Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

TY - JOUR

T1 - Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

AU - Rogers, Albert J

AU - Selvalingam, Anojan

AU - Alhusseini, Mahmood I

AU - Krummen, David E

AU - Corrado, Cesare

AU - Abuzaid, Firas

AU - Baykaner, Tina

AU - Meyer, Christian

AU - Clopton, Paul

AU - Giles, Wayne

AU - Bailis, Peter

AU - Niederer, Steven

AU - Wang, Paul J

AU - Rappel, Wouter-Jan

AU - Zaharia, Matei

AU - Narayan, Sanjiv M

PY - 2021/1/22

Y1 - 2021/1/22

N2 - 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.

AB - 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.

KW - Action Potentials

KW - Aged

KW - Aged, 80 and over

KW - Cardiomyopathies/diagnosis

KW - Death, Sudden, Cardiac/etiology

KW - Diagnosis, Computer-Assisted

KW - Electrophysiologic Techniques, Cardiac

KW - Female

KW - Humans

KW - Male

KW - Middle Aged

KW - Myocardial Infarction/complications

KW - Neural Networks, Computer

KW - Phenotype

KW - Predictive Value of Tests

KW - Prognosis

KW - Prospective Studies

KW - Risk Assessment

KW - Risk Factors

KW - Signal Processing, Computer-Assisted

KW - Support Vector Machine

KW - Tachycardia, Ventricular/diagnosis

KW - Time Factors

KW - Ventricular Fibrillation/diagnosis

U2 - 10.1161/CIRCRESAHA.120.317345

DO - 10.1161/CIRCRESAHA.120.317345

M3 - SCORING: Journal article

C2 - 33167779

VL - 128

SP - 172

EP - 184

JO - CIRC RES

JF - CIRC RES

SN - 0009-7330

IS - 2

ER -