Identified Factors in COVID-19 Patients in Predicting Mortality.

Area of Science:

• Medical Research
• Epidemiology
• Critical Care Medicine

Background:

• The COVID-19 pandemic caused increased global mortality.
• Previous studies assessed overall COVID-19 mortality without differentiating ward vs. ICU deaths.

Purpose of the Study:

• To identify factors associated with mortality in COVID-19 patients admitted to hospital wards.
• To inform potential pandemic triage strategies for intensive care unit (ICU) admissions.

Main Methods:

• Retrospective analysis of 237 COVID-19 patients admitted between January 2020 and December 2021.
• Collected demographic, clinical, laboratory, and CT imaging data.
• Compared data between deceased and recovered patients.

Main Results:

• Logistic regression identified decreased albumin, increased troponin and procalcitonin, higher PTT, diabetes mellitus, and emergency department admission as significant mortality predictors.
• A predictive model incorporating these factors demonstrated high accuracy (AUC=0.904) for predicting ward mortality.
• The model achieved 77.2% sensitivity and 91.2% specificity.

Conclusions:

• A predictive model can identify COVID-19 patients at high risk of mortality in wards.
• This model may aid in optimizing ICU admission criteria during health crises.
• Further research could refine predictive models for resource allocation in pandemics.