Journal of Patient Safety & Quality Improvement

Journal of Patient Safety & Quality Improvement

Risk Stratification in Early COVID-19 and Its Predictive Power for Hospitalized Fatality

Document Type : Research Paper

Authors
Hamed Tabesh 1
Maryam Rastegar 2
Azadeh Saki 3
1 1. Patient Safety Research Center, Clinical Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran. 2. Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
2 Infectious Diseases and Tropical Medicine Research Center, Research Institute of Cellular and Molecular Sciences in Infectious Diseases, Zahedan University of Medical Sciences, Zahedan, Iran.
3 Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.
10.22038/psj.2026.95338.1529
Abstract
Background:
Accurate assessment of COVID-19 severity is essential for predicting patient outcomes, enabling clinicians to identify individuals at high risk of fatality early in the disease course. This prognostic information serves to guide triage decisions, ensuring that limited hospital resources are effectively allocated to those in greatest need.

methods:
This retrospective study included 160 hospitalized patients with confirmed COVID-19, aged 18 years and older, who were admitted to hospitals in Iranshahr during 2020. Comorbidities, social and behavioral variables were incorporated into an Artificial Neural Network (ANN) model to predict the severity and fatality of COVID-19.

Results:
The overall in-hospital fatality rate was 14.4%, with all deaths occurring within the critical group. Among demographic factors, older age and lower educational level were significantly associated with increased disease severity (๐‘ƒ<0.01), whereas sex and occupational status showed non-significant trends. Regarding behavioral factors, smoking exhibited a positive but statistically non-significant association with COVID-19 severity (๐‘ƒ=0.067), while lower fruit consumption was significantly associated with more severe outcomes (๐‘ƒ=0.021). Among comorbidities, cancer, hypertension, and cardiovascular disease were strongly associated with severe and critical illness (๐‘ƒ<0.05). The ANN analysis revealed that the national COVID-19 severity protocol possesses exceptional predictive power for fatality (AUC = 0.99, 95% CI: 0.98–1.00).

Conclusions:
The findings of this study highlight the critical role of national protocols in optimizing patient triage and hospitalization priorities, and suggest that clinical measures should be integrated with a comprehensive understanding of the social and behavioral factors influencing severity and fatality.
Keywords
COVIDโ€‘19 Severity
Social Determinants
National Protocols
Artificial Neural Network
Subjects
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Journal of Patient Safety & Quality Improvement
Volume 14, Issue 4
Autumn 2026
Pages 221-235