Electrolyte Abnormalities and Clinical Outcomes in Heart Failure Patients: A Retrospective Cohort Study

BackgroundElectrolyte abnormalities commonly complicate heart failure management, yet their prognostic significance and optimal monitoring strategies remain incompletely characterized. We examined the prevalence, temporal patterns, and clinical outcomes associated with electrolyte abnormalities in hospitalized heart failure patients. MethodsRetrospective cohort study of 30,678 heart failure patients (80,408 admissions) from the MIMIC-IV database (2008-2022). Clinically significant electrolyte abnormalities (CSEA) were defined as documented potassium <3.5 or >5.0 mEq/L and sodium <135 or >145 mEq/L during hospitalization. Primary outcomes were 30-day all-cause readmission and mortality. We developed a clinical risk score incorporating electrolyte abnormalities and examined temporal electrolyte patterns. ResultsCSEA occurred in 705 patients (2.3%), with 96.7% achieving normalization by discharge. Patients with abnormalities had paradoxically lower 30-day readmission (3.9% vs 15.3%; adjusted HR 0.29, 95% CI 0.21-0.40) but substantially higher mortality (15.4% vs 7.0%; adjusted HR 1.93, 95% CI 1.57-2.38), reflecting competing mortality risk. Individual electrolyte abnormalities independently predicted mortality: hypokalemia HR 1.45 (1.28-1.65), hyperkalemia HR 1.67 (1.48-1.89), hyponatremia HR 1.34 (1.19-1.51). Temporal analysis revealed biphasic patterns--initial correction followed by recurrence--identified the highest-risk subset (composite event rates 27.6% for potassium, 28.5% for sodium). A five-variable risk score (electrolyte abnormality, age [&ge;]75, chronic kidney disease, multiple admissions, coronary artery disease) achieved C-statistic 0.595 with 2.34-fold risk discrimination across categories. ConclusionsCSEA independently predict mortality in heart failure patients despite paradoxically lower readmission rates due to competing risks. Biphasic electrolyte patterns identify particularly high-risk patients. Simple risk stratification using routinely collected electrolyte data may enhance post-discharge risk prediction and inform targeted monitoring strategies.