Machine Learning Prediction of Blood Potassium at Different Time Cutoffs.

Because serum potassium and ECG morphology changes exhibit a well-understood connection, and the timeline of ECG changes can be relatively quick, there is motivation to explore the sensitivity of ML based prediction of serum potassium using 12 lead ECG data with respect to the time between the ECG and potassium readings. We trained a convolutional neural network to classify abnormal (serum potassium above 5 mEq/L) vs normal (serum potassium between 4 and 5 mEq/L) from the ECG alone. We compared training with ECGs and potassium measurements filtered to be within 1 hour, 30 minutes, and 15 minutes of each other. We explored scenarios that both leveraged all available data at each time cutoff as well as restricted data to match training set sizes across the time cutoffs. For each case, we trained five separate instances of our neural network to account for variability. The 1 hour cutoff with all data resulted in an average area under the receiver operator curve (AUC) of 0.850 and a weighted accuracy of 76.3%, 15 minutes resulted in 0.814, 72.5%, and 30 minutes. Truncating the training sets to the same size as the 15 minute cutoff results in comparable average accuracy and AUC for all. Our future studies will continue to explore the performance of ML potassium predictions through investigations of failure cases, identification of biases, and explainability analyses.