BACKGROUND

The Omicron subvariants of SARS-CoV-2 spread rapidly since 2021. Following China's relaxation of containment measures in December 2022, a surge in COVID-19 cases poses a public health threat. Early identification of elderly COVID-19 patients at death risk is crucial for optimizing treatment and resource use.

OBJECTIVE

To develop a clinical score for predicting death risk in elderly COVID-19 patients at hospital admission, based on a cohort from the Second Hospital of Shandong University.

METHODS

We established a retrospective cohort of hospitalized COVID-19 patients from November 1, 2022, to March 31, 2023. Cox regression identified prognostic factors, leading to the development of a nomogram-based prediction model and a clinical risk score. Patients were classified into low- and high-risk groups using optimal segmentation thresholds, with survival curves generated by the Kaplan-Meier method. An online risk calculator was developed to facilitate real-time risk assessment in clinical settings.

RESULTS

The cohort included 1413 hospitalized COVID-19 patients. Elderly patients (≥60 years, N = 971) had a high mortality rate of 18.13%. Four independent predictors of mortality were identified: age (HR = 1.07), serum albumin (HR = 0.88), serum potassium (HR = 0.35), and serum sodium (HR = 0.91). The developed risk score demonstrated strong predictive performance and effectively stratified patients into risk categories.

CONCLUSION

We developed a validated clinical risk score integrating age, serum albumin, potassium, and sodium levels to predict mortality in hospitalized elderly COVID-19 patients. This scoring system enables early risk stratification, assisting clinicians in decision-making and optimizing patient management.