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Mortality Due to Covid-19 in Hospitalized Patients: A Prediction Model Based on Different Risk Factors.
Irma Luz Yupari-Azabache1,2, Ruben Kenny Briceno2,3, Jorge Luis Díaz-Ortega1,4
1Institutos Y Centros de Investigación, Universidad César Vallejo, Trujillo, Peru.
Risk Management and Healthcare Policy
|September 26, 2025
Summary
This study developed a COVID-19 mortality prediction model using patient data. Key factors like age, symptoms, and lab results help forecast outcomes for better healthcare management.
Area of Science:
- Epidemiology
- Public Health
- Medical Informatics
Background:
- The COVID-19 pandemic has caused significant global mortality and socioeconomic disruption since 2020.
- Healthcare systems worldwide have been severely impacted by the pandemic's scale and severity.
Purpose of the Study:
- To develop and analyze a predictive model for COVID-19 mortality.
- Identify key risk factors associated with fatal outcomes in hospitalized COVID-19 patients.
Main Methods:
- A retrospective, cross-sectional study involving 2000 hospitalized patients.
- Analysis of biological, clinical, laboratory, and comorbidity data.
- Bivariate and multivariate analysis using binary logistic regression (SPSS v29).
Main Results:
- Deceased patients were predominantly male, over 60, with blood type O positive, hypertension, type 2 diabetes, and obesity.
- Common symptoms included fever, malaise, shortness of breath, and fatigue.
- Tomography showed bilateral ground-glass opacities (BiRad 5 scale) in severely ill patients.
Conclusions:
- A predictive model for COVID-19 mortality was successfully developed with a 76% prognostic accuracy.
- Significant predictors of mortality include age, specific symptoms (fever, cough, sore throat, fatigue, shortness of breath), CT findings (unilateral consolidation), and laboratory values (hemoglobin, leukocytes, lymphocytes, platelets, urea, ferritin).
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