Dynamic Clinical and Laboratory Predictors of in-Hospital Mortality in COVID-19: A Multivariate Cox Regression Study

Background/Objectives: Identifying early and dynamic predictors of mortality in hospitalized COVID-19 patients is essential for improving prognosis and guiding therapy. Our aim is to evaluate clinical and laboratory predictors of in-hospital mortality among moderate and severe COVID-19 patients using multivariate Cox proportional hazards regression analysis. Methods: This retrospective cohort study included 168 adults (aged 18–64 years) with RT-PCR–confirmed COVID-19. Basic demographic data (age and sex) and laboratory parameters were collected on Day 1 and Day 7 of hospitalization. Stepwise Cox regression models were constructed for all patients and for the severe-disease subgroup. Results: Of 168 patients, 104 (61.9%) had severe and 64 (38.1%) moderate disease; 33 (19.6%) died, all with severe COVID-19. On Day 1, independent predictors of mortality in both the total cohort and the severe subgroup were older age (HR = 1.095, p = 0.003), male sex (HR = 0.324, p = 0.013), lower lymphocyte percentage (HR = 0.869, p = 0.041), and elevated procalcitonin (PCT) (HR = 10.972, p < 0.001). On Day 7, predictive significance shifted: in severe cases, mortality was independently associated with sex, PCT, eosinophil percentage, ferritin, vitamin D, and gamma-glutamyl transferase (GGT) (χ² = 69.47, p < 0.0001). In the total cohort, age, PCT, interleukin-6 (IL-6), and GGT were independent predictors (χ² = 86.24, p < 0.0001). Conclusions: Early mortality risk in COVID-19 was driven by demographic factors and inflammation markers, whereas by Day 7 biochemical indicators of systemic inflammation, oxidative stress and hepatic dysfunction became stronger determinants of outcome.