Development and validation of a predictive model for the assessment of potassium-lowering treatment among hyperkalemia patients.

Background: Hyperkalemia is common among patients in emergency department and is associated with mortality. While, there is a lack of good evaluation and prediction methods for the efficacy of potassium-lowering treatment, making the drug dosage adjustment quite difficult. We aimed to develop a predictive model to provide early forecasting of treating effects for hyperkalemia patients.

Methods: Around 80% of hyperkalemia patients (=818) were randomly selected as the training dataset and the remaining 20% (=196) as the validating dataset. According to the serum potassium (K) levels after the first round of potassium-lowering treatment, patients were classified into the effective and ineffective groups. Multivariate logistic regression analyses were performed to develop a prediction model. The receiver operating characteristic (ROC) curve and calibration curve analysis were used for model validation.

Results: In the training dataset, 429 patients had favorable effects after treatment (effective group), and 389 had poor therapeutic outcomes (ineffective group). Patients in the ineffective group had a higher percentage of renal disease (=0.007), peripheral edema (<0.001), oliguria (=0.001), or higher initial serum K level (<0.001). The percentage of insulin usage was higher in the effective group than in the ineffective group (=0.005). After multivariate logistic regression analysis, we found age, peripheral edema, oliguria, history of kidney transplantation, end-stage renal disease, insulin, and initial serum K were all independently associated with favorable treatment effects.

Conclusion: The predictive model could provide early forecasting of therapeutic outcomes for hyperkalemia patients after drug treatment, which could help clinicians to identify hyperkalemia patients with high risk and adjust the dosage of medication for potassium-lowering.