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Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability by Kwak, Gloria Hyunjung; Chen, Christina; Ling, Lowell; Ghosh, Erina; Celi, Leo Anthony; Hui, Pan is a scholarly article available to read on EtoBox.

What is Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability about?

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

Author

Kwak, Gloria Hyunjung; Chen, Christina; Ling, Lowell; Ghosh, Erina; Celi, Leo Anthony; Hui, Pan

Published

2021

Language

EN