ArticleInternational journal of general medicine2025
A Clinical Risk Score Based on Albumin and Electrolyte Levels for Predicting Death Risk in Hospitalized Elderly COVID-19 Patients.
Article in International journal of general medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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