Key result
Machine learning accurately predicts extracellular potassium and intracellular pH from simulated ischemic action potentials.
Why the study?
Although hyperkalemia, acidosis, and ATP depletion alter cardiomyocyte electrophysiology during myocardial ischemia, it is difficult to discern how much each condition individually contributes to changes in the action potential.
A machine learning framework can accurately deconvolute the distinct electrophysiological signatures of hyperkalemia and acidosis from a single simulated ischemic action potential, offering potential for in silico drug screening.
Background Myocardial ischemia is a dynamic, complex process characterized by hyperkalemia, acidosis, and ATP depletion. While these three conditions alter cardiomyocyte electrophysiology, it is difficult to discern how much each one individually contributes to the resulting changes in action potential (AP). In this study, we test whether machine learning can deconvolute these distinct ischemic patterns within a single AP. Methods We developed a multi-target regression model trained on data generated by the Luo-Rudy (1991) computational model of a ventricular cardiomyocyte, simulating a wide range of ischemic conditions. The model was designed to predict two continuous variables: extracellular potassium concentration ([K + ]o) and intracellular pH (pHi). Results The model achieved high accuracy on a held-out test set, with mean squared errors (MSE) below 0.25 for [K + ]o and below 0.01 for pHi. To further generalize this model, we applied this trained model to a structurally distinct model, the Ten Tusscher (2006) framework. We were able to accurately predict [K + ]o and pHi from APs, demonstrating that the learned principles are robust. A feature importance analysis revealed that resting membrane potential (RMP) was the strongest predictor for [K + ]o, while action potential duration (APD) is most important for predicting pHi, underscoring these distinct cardiomyocyte electrophysiological patterns Conclusions Our approach can distinguish distinct ischemic drivers and has potential for in silico drug screening and mechanistic analysis.
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Ahmad et al. (2025) studied Myocardial ischemia (n=200). Machine learning multi-target regression framework was evaluated on Mean squared error (MSE) for predicting extracellular potassium concentration ([K+]o) and intracellular pH (pHi). A machine learning framework accurately predicted extracellular potassium concentration (MSE < 0.25) and intracellular pH (MSE < 0.01) from simulated ischemic cardiomyocyte action potentials.
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