Exploratory COVID-19 death risk score based on basic laboratory tests and physiological clinical measurements

Area of Science:

• Medical research
• Biostatistics
• Epidemiology

Background:

• Accurate prediction of COVID-19 mortality is crucial during resource shortages.
• A rapid and simple model is needed for timely clinical decisions.

Purpose of the Study:

• To develop and validate a novel prediction model for 30-day mortality in COVID-19 patients.
• To assess the performance of the "COVID-19 death risk score" (CDRS) against existing scores.

Main Methods:

• Retrospective study of 278 COVID-19 patients comparing survivors and non-survivors.
• Logistic regression analysis to develop the CDRS model using high-sensitivity C-reactive protein (hsCRP), D-dimer, and respiratory rate (RR).
• Internal validation using bootstrap method and assessment of discrimination (AUROC) and calibration (Hosmer-Lemeshow test).

Main Results:

• High-sensitivity C-reactive protein (hsCRP), D-dimer, and respiratory rate (RR) were independently associated with 30-day mortality.
• The developed CDRS model demonstrated high predictive accuracy (AUROC=0.984), comparable to SOFA (0.975) and CURB-65 (0.971) scores.
• Internal validation confirmed the model's robust performance (AUROC=0.980) with good calibration.

Conclusions:

• CDRS is an efficient and accurate novel prediction model for early identification of COVID-19 patients at high risk of mortality.
• The CDRS model offers similar clinical utility to established scores like SOFA and CURB-65.
• This model aids in early risk stratification for COVID-19 patients, particularly in resource-limited settings.