A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes

Joshua Lampert; Akhil Vaid; William Whang; Jacob Koruth; Marc A Miller; Marie-Noelle Langan; Daniel Musikantow; Mohit Turagam; Abhishek Maan; Iwanari Kawamura; Srinivas Dukkipati; Girish N Nadkarni; Vivek Y Reddy

doi:10.1016/j.jacep.2023.05.025

A Novel ECG-Based Deep Learning Algorithm to Predict Cardiomyopathy in Patients With Premature Ventricular Complexes

JACC Clin Electrophysiol. 2023 Aug;9(8 Pt 2):1437-1451. doi: 10.1016/j.jacep.2023.05.025. Epub 2023 Jul 19.

Affiliations

¹ Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA. Electronic address: joshuamlampert@gmail.com.
² The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
³ Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
⁴ The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Division of Data Driven and Digital Medicine (D3M), The Charles Bronfman Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.

PMID: 37480862
DOI: 10.1016/j.jacep.2023.05.025

Abstract

Background: Premature ventricular complexes (PVCs) are prevalent and, although often benign, they may lead to PVC-induced cardiomyopathy. We created a deep-learning algorithm to predict left ventricular ejection fraction (LVEF) reduction in patients with PVCs from a 12-lead electrocardiogram (ECG).

Objectives: This study aims to assess a deep-learning model to predict cardiomyopathy among patients with PVCs.

Methods: We used electronic medical records from 5 hospitals and identified ECGs from adults with documented PVCs. Internal training and testing were performed at one hospital. External validation was performed with the others. The primary outcome was first diagnosis of LVEF ≤40% within 6 months. The dataset included 383,514 ECGs, of which 14,241 remained for analysis. We analyzed area under the receiver operating curves and explainability plots for representative patients, algorithm prediction, PVC burden, and demographics in a multivariable Cox model to assess independent predictors for cardiomyopathy.

Results: Among the 14,241-patient cohort (age 67.6 ± 14.8 years; female 43.8%; White 29.5%, Black 8.6%, Hispanic 6.5%, Asian 2.2%), 22.9% experienced reductions in LVEF to ≤40% within 6 months. The model predicted reductions in LVEF to ≤40% with area under the receiver operating curve of 0.79 (95% CI: 0.77-0.81). The gradient weighted class activation map explainability framework highlighted the sinus rhythm QRS complex-ST segment. In patients who underwent successful PVC ablation there was a post-ablation improvement in LVEF with resolution of cardiomyopathy in most (89%) patients.

Conclusions: Deep-learning on the 12-lead ECG alone can accurately predict new-onset cardiomyopathy in patients with PVCs independent of PVC burden. Model prediction performed well across sex and race, relying on the QRS complex/ST-segment in sinus rhythm, not PVC morphology.

Keywords: cardiomyopathy; machine learning; premature ventricular contraction.

MeSH terms

Adult
Aged
Aged, 80 and over
Algorithms
Cardiomyopathies* / complications
Cardiomyopathies* / diagnosis
Deep Learning*
Electrocardiography
Female
Humans
Middle Aged
Stroke Volume
Ventricular Function, Left
Ventricular Premature Complexes* / diagnosis
Ventricular Premature Complexes* / surgery