Risk factors for mortality in critically ill patients with COVID-19 in Huanggang, China: A single-center multivariate

Yinyin Chen1,2,3, Zeqiang Linli4,5, Yuting Lei1

  • 1Department of Nephrology and Laboratory of Kidney Disease, Hunan Provincial People's Hospital, Hunan Normal University, Changsha, China.

Journal of Medical Virology
|September 30, 2020
PubMed

Insights

Simple patient indicators like age and symptom duration are key predictors of mortality in critically ill COVID-19 patients. Early identification of these risk factors can improve patient outcomes.

Area of Science:

  • Critical Care Medicine
  • Infectious Diseases
  • Biostatistics

Background:

  • COVID-19 has a global impact, with limited understanding of mortality risk factors in critically ill patients.
  • Detailed self-reported and laboratory indicators for predicting COVID-19 mortality require further investigation.

Purpose of the Study:

  • To identify and analyze risk factors for mortality in critically ill COVID-19 patients.
  • To develop a predictive model for COVID-19 mortality using self-evaluation and laboratory data.

Main Methods:

  • Retrospective analysis of 192 critically ill COVID-19 patients (142 survivors, 50 non-survivors).
  • Comparison of demographic, clinical, symptom, and laboratory data between survivors and non-survivors.
  • Multivariate pattern analysis (MVPA) to identify significant mortality predictors.

Main Results:

  • MVPA model achieved 93% classification accuracy using both self-evaluation and laboratory indicators.
  • Key self-evaluation risk factors for mortality included age, illness duration, and Barthel Index score.
  • Laboratory indicators like C-reactive protein, WBC, platelet count, fibrin degradation products, oxygenation index, lymphocyte count, and d-dimer were also significant predictors.
  • Illness duration was identified as the strongest predictor of all-cause mortality.

Conclusions:

  • Accessible self-evaluation indicators, alongside laboratory results, can predict mortality in critically ill COVID-19 patients.
  • A predictive model utilizing these factors can aid clinicians in identifying high-risk individuals.
  • This approach can enhance public health awareness and inform clinical decision-making for COVID-19 patient management.

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