Derivation and Internal Validation of a Clinical Risk Prediction Tool for Hyperkalemia-Related Emergency Department Encounters Among Hemodialysis Patients

Background: Approximately 10% of emergency department (ED) visits among dialysis patients are for conditions that could potentially be managed in outpatient settings, such as hyperkalemia. Objective: Using population-based data, we derived and internally validated a risk score to identify hemodialysis patients at increased risk of hyperkalemia-related ED events. Design: Retrospective cohort study. Setting: Ten in-center hemodialysis sites in southern Alberta, Canada. Patients: All maintenance hemodialysis patients (>= 18 years) between March 2009 and March 2017. Measurements: Predictors of hyperkalemia-related ED events included patient demographics, comorbidities, health-system use, laboratory measurements, and dialysis information. The outcome of interest (hyperkalemia-related ED events) was defined by International Classification of Diseases (10th Revision; ICD-10) codes and/or serum potassium [K+] >= 6 mmol/L. Methods: Bootstrapped logistic regression was used to derive and internally validate a model of important predictors of hyperkalemia-related ED events. A point system was created based on regression coefficients. Model discrimination was assessed by an optimism-adjusted C-statistic and calibration by deciles of risk and calibration slope. Results: Of the 1533 maintenance hemodialysis patients in our cohort, 331 (21.6%) presented to the ED with 615 hyperkalemia-related ED events. A 9-point scale for risk of a hyperkalemia-related ED event was created with points assigned to 5 strong predictors based on their regression coefficients: >= 1 laboratory measurement of serum K+ >= 6 mmol/L in the prior 6 months (3 points); >= 1 Hemoglobin A1C [HbA1C] measurement >= 8% in the prior 12 months (1 point); mean ultrafiltration of >= 10 mL/kg/h over the preceding 2 weeks (2 points); >= 25 hours of cumulative time dialyzing over the preceding 2 weeks (1 point); and dialysis vintage of >= 2 years (2 points). Model discrimination (C-statistic: 0.75) and calibration were good. Limitations: Measures related to health behaviors, social determinants of health, and residual kidney function were not available for inclusion as potential predictors. Conclusions: While this tool requires external validation, it may help identify high-risk patients and allow for preventative strategies to avoid unnecessary ED visits and improve patient quality of life.