Prediction of hypokalemia in patients with ST-segment elevation myocardial infarction: development and validation of a model with prehospital applicability

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 prediction model for hypokalemia in STEMI patients using readily available clinical and electrocardiographic parameters that are fully accessible in prehospital settings, and to systematically evaluate its prehospital application potential. Methods: = 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 and validated a practical nomogram for predicting hypokalemia risk in STEMI patients using five variables readily available in prehospital and emergency settings. This tool enables early risk stratification, facilitates targeted intervention in high-risk individuals, and guides early potassium supplementation. It may improve prehospital care and clinical outcomes in STEMI patients.