Development of a hyperkalemia risk assessment model for patients with chronic kidney disease

Abstract

Objective: To investigate risk factors for hyperkalemia among chronic kidney disease (CKD) patients and establish a risk assessment model for predicting hyperkalemia events. Methods: Clinical data of CKD patients (stage 3 to 5) hospitalized between May 2017 and June 2020 from 14 hospitals were retrospectively collected and divided into training dataset and validation dataset through balanced random sampling. Multivariate logistic regression analysis was used to analyze risk factors for hyperkalemia in CKD patients and the factors were scored. Receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated. Meanwhile, the cut-off value with the best sensitivity and specificity were used to verify the accuracy of the model in validation dataset. Results: A total of 847 CKD patients were enrolled and further divided into training dataset (n=675) and validation dataset (n=172). There were 555 males and 292 females, with a mean age of (57.2±15.6) years. Multivariate logistic regression analysis showed that age, CKD stage, history of heart failure, history of serum potassium ≥5.0 mmol/L, diabetes, metabolic acidosis, and use of medications that increase serum potassium levels were risk factors for causing hyperkalemia in patients with CKD. Risk assessment model was established based on these risk factors. The AUC of the ROC curve was 0.809. Using 4 as the cut-off value, the sensitivity and specificity for predicting hyperkalemia events reached 87.1% and 57.0%, respectively. Conclusion: The model established in the current study can be used for predicting hyperkalemia events in clinical practices, which offers a new way to optimize serum potassium management in patients with CKD.