Predictors of adverse prognosis in COVID‐19: A systematic review and meta‐analysis
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- Stefano Figliozzi
- School of Biomedical Engineering & Imaging Sciences King's College London London UK
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- Pier Giorgio Masci
- School of Biomedical Engineering & Imaging Sciences King's College London London UK
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- Navid Ahmadi
- Department of Cardiology‐Intensive Therapy Poznan University of Medical Sciences Poznan Poland
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- Lara Tondi
- Department of Multimodality Cardiovascular Imaging IRCCS Policlinico San Donato San Donato Milanese Italy
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- Evangelia Koutli
- Institute for Liver and Digestive Health Royal Free Hospital & UCL University College London London UK
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- Alberto Aimo
- Institute of Life Sciences Scuola Superiore Sant’Anna Pisa Italy
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- Kimon Stamatelopoulos
- Department of Clinical Therapeutics National and Kapodistrian University of Athens School of Medicine Athen Greece
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- Meletios‐Athanasios Dimopoulos
- Department of Clinical Therapeutics National and Kapodistrian University of Athens School of Medicine Athen Greece
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- Alida L. P. Caforio
- Department of Cardiac Thoracic Vascular Sciences and Public Health University of Padua Medical School Padova Italy
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- Georgios Georgiopoulos
- School of Biomedical Engineering & Imaging Sciences King's College London London UK
抄録
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Identification of reliable outcome predictors in coronavirus disease 2019 (COVID‐19) is of paramount importance for improving patient's management.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>A systematic review of literature was conducted until 24 April 2020. From 6843 articles, 49 studies were selected for a pooled assessment; cumulative statistics for age and sex were retrieved in 587 790 and 602 234 cases. Two endpoints were defined: (a) a composite outcome including death, severe presentation, hospitalization in the intensive care unit (ICU) and/or mechanical ventilation; and (b) in‐hospital mortality. We extracted numeric data on patients’ characteristics and cases with adverse outcomes and employed inverse variance random‐effects models to derive pooled estimates.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We identified 18 and 12 factors associated with the composite endpoint and death, respectively. Among those, a history of CVD (odds ratio (OR) = 3.15, 95% confidence intervals (CIs) 2.26‐4.41), acute cardiac (OR = 10.58, 5.00‐22.40) or kidney (OR = 5.13, 1.78‐14.83) injury, increased procalcitonin (OR = 4.8, 2.034‐11.31) or D‐dimer (OR = 3.7, 1.74‐7.89), and thrombocytopenia (OR = 6.23, 1.031‐37.67) conveyed the highest odds for the adverse composite endpoint. Advanced age, male sex, cardiovascular comorbidities, acute cardiac or kidney injury, lymphocytopenia and D‐dimer conferred an increased risk of in‐hospital death. With respect to the treatment of the acute phase, therapy with steroids was associated with the adverse composite endpoint (OR = 3.61, 95% CI 1.934‐6.73), but not with mortality.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Advanced age, comorbidities, abnormal inflammatory and organ injury circulating biomarkers captured patients with an adverse clinical outcome. Clinical history and laboratory profile may then help identify patients with a higher risk of in‐hospital mortality.</jats:p></jats:sec>
収録刊行物
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- European Journal of Clinical Investigation
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European Journal of Clinical Investigation 50 (10), 2020-08-27
Wiley