Early predictors and screening tool developing for severe patients with COVID-19.

Area of Science:

• Infectious Diseases
• Public Health
• Epidemiology

Background:

• Coronavirus disease 2019 (COVID-19) pandemic necessitates early identification of high-risk individuals to reduce mortality.
• Accessible screening tools are crucial, as not all predictors are universally available in healthcare settings.

Purpose of the Study:

• To develop and validate a simple screening tool for early prediction of severe COVID-19 cases.
• To identify easily obtainable predictors for risk stratification in COVID-19 patients.

Main Methods:

• Retrospective study of 813 confirmed COVID-19 cases.
• Logistic regression analysis to select key predictors.
• Development and ROC curve analysis of a novel screening tool.

Main Results:

• Seven early predictors identified: chronic kidney disease, age >60, low lymphocyte count, high Neutrophil to Lymphocyte Ratio, high fever, male sex, and cardiovascular disease.
• The screening tool achieved an Area Under the ROC Curve of 0.798, with 72.0% sensitivity and 75.3% specificity at a cut-off >4.5.

Conclusions:

• The developed screening tool offers a practical approach for early prediction of severe COVID-19.
• It is particularly valuable for healthcare systems facing overload, enabling timely alerts for at-risk patients.