Scoring systems for predicting mortality for severe patients with COVID-19

Area of Science:

• Infectious Diseases
• Critical Care Medicine
• Biostatistics

Background:

• Severe Coronavirus disease 2019 (COVID-19) poses a significant global health threat, necessitating identification of mortality predictors.
• Understanding risk factors for severe COVID-19 is crucial for improving patient outcomes and resource allocation.

Purpose of the Study:

• To investigate independent risk factors associated with in-hospital mortality in severe COVID-19 patients.
• To develop and validate a predictive scoring system for in-hospital mortality and complications in severe COVID-19.

Main Methods:

• Retrospective analysis of 2529 COVID-19 patients, with 452 severe cases included for final analysis.
• Utilized LASSO regression and multivariable analysis to identify significant predictors of mortality.
• Developed a COVID-19 Scoring System (CSS) based on identified independent risk factors.

Main Results:

• Old age, coronary heart disease (CHD), low percentage of lymphocytes (LYM%), elevated procalcitonin (PCT), and high D-dimer (DD) were identified as independent risk factors for mortality.
• The developed CSS demonstrated strong predictive performance with an AUC of 0.919 and good calibration.
• Significant differences in complications were observed between low-risk and high-risk groups identified by the CSS.

Conclusions:

• Old age, CHD, LYM%, PCT, and DD are independently associated with increased mortality in severe COVID-19.
• The CSS is a valuable tool for clinicians to predict in-hospital mortality and complications, aiding in risk stratification and patient management.