Prehospital Prediction of Hypokalemia in patients with ST‑Segment Elevation Myocardial Infarction: Development and Validation of a Prediction Model

Background Hypokalemia is common in patients with ST-segment elevation myocardial infarction (STEMI) and significantly elevates the risk of life-threatening arrhythmias and mortality. Yet no validated prehospital prediction tool exists to identify this high-risk condition early. Objective To develop and validate a prehospital prediction model for hypokalemia in STEMI patients using readily available clinical and electrocardiographic parameters. Methods A retrospective observational study was conducted involving 320 STEMI patients admitted to the Second Affiliated Hospital of Soochow University between January 2023 and December 2024. Patients were categorized into hypokalemia (n = 114) and non-hypokalemia (n = 206) groups based on initial serum potassium levels. Univariate logistic regression, least absolute shrinkage and selection operator(LASSO), and multivariate logistic regression were used to identify independent predictors. A nomogram was constructed and evaluated for discrimination, calibration, and clinical utility. Results Five independent predictors were identified: symptom-to-door time (OR = 0.85, 95% CI: 0.78–0.94), syncope/coma (OR = 3.57, 95% CI: 1.12–11.37), atrial arrhythmia (OR = 4.18, 95% CI: 1.33–13.17), PR interval (OR = 1.01, 95% CI: 1.00–1.02), and U wave (OR = 5.20, 95% CI: 2.59–10.46). The prediction model demonstrated good discrimination with an AUC of 0.735 (95% CI: 0.680–0.791). Calibration curves and decision curve analysis confirmed satisfactory model performance and clinical usefulness. Conclusion We developed a practical and validated nomogram for predicting prehospital hypokalemia in STEMI patients using five easily obtainable clinical and ECG variables. This tool may facilitate early identification and intervention in high-risk individuals, potentially improving prehospital management and clinical outcomes.