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10.1111/eci.13362 http://scihub22266oqcxt.onion/10.1111/eci.13362 32726868!�!32726868 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32726868&cmd=llinks): Failed to open stream: HTTP request failed! 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Predictors of adverse prognosis in COVID-19: A systematic review and meta-analysis |pdf=|usr=}}{{32726868}} gab.com Text Predictors of adverse prognosis in COVID-19: A systematic review and meta-analysis JO: Eur J Clin Invest http://www.kidney.de/pget.php?&tw=1&pmid=32726868 #FOAvip 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. Twit Text FOAVip Predictors of adverse prognosis in COVID-19: A systematic review and meta-analysis ????Eur J Clin Invest http://www.kidney.de/pget.php?&tw=1&pmid=32726868 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32726868 #FOAvip English Wikipedia <ref>{{Cite pmid|32726868}}</ref> Predictors of adverse prognosis in COVID-19: A systematic review and meta-analysis #MMPMID32726868 Figliozzi S; Masci PG; Ahmadi N; Tondi L; Koutli E; Aimo A; Stamatelopoulos K; Dimopoulos MA; Caforio ALP; Georgiopoulos G Eur J Clin Invest 2020[Oct]; 50 (10): e13362 PMID32726868show ga 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. |Acute Disease[MESH] |Acute Kidney Injury/*epidemiology[MESH] |Adrenal Cortex Hormones/therapeutic use[MESH] |Adult[MESH] |Age Factors[MESH] |Aged[MESH] |Aged, 80 and over[MESH] |Betacoronavirus[MESH] |C-Reactive Protein/metabolism[MESH] |COVID-19[MESH] |Cardiovascular Diseases/*epidemiology[MESH] |Cerebrovascular Disorders/epidemiology[MESH] |Coronavirus Infections/epidemiology/metabolism/mortality/*therapy[MESH] |Diabetes Mellitus/epidemiology[MESH] |Female[MESH] |Ferritins/metabolism[MESH] |Fibrin Fibrinogen Degradation Products/*metabolism[MESH] |Heart Diseases[MESH] |Hospital Mortality[MESH] |Hospitalization[MESH] |Humans[MESH] |Hypertension/epidemiology[MESH] |Intensive Care Units[MESH] |Interleukin-6/metabolism[MESH] |Liver Diseases/epidemiology[MESH] |Lymphopenia/epidemiology[MESH] |Male[MESH] |Middle Aged[MESH] |Neoplasms/epidemiology[MESH] |Obesity/epidemiology[MESH] |Pandemics[MESH] |Pneumonia, Viral/epidemiology/metabolism/mortality/*therapy[MESH] |Procalcitonin/*metabolism[MESH] |Prognosis[MESH] |Pulmonary Disease, Chronic Obstructive/epidemiology[MESH] |Respiration, Artificial[MESH] |SARS-CoV-2[MESH] |Severity of Illness Index[MESH] |Sex Factors[MESH] |Smoking/*epidemiology[MESH] |Thrombocytopenia/*epidemiology[MESH]Predictors of adverse prognosis in COVID-19: A systematic review and (epmc).pdf DeepDyve Pubget Overpricing