Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability

TL;DR

This study develops and evaluates predictive models for hyperkalemia in ICU patients, demonstrating good accuracy and exploring the roles of AKI and other features, with a focus on interpretability and generalizability.

Contribution

The paper introduces interpretable machine learning models for hyperkalemia prediction in ICU patients, assessing their performance across different patient groups and clinical scenarios.

Findings

Models achieved AUCs up to 0.85 for hyperkalemia prediction.

Four of the top five predictive features were consistent across models.

AKI stage was significant only when considering all patients, not just those with AKI.

Abstract

Hyperkalemia is a potentially life-threatening condition that can lead to fatal arrhythmias. Early identification of high risk patients can inform clinical care to mitigate the risk. While hyperkalemia is often a complication of acute kidney injury (AKI), it also occurs in the absence of AKI. We developed predictive models to identify intensive care unit (ICU) patients at risk of developing hyperkalemia by using the Medical Information Mart for Intensive Care (MIMIC) and the eICU Collaborative Research Database (eICU-CRD). Our methodology focused on building multiple models, optimizing for interpretability through model selection, and simulating various clinical scenarios. In order to determine if our models perform accurately on patients with and without AKI, we evaluated the following clinical cases: (i) predicting hyperkalemia after AKI within 14 days of ICU admission, (ii) predicting hyperkalemia within 14 days of ICU admission regardless of AKI status, and compared different lead times for (i) and (ii). Both clinical scenarios were modeled using logistic regression (LR), random forest (RF), and XGBoost. Using observations from the first day in the ICU, our models were able to predict hyperkalemia with an AUC of (i) 0.79, 0.81, 0.81 and (ii) 0.81, 0.85, 0.85 for LR, RF, and XGBoost respectively. We found that 4 out of the top 5 features were consistent across the models. AKI stage was significant in the models that included all patients with or without AKI, but not in the models which only included patients with AKI. This suggests that while AKI is important for hyperkalemia, the specific stage of AKI may not be as important. Our findings require further investigation and confirmation.