Predictors of adverse prognosis in COVID‐19: A systematic review and meta‐analysis
Background Identification of reliable outcome predictors in coronavirus disease 2019 (COVID‐19) is of paramount importance for improving patient's management. Methods A systematic review of literature was conducted until 24 April 2020. From 6843 articles, 49 studies were selected for a pooled a...
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| Vydáno v: | European journal of clinical investigation Ročník 50; číslo 10; s. e13362 - n/a |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
England
Blackwell Publishing Ltd
01.10.2020
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| Témata: | |
| ISSN: | 0014-2972, 1365-2362, 1365-2362 |
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| Abstract | Background
Identification of reliable outcome predictors in coronavirus disease 2019 (COVID‐19) is of paramount importance for improving patient's management.
Methods
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.
Results
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.
Conclusions
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. |
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| AbstractList | BackgroundIdentification of reliable outcome predictors in coronavirus disease 2019 (COVID‐19) is of paramount importance for improving patient's management.MethodsA 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.ResultsWe 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.ConclusionsAdvanced 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. Identification of reliable outcome predictors in coronavirus disease 2019 (COVID-19) is of paramount importance for improving patient's management.BACKGROUNDIdentification of reliable outcome predictors in coronavirus disease 2019 (COVID-19) is of paramount importance for improving patient's management.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.METHODSA 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.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.RESULTSWe 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.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.CONCLUSIONSAdvanced 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. Identification of reliable outcome predictors in coronavirus disease 2019 (COVID-19) is of paramount importance for improving patient's management. 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. 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. 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. Background Identification of reliable outcome predictors in coronavirus disease 2019 (COVID‐19) is of paramount importance for improving patient's management. Methods 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. Results 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. Conclusions 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. |
| Author | Stamatelopoulos, Kimon Georgiopoulos, Georgios Aimo, Alberto Koutli, Evangelia Dimopoulos, Meletios‐Athanasios Masci, Pier Giorgio Ahmadi, Navid Tondi, Lara Caforio, Alida L. P. Figliozzi, Stefano |
| Author_xml | – sequence: 1 givenname: Stefano orcidid: 0000-0003-2991-1548 surname: Figliozzi fullname: Figliozzi, Stefano organization: Humanitas Clinical and Research Center ‐ IRCCS – sequence: 2 givenname: Pier Giorgio surname: Masci fullname: Masci, Pier Giorgio organization: King's College London – sequence: 3 givenname: Navid surname: Ahmadi fullname: Ahmadi, Navid organization: Poznan University of Medical Sciences – sequence: 4 givenname: Lara surname: Tondi fullname: Tondi, Lara organization: IRCCS Policlinico San Donato – sequence: 5 givenname: Evangelia surname: Koutli fullname: Koutli, Evangelia organization: University College London – sequence: 6 givenname: Alberto surname: Aimo fullname: Aimo, Alberto organization: University Hospital of Pisa – sequence: 7 givenname: Kimon surname: Stamatelopoulos fullname: Stamatelopoulos, Kimon organization: National and Kapodistrian University of Athens School of Medicine – sequence: 8 givenname: Meletios‐Athanasios surname: Dimopoulos fullname: Dimopoulos, Meletios‐Athanasios organization: National and Kapodistrian University of Athens School of Medicine – sequence: 9 givenname: Alida L. P. surname: Caforio fullname: Caforio, Alida L. P. organization: University of Padua Medical School – sequence: 10 givenname: Georgios surname: Georgiopoulos fullname: Georgiopoulos, Georgios email: georgios.georgiopoulos@kcl.ac.uk organization: National and Kapodistrian University of Athens School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32726868$$D View this record in MEDLINE/PubMed |
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| Notes | Meletios‐Athanasios Dimopoulos, Alida LP Caforio, and Georgios Georgiopoulos contributed equally as senior authors. Prospero registration number: CRD42020181873 Stefano Figliozzi and Pier Giorgio Masci contributed equally as first authors. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Undefined-3 |
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Identification of reliable outcome predictors in coronavirus disease 2019 (COVID‐19) is of paramount importance for improving patient's management.... Identification of reliable outcome predictors in coronavirus disease 2019 (COVID-19) is of paramount importance for improving patient's management. A... BackgroundIdentification of reliable outcome predictors in coronavirus disease 2019 (COVID‐19) is of paramount importance for improving patient's... Identification of reliable outcome predictors in coronavirus disease 2019 (COVID-19) is of paramount importance for improving patient's... |
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| SubjectTerms | Acute Disease Acute Kidney Injury - epidemiology Adrenal Cortex Hormones - therapeutic use Adult Age Age Factors Aged Aged, 80 and over Betacoronavirus Biomarkers C-Reactive Protein - metabolism Cardiovascular Diseases - epidemiology Cerebrovascular Disorders - epidemiology Confidence intervals Coronavirus Infections - epidemiology Coronavirus Infections - metabolism Coronavirus Infections - mortality Coronavirus Infections - therapy Coronaviruses COVID-19 Death Diabetes Mellitus - epidemiology Dimers Female Ferritins - metabolism Fibrin Fibrinogen Degradation Products - metabolism Heart Heart Diseases Hospital Mortality Hospitalization Humans Hypertension - epidemiology Inflammation Injuries Intensive Care Units Interleukin-6 - metabolism Kidneys Literature reviews Liver Diseases - epidemiology Lymphopenia Lymphopenia - epidemiology Male Mechanical ventilation Meta-analysis Middle Aged Mortality Neoplasms - epidemiology Obesity - epidemiology outcomes Pandemics Patients Pneumonia, Viral - epidemiology Pneumonia, Viral - metabolism Pneumonia, Viral - mortality Pneumonia, Viral - therapy predictors Procalcitonin Procalcitonin - metabolism Prognosis Pulmonary Disease, Chronic Obstructive - epidemiology Respiration, Artificial SARS-CoV-2 Severity of Illness Index Sex Sex Factors Smoking - epidemiology Statistical analysis Steroid hormones Steroids Systematic review Thrombocytopenia Thrombocytopenia - epidemiology Ventilation Viral diseases Young Adult |
| Title | Predictors of adverse prognosis in COVID‐19: A systematic review and meta‐analysis |
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