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Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review

Author

Listed:
  • Ariel Izcovich
  • Martín Alberto Ragusa
  • Fernando Tortosa
  • María Andrea Lavena Marzio
  • Camila Agnoletti
  • Agustín Bengolea
  • Agustina Ceirano
  • Federico Espinosa
  • Ezequiel Saavedra
  • Verónica Sanguine
  • Alfredo Tassara
  • Candelaria Cid
  • Hugo Norberto Catalano
  • Arnav Agarwal
  • Farid Foroutan
  • Gabriel Rada

Abstract

Background and purpose: The objective of our systematic review is to identify prognostic factors that may be used in decision-making related to the care of patients infected with COVID-19. Data sources: We conducted highly sensitive searches in PubMed/MEDLINE, the Cochrane Central Register of Controlled Trials (CENTRAL) and Embase. The searches covered the period from the inception date of each database until April 28, 2020. No study design, publication status or language restriction were applied. Study selection and data extraction: We included studies that assessed patients with confirmed or suspected SARS-CoV-2 infectious disease and examined one or more prognostic factors for mortality or disease severity. Results: We included 207 studies and found high or moderate certainty that the following 49 variables provide valuable prognostic information on mortality and/or severe disease in patients with COVID-19 infectious disease: Demographic factors (age, male sex, smoking), patient history factors (comorbidities, cerebrovascular disease, chronic obstructive pulmonary disease, chronic kidney disease, cardiovascular disease, cardiac arrhythmia, arterial hypertension, diabetes, dementia, cancer and dyslipidemia), physical examination factors (respiratory failure, low blood pressure, hypoxemia, tachycardia, dyspnea, anorexia, tachypnea, haemoptysis, abdominal pain, fatigue, fever and myalgia or arthralgia), laboratory factors (high blood procalcitonin, myocardial injury markers, high blood White Blood Cell count (WBC), high blood lactate, low blood platelet count, plasma creatinine increase, high blood D-dimer, high blood lactate dehydrogenase (LDH), high blood C-reactive protein (CRP), decrease in lymphocyte count, high blood aspartate aminotransferase (AST), decrease in blood albumin, high blood interleukin-6 (IL-6), high blood neutrophil count, high blood B-type natriuretic peptide (BNP), high blood urea nitrogen (BUN), high blood creatine kinase (CK), high blood bilirubin and high erythrocyte sedimentation rate (ESR)), radiological factors (consolidative infiltrate and pleural effusion) and high SOFA score (sequential organ failure assessment score). Conclusion: Identified prognostic factors can help clinicians and policy makers in tailoring management strategies for patients with COVID-19 infectious disease while researchers can utilise our findings to develop multivariable prognostic models that could eventually facilitate decision-making and improve patient important outcomes. Systematic review registration: Prospero registration number: CRD42020178802. Protocol available at: https://www.medrxiv.org/content/10.1101/2020.04.08.20056598v1.

Suggested Citation

  • Ariel Izcovich & Martín Alberto Ragusa & Fernando Tortosa & María Andrea Lavena Marzio & Camila Agnoletti & Agustín Bengolea & Agustina Ceirano & Federico Espinosa & Ezequiel Saavedra & Verónica Sangu, 2020. "Prognostic factors for severity and mortality in patients infected with COVID-19: A systematic review," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-30, November.
    • Handle: RePEc:plo:pone00:0241955
    DOI: 10.1371/journal.pone.0241955
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    References listed on IDEAS

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    1. Luhuan Yang & Yunhong Lei & Jinglan Liu & Qiong Liu & Mingwu Li & Xiaojing Zhou & Chuangjun Hu & Zifeng Li & Rong Zhang & Jun Yang, 2020. "Epidemiological and Clinical Features Of 200 Hospitalized Patients with Corona Virus Disease 2019 in Yichang, China: A Descriptive Study," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 27(1), pages 20527-20534, April.
    2. Wang, Zhu, 2013. "Converting Odds Ratio to Relative Risk in Cohort Studies with Partial Data Information," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i05).
    3. Viechtbauer, Wolfgang, 2010. "Conducting Meta-Analyses in R with the metafor Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i03).
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    1. Quy Xiao Xuan Lin & Deepa Rajagopalan & Akshamal M. Gamage & Le Min Tan & Prasanna Nori Venkatesh & Wharton O. Y. Chan & Dilip Kumar & Ragini Agrawal & Yao Chen & Siew-Wai Fong & Amit Singh & Louisa J, 2024. "Longitudinal single cell atlas identifies complex temporal relationship between type I interferon response and COVID-19 severity," Nature Communications, Nature, vol. 15(1), pages 1-19, December.

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