Machine Learning Models Predicting Cardiovascular and Renal Outcomes and Mortality in Patients with Hyperkalemia

Abstract

Hyperkalemia is associated with increased risks of mortality and adverse clinical outcomes. The treatment of hyperkalemia often leads to the discontinuation or restriction of beneficial but potassium-increasing therapy such as renin-angiotensin-aldosterone inhibitors (RAASi) and high-potassium diet including fruits and vegetables. To date, limited evidence is available for personalized risk evaluation in this heterogeneous and multifactorial pathophysiological condition. We developed risk prediction models using extreme gradient boosting (XGB), multiple logistic regression (LR), and deep neural network. Models were derived from a retrospective cohort of hyperkalemic patients with either heart failure or chronic kidney disease stage ≥3a from a Japanese nationwide database (1 April 2008−30 September 2018). Studied outcomes included all-cause death, renal replacement therapy introduction (RRT), hospitalization for heart failure (HHF), and cardiovascular events within three years after hyperkalemic episodes. The best performing model was further validated using an external cohort. A total of 24,949 adult hyperkalemic patients were selected for model derivation and internal validation. A total of 1452 deaths (16.6%), 887 RRT (10.1%), 1,345 HHF (15.4%), and 621 cardiovascular events (7.1%) were observed. XGB outperformed other models. The area under receiver operator characteristic curves (AUROCs) of XGB vs. LR (95% CIs) for death, RRT, HHF, and cardiovascular events were 0.823 (0.805−0.841) vs. 0.809 (0.791−0.828), 0.957 (0.947−0.967) vs. 0.947 (0.936−0.959), 0.863 (0.846−0.880) vs. 0.838 (0.820−0.856), and 0.809 (0.784−0.834) vs. 0.798 (0.772−0.823), respectively. In the external dataset including 86,279 patients, AUROCs (95% CIs) for XGB were: death, 0.747 (0.742−0.753); RRT, 0.888 (0.882−0.894); HHF, 0.673 (0.666−0.679); and cardiovascular events, 0.585 (0.578−0.591). Kaplan−Meier curves of the high-risk predicted group showed a statistically significant difference from that of the low-risk predicted groups for all outcomes (p < 0.005; log-rank test). These findings suggest possible use of machine learning models for real-world risk assessment as a guide for observation and/or treatment decision making that may potentially lead to improved outcomes in hyperkalemic patients while retaining the benefit of life-saving therapies.

Keywords: artificial intelligence; chronic kidney disease; congestive heart failure; hyperkalemia.

Figure 1

Patient flow diagram for model development.

Figure 2

Receiver operator characteristics curves evaluated on the internal validation set. The black rings on each plot show the best-cut off values. XGB, extreme gradient boosting; LR, logistic regression; NN, neural network; AUROC, area under the operator receiver characteristics curve.

Figure 3

Shapley additive explanations summary plots of the top 20 most important variables for the extreme gradient boosting model.

Figure 4

Shapley additive explanations summary plots of the top 20 most important variables for the logistic regression model.

Figure 5

Kaplan–Meier plots of high- and low-risk groups based on risk predictions in the external validation set.