AI-powered ECG model predicts heart disease risk with precision

By Hugo Francisco de SouzaReviewed by Susha Cheriyedath, M.Sc.Nov 5 2024

Developed using data from diverse patient groups, AIRE’s advanced AI predicts heart disease risk and mortality with precision, giving clinicians tools for more targeted, long-term patient care.

Study: Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study. Image Credit: Shutterstock AI

In a recent study published in the journal The Lancet, researchers developed and validated a novel artificial intelligence (AI)-enhanced electrocardiography (ECG) model capable of leveraging patients’ medical histories and imaging results to accurately predict mortality and cardiovascular disease (CVD) risk.

While not the first attempt to use AI in disease and mortality prediction, this implementation overcomes previous models’ limitations of temporality, biological plausibility, and explainability, enabling it to generate predictions that can support actionable insights in clinical practice.

Study findings revealed that the novel model (named ‘AIRE’) can accurately predict all-cause mortality, ventricular arrhythmia, atherosclerotic CVD, and heart failure risk.

It surpassed conventional AI models in computing both short- and long-term risk estimations, providing clinicians with insights for short-term, single-time point diagnostic predictions and suggesting long-term, progressive interventions for the remainder of the patient’s pharmacological support.

Background

Electrocardiograms (ECGs) are non-invasive, graphical evaluations of cardiovascular electrical activity. The technique involves using external electrodes strategically placed at specific locations on patients’ chest, arms, and legs, providing clinicians with visual representations of heart electrical signals and rhythms.

ECGs have been routine in cardiovascular evaluations and have remained almost methodologically unchanged for over 100 years.

Recent advances in computer processing capabilities and the advent of next-generation predictive machine learning (ML) models have sparked excitement in the research community.

Since 2020, a handful of studies have attempted to utilize ECG-data-trained artificial intelligence (AI) models to provide predictions on patients’ CVD and mortality risk, highlighting model performance – in almost every implementation of AI in disease/mortality risk prediction, AI models achieve diagnostic and predictive performance comparable to, or exceeding human expert predictions.

AI models thus have the potential to lessen patient burdens on clinicians (geographically determined number of individuals per number of doctors), particularly in rural and underdeveloped areas, while hastening diagnostic speed and reducing the financial burden on patients themselves.

Unfortunately, despite their clinical-trial-based safety and performance validations, AI-enhanced ECG models are rarely utilized in real-world ECG applications.

“Existing mortality prediction models are limited by predicting survival at one or a small number of set timepoints and do not provide information on specific actionable pathways. A high-risk prediction is unhelpful to a clinician if there is no accompanying information on how to improve the survival trajectory of their patient. Thus, making AI-ECG predictions more actionable requires considering time-to-event predictions and specific predictions for diseases with established preventive and disease-modifying treatments.”

From the research standpoint, while accurate, previous AI implementations provided insufficient explanations of model performance (a computational ‘black box’) and biological plausibility, leading clinicians to hesitate to trust model predictions.

About the Study

In the present study, researchers develop, train, and validate eight novel AI-ECG risk estimation (AIRE) models (collectively referred to as the ‘AIRE platform’) aimed at predicting mortality risk (all-cause and cardiovascular) without the limitations of previous AI implementations.

Study data was obtained from five geographically diverse sources receiving minimally overlapping clinical care. These include the Beth Israel Deaconess Medical Center (BIDMC) cohort (secondary patient care dataset), the São Paulo-Minas Gerais Tropical Medicine Research Center (SaMi-Trop) cohort (chronic Chagas cardiomyopathy dataset), the Longitudinal Study of Adult Health (ELSA-Brasil) cohort (public servants), and the United Kingdom (UK) BioBank (UKB) cohort (volunteers). The Clinical Outcomes in Digital Electrocardiography (CODE) cohort was additionally used to fine-tune model performance.

AI model development was carried out using the BIDMC cohort for model derivation. The dataset was randomly divided into training (50%), validation (10%), and 40% for internal testing.

Residual block-based convolutional neural network architectures allowed researchers to incorporate a discrete-time survival approach, creating patient-specific survival curves that account for both participant mortality and censorship (follow-up inability).

CODE cohort data-associated model improvements involved using 75% of the dataset for model parameter fine-tuning, 5% for generalized (external) validation, and 20% for internal primary care validation.

Additionally, five other models focusing on CV death (AIRE-CV death), non-CV death (AIRE-NCV death), atherosclerotic cardiovascular disease (AIRE-ASCVD), ventricular arrhythmia (AIRE-VA), and heart failure (AIRE-HF) were derived using similar approaches.

Statistical analyses were used to measure model performance, particularly compared with human expert perceptions and the Stanford Estimator of ECG Risk (SEER). Cox models (adjusted for demographics, clinical data, and imaging parameters) and Kaplan-Meier curves were employed to compute differential model accuracy. Biological plausibility was explained using phenome-wide association studies (PheWAS) and genome-wide association studies (GWAS) to identify relevant cardiac and metabolic markers.

Study Findings

Hold-out test results revealed that AIRE could predict all-cause mortality with concordance values = 0.775. Notably, the platform was observed to outperform conventional risk factor predictors (cumulative C-index = 0.759) across both holistic (AIRE Cox C-index = 0.794) and cardiovascular death predictions (C-index = 0.844), highlighting model accuracy.

Notably, AIRE was capable of accurately predicting heart failure events in participants without a personal or family history of CVD, which is especially relevant as conventional diagnoses in these populations are typically delayed.

Encouragingly, AIRE results remained robust even when provided single-lead ECG data (from consumer devices; clinical ECG devices use between 8-12 leads), highlighting the platform’s application in stay-at-home CVD risk monitoring.

PheWAS and GWAS analyses revealed that the model provided sufficient biological plausibility, explaining that surrogate pulmonary pressure measures and ventricular diameter inversely correlated with predicted survival, while the left ventricular ejection fraction (LVEF) demonstrated a direct correlation.

Conclusions

The present study develops and validates the most clinically practical AI-enhanced ECG evaluation platform currently available – the AIRE platform.

Study findings revealed that the platform outperforms conventional human-based predictions and similar older-generation AI models in predictive accuracy without the latter’s need for demographic or medical history data.

Notably, the model remained robust even when provided with single-lead data from consumer devices, highlighting AIRE’s potential for remote patient monitoring, particularly among those without medical CVD histories or those in remote areas without adequate clinical support.

“…the AIRE platform is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts, including primary and secondary care, for short-term and long-term risk prediction at population and disease-specific levels.”