.3%] deaths) from hospitals in 13 countries. A higher in-hospital mortality risk was found for dyspnoea (p-OR = 2.5), smoking (p-OR = 1.6) and several comorbidities (p-OR range: 1.8 to 4.7) and laboratory parameters (p-ES range: 0.3 to -2.6). Age was the main source of heterogeneity, followed by sex and health condition. The following predictors were more markedly associated with mortality in studies with patients with a mean age ≤60 years: dyspnoea (p-OR = 4.3), smoking (p-OR = 2.8), kidney disease (p-OR = 3.8), hypertension (p-OR = 3.7), malignancy (p-OR = 3.7), diabetes (p-OR = 3.2), pulmonary disease (p-OR = 3.1), decreased platelet count (p-ES = -1.7), decreased haemoglobin concentration (p-ES = -0.6), increased creatinine (p-ES = 2.4), increased interleukin-6 (p-ES = 2.4) and increased cardiac troponin I (p-ES = 0.7). On the other hand, in addition to comorbidities, the most important mortality predictors in studies with older patients were albumin (p-ES = -3.1), total bilirubin (p-ES = 0.7), AST (p-ES = 1.8), ALT (p-ES = 0.4), urea nitrogen (p-ES), C-reactive protein (p-ES = 2.7), LDH (p-ES = 2.4) and ferritin (p-ES = 1.7). Obesity was associated with increased mortality only in studies with fewer chronic or critical patients (p-OR = 1.8).

          Conclusion

          The prognostic effect of clinical conditions on COVID-19 mortality vary substantially according to the mean age of patients.

          PROSPERO registration number

          CRD42020176595.

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          Most cited references130

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          Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

          Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
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            Measuring inconsistency in meta-analyses.

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              Bias in meta-analysis detected by a simple, graphical test

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                Author and article information

                Contributors
                Role: ConceptualizationRole: InvestigationRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Supervision
                Role: Data curationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: VisualizationRole: Writing – review & editing
                Role: Data curationRole: Methodology
                Role: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                3 November 2020
                2020
                3 November 2020
                : 15
                : 11
                : e0241742
                Affiliations
                [1 ] Health and Social Research Centre, Universidad de Castilla-La Mancha, Cuenca, Spain
                [2 ] Postgraduate Program in Public Health, Universidade Estadual de Londrina, Londrina, Paraná, Brasil
                [3 ] Universidad Politécnica y Artística del Paraguay, Asunción, Paraguay
                [4 ] Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Talca, Chile
                Institute of Tropical Medicine Antwerp, BELGIUM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist

                Author information
                https://orcid.org/0000-0003-2617-0430
                https://orcid.org/0000-0001-6121-7893
                Article
                PONE-D-20-14326
                10.1371/journal.pone.0241742
                7608886
                33141836
                aa0dca6f-2980-4b55-993a-a796f0997a8c
                © 2020 Mesas et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 May 2020
                : 20 October 2020
                Page count
                Figures: 1, Tables: 5, Pages: 23
                Funding
                This study was funded by FEDER funds.
                Categories
                Research Article
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Metaanalysis
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Metaanalysis
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Cancer Risk Factors
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                Oncology
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                Medical Conditions
                Respiratory Disorders
                Dyspnea
                Medicine and Health Sciences
                Pulmonology
                Respiratory Disorders
                Dyspnea
                Biology and Life Sciences
                Biochemistry
                Proteins
                C-Reactive Proteins
                Medicine and Health Sciences
                Diagnostic Medicine
                Prognosis
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                Anatomy
                Renal System
                Kidneys
                Medicine and Health Sciences
                Anatomy
                Renal System
                Kidneys
                Physical Sciences
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                Urea
                Physical Sciences
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                Organic Chemistry
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                All relevant data are within the manuscript and its Supporting Information files.
                COVID-19

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