Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death
- 10 November 2020
- journal article
- research article
- Published by Ovid Technologies (Wolters Kluwer Health) in Circulation Research
- Vol. 128 (2), 172-184
- https://doi.org/10.1161/circresaha.120.317345
Abstract
Download figure Download PowerPoint Rationale: Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside. Objective: To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes. Methods and Results: We recorded 5706 ventricular MAPs in 42 patients with coronary artery disease and left ventricular ejection fraction ≤40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines and convolutional neural networks were trained to 2 end points: (1) sustained VT/VF or (2) mortality at 3 years. Support vector machines provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each end point. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI, 0.76–1.00) and 0.91 for mortality (95% CI, 0.83–1.00) and were the most significant multivariate predictors. Interpreting trained support vector machine revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium-calcium exchanger as predominant phenotypes for VT/VF. Conclusions: Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.Keywords
Funding Information
- HHS | National Institutes of Health (F32 HL144101)
- HHS | National Institutes of Health (K23 HL145017)
- HHS | National Institutes of Health (R21 HL145500)
- HHS | National Institutes of Health (R01 HL83359)
- HHS | NIH | National Heart, Lung, and Blood Institute (R01 HL149134)
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