  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.
