The Charlson Comorbidity Index as a Predictor of mortality in hospitalized Covid-19 patients during the pandemic

Abstract

Introduction: This study aimed to predict the risk of mortality among COVID-19 patients in the central region of Iran by employing the Charlson Comorbidity Index (CCI), with adjustments made for age in the predictive model. Material & Methods: In this cross-sectional study, encompassing all probable, suspicious, and confirmed COVID-19 cases from the onset of the pandemic (55307 individuals), 3415 cases resulting in death were designated as the study group, while the survivors constituted the control group. Results: The Charlson Comorbidity Index revealed that over 11 percent of all patients had at least one underlying medical condition. Logistic regression analysis indicated a significantly elevated likelihood of mortality among patients with comorbidities. Specifically, individuals with a CCI score of 6 or higher were more than twice as likely to succumb to the virus compared to those without underlying diseases. Those with a score of 6 or more exhibited the highest odds ratio (OR 2.4; 95% CI 1.3-4.5). Conclusion: The study findings underscore the heightened vulnerability of individuals to COVID-19 mortality, particularly among the elderly with pre-existing health conditions. The coexistence of age and comorbidities substantially increased the risk of death due to COVID-19 in this population. Consequently, targeted interventions and focused care strategies may be crucial for this high-risk demographic in pandemic management efforts.

Keywords

• mortality
• prediction
• infection
• charlson comorbidity index