A Mixture Cure Modeling Framework for Survival Analysis of Hospitalized Covid-19 Patients: Implications for Hospital Resource Management Publisher Pubmed

Summary: Who faced greater COVID-19 mortality risk? A statistical mixture-cure analysis distinguished long-term survivors from higher-risk patients, without showing causes. #COVID19 #Biostatistics

Rezaei Ghahroodi Z ; Darvishi R ; Safari A ; Haghighi F ; Mansourian M
Source: BMC Medical Informatics and Decision Making Published:2026

Abstract

Background: COVID-19 has caused substantial global morbidity and mortality, placing unprecedented strain on hospital systems. Understanding heterogeneity in survival outcomes and identifying subgroups with distinct mortality risk profiles are essential for improving preparedness in future pandemics. Traditional survival models do not account for the presence of patients who are effectively cured within a clinically meaningful window. Mixture cure models offer a framework for simultaneously estimating the probability of long-term survival and the time-to-event among uncured individuals. This study applies a mixture cure modeling approach to evaluate mortality risk factors among hospitalized COVID-19 patients and to derive statistical insights relevant to hospital resource planning. Methods: Data were obtained from 1,998 patients enrolled in the Khorshid COVID Cohort (KCC), a prospective hospital-based study with one-year follow-up. The outcome was COVID-19 death occurring during hospitalization or within 12 months post-discharge. Patients who remained alive throughout the one-year follow-up window were classified as cured. The analysis accounted for exact, right-censored, and interval-censored event times within the mixture cure model. Feature selection was performed using random survival forests and binary random forests, followed by interaction identification with decision trees. The cure component was modeled using logistic regression, while Weibull and Cox proportional hazards models characterized time-to-death among uncured individuals. Results: Older age was a strong predictor of mortality, with hazard ratios of 2.30 for ages 65–74 and 3.32 for ages > 74 compared with < 45 years. Lower baseline oxygen saturation (< 80%) increased the death hazard by 3.41-fold. Longer time to ICU admission increased mortality risk (HR = 1.17). Higher cumulative methylprednisolone dosage increased the odds of death by 2.06-fold, whereas cumulative ceftriaxone use reduced the death hazard by 49% among patients with oxygen saturation of 80–89%. Meropenem use—particularly without concurrent methylprednisolone—was associated with a significantly elevated hazard of death. Results were consistent across mixture cure and Cox models. Feature selection identified key predictive variables, including intubation duration, ICU duration, oxygen saturation, pulse and respiratory rates, BUN, and specific treatment interactions. Conclusion: The mixture cure framework distinguished between patients with long-term survival potential and those at sustained risk of mortality, revealing clinically relevant factors associated with both cure probability and death hazard. Although descriptive—not causal—these findings highlight subgroups with high resource needs and may inform conceptual approaches to planning ICU capacity, ventilator demand, and treatment prioritization during future pandemics. Mixture cure models provide a flexible statistical tool for understanding heterogeneous survival patterns in acute infectious diseases. Clinical trial number: Not applicable. © The Author(s) 2026.