Short-term survival analysis and mortality rate of COVID-19 patients in Neyshabur, Iran (February to June 2020): a retrospective cohort study

Background and Aim: COVID-19 has caused high mortality, strained healthcare systems, and socio-economic disruptions, representing a major public health crisis. Despite subsiding main waves, resurgence with new variants or similar viruses remains possible. Identifying factors affecting patient survival is crucial for health system preparedness and crisis management.
Methods: This prospective cohort study included 1,114 COVID-19 patients in Neyshabur, Iran, from February 20 to June 20, 2020. Data were collected from medical records, with one-month follow-up. Survival analysis used Kaplan–Meier and Cox proportional hazards models.
Results: Of the total patients, 923 were hospitalized and 191 were treated as outpatients. The median survival time among hospitalized patients was 20 days. In the multivariable model, age (per year: HR = 1.024, 95% CI: 1.012–1.036, P<0.001), male gender (HR = 1.948, 95% CI: 1.292–2.938, P<0.001), oxygen saturation below 93% (HR = 1.838, 95% CI: 1.097–3.082, P=0.021), and intubation (HR = 4.926, 95% CI: 2.927–8.289, P<0.001) were significantly associated with reduced survival. Diabetes and cardiovascular diseases were not significant in the final model.
Conclusion: Older age, male gender, low oxygen saturation, and the need for intubation are key predictors of reduced survival in COVID-19 patients. These findings can assist in identifying high-risk patients, optimizing clinical care, and improving health system readiness in future pandemics or similar respiratory disease outbreaks.
Introduction: Background and Aim: Coronavirus disease 2019 (COVID-19), characterized by high mortality, substantial pressure on healthcare systems, and profound social and economic disruptions, has been one of the most severe public health crises in recent decades. Although the major waves of the pandemic have subsided, the possibility of the re-emergence of the disease through new variants or the emergence of similar respiratory viruses remains. Identifying the factors associated with patient survival is therefore essential for improving health system preparedness and enhancing the management of future public health crises.
Methods: This retrospective cohort study was conducted on 1,114 patients with confirmed COVID-19 who presented to the two main referral hospitals in Neyshabur, Iran, between February 20 and June 20, 2020. Clinical and demographic data were extracted from medical records and hospital electronic information systems. Patients were followed for one month after the date of diagnosis, and vital status, including the date of death when applicable, was obtained from documented clinical records. Survival analysis was performed using the Kaplan–Meier method and the Cox proportional hazards model in SPSS version 26, with a significance level of 0.05.
Results: Of the 1,114 patients with COVID-19, 923 were hospitalized and 191 were managed as outpatients. The median survival time among hospitalized patients was 20 days. Overall, 117 patients died, corresponding to a one-month case fatality rate (CFR) of 10.5%. The CFR was 11.81% among hospitalized patients and 4.19% among outpatients. Multivariable Cox regression analysis showed that increasing age (per one-year increase: HR=1.024; 95% CI: 1.012–1.036; P<0.001), male sex (HR=1.948; 95% CI: 1.292–2.938; P<0.001), oxygen saturation below 93% (HR=1.838; 95% CI: 1.097–3.082; P=0.021), and the need for intubation (HR=4.926; 95% CI: 2.927–8.289; P<0.001) were independently associated with reduced survival. Diabetes mellitus and cardiovascular disease were not statistically significant in the final model.
Conclusion: Advanced age, male sex, reduced oxygen saturation, and the need for intubation were the most important predictors of decreased survival among patients with COVID-19. These findings may contribute to the early identification of high-risk patients, optimization of clinical care, and improved preparedness of health systems for future pandemics or similar respiratory disease outbreaks.