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Investigating the Pathogenesis of MYH7 Mutation Gly823Glu in Familial Hypertrophic Cardiomyopathy using a Mouse Model

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Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death.

Albert J Rogers¹, Anojan Selvalingam^1,2, Mahmood I Alhusseini¹

¹Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford University.

Circulation Research

|November 10, 2020

Summary

This summary is machine-generated.

Predicting ventricular tachycardia/fibrillation (VT/VF) and mortality in ischemic cardiomyopathy is challenging. Machine learning of ventricular action potentials identified novel computational phenotypes, improving long-term outcome prediction.

Keywords:

artificial intelligence coronary disease death, sudden, cardiac heart failure ion channels systems biology

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Area of Science:

Cardiology
Computational Biology
Machine Learning

Background:

Predicting ventricular tachycardia/fibrillation (VT/VF) and mortality in ischemic cardiomyopathy is clinically difficult.
Existing clinical tools and cellular mechanism translations have limitations in predicting patient outcomes.

Purpose of the Study:

To develop computational phenotypes for patients with ischemic cardiomyopathy.
To train and interpret machine learning models using ventricular monophasic action potentials (MAPs) to predict long-term outcomes.

Main Methods:

Recorded 5706 ventricular MAPs from 42 patients with coronary artery disease and low ejection fraction.
Utilized Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) trained on VT/VF and 3-year mortality endpoints.
Employed 10-fold cross-validation and independent testing cohorts for model validation.

Main Results:

SVM models achieved superior classification performance.
Patient-level predictions demonstrated high accuracy: c-statistics of 0.90 for VT/VF and 0.91 for mortality.
Interpreting SVM models identified specific MAP morphologies linked to L-type calcium current and sodium-calcium exchanger activity.

Conclusions:

Machine learning analysis of action potential recordings reveals novel phenotypes for predicting outcomes in ischemic cardiomyopathy.
Computational phenotypes offer a new approach to link cellular mechanisms with clinical outcomes.
This methodology holds potential for application in other cardiovascular conditions.