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10.1111/eci.13362

http://scihub22266oqcxt.onion/10.1111/eci.13362
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32726868!ä!32726868

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suck abstract from ncbi


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pmid32726868      Eur+J+Clin+Invest 2020 ; 50 (10): e13362
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  • 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]


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