Predicting Covid-19 Progression in Hospitalized Patients in Kurdistan Province Using a Multi-State Model Publisher



Bayazidi S^{1, 2} ; Moradi G³ ; Masoumi S^{4, 5} ; Setarehdan SA^{1, 6} ; Baradaran HR⁷
Source: Journal of Diabetes and Metabolic Disorders Published:2025

Abstract

Objectives: This study aimed to implement a multi-state risk prediction model to predict the progression of COVID-19 cases among hospitalized patients in Kurdistan province by analyzing hospital care data. Methods: This retrospective analysis consisted of data from 17,286 patients admitted to hospitals with COVID-19 from March 23, 2019, to December 19, 2021, in various areas in the Kurdistan province. A multi-state prediction model was used to show that each transition is predicted by a different set of variables. These variables include underlying diseases (like diabetes, hypertension, etc.) and sociodemographic information (like sex and age). Model aims to predict the likelihood of recovery, the need for critical care intervention (e.g., transfer to isolation units or the ICU), or exits from the hospitalization course. We performed the statistical analysis using R software and the mstate package. Results: Of the hospitalized patients studied, 5.6% died of the disease, 6.6% were admitted to ICUs, and 38.72% were treated in isolation units. Mortality rates in general wards, isolation units, and the ICU were 3.48%, 4.56%, and 26.6%, respectively. Significant predictors for ICU admission include age over 60 years (HR: 1.46, 95% CI 1.37–1.55), kidney-related conditions (HR: 2.19, 95% CI 1.65–2.91), cardiovascular diseases (HR: 1.68, 95% CI 1.46–1.94), lung disease (HR: 1.89,‏95% CI 1.43–2.05), and cancer (HR: 2.46,‏95% CI 1.77–3.41). The likelihood of in-hospital death is significantly increased by age over 60 years (HR: 2.40, 95% CI 2.09–2.76), diabetes (HR: 1.97, 95% CI 1.45–2.68), high blood pressure (HR: 2.30, 95% CI 1.78–2.97), and history of heart disease (HR: 3.01, 95% CI 2.29–3.95). Conclusion: The model helps the provider and policymakers to make an informed decision depending on patient management and resource allocation within the health care systems. © The Author(s), under exclusive licence to Tehran University of Medical Sciences 2025.