Antonio Luiz P Ribeiro

BACKGROUND: Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform. METHODS: The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients. FINDINGS: AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773-0·776; C-indices on external validation datasets 0·638-0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756-0·763; UKB C-index 0·719, 95% CI 0·635-0·803), future atherosclerotic cardiovascular disease (0·696, 0·694-0·698; 0·643, 0·624-0·662), and future heart failure (0·787, 0·785-0·789; 0·768, 0·733-0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome. INTERPRETATION: AIRE 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 for short-term and long-term risk estimation. FUNDING: British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.

ABSTRACT Background Left ventricular (LV) systolic dysfunction is associated with over 8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of electrocardiogram (ECG) signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction. Methods Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale-New Haven Hospital (YNHH) during 2015-2021, we developed a convolutional neural network algorithm to detect LV ejection fraction < 40%. The model was validated within clinical settings at YNHH as well as externally on ECG images from Cedars Sinai Medical Center in Los Angeles, CA, Lake Regional Hospital (LRH) in Osage Beach, MO, Memorial Hermann Southeast Hospital in Houston, TX, and Methodist Cardiology Clinic of San Antonia, TX. In addition, it was validated in the prospective Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Gradient-weighted class activation mapping was used to localize class-discriminating signals in ECG images. Results Overall, 385,601 ECGs with paired echocardiograms were used for model development. The model demonstrated high discrimination power across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROC] 0.91, area under precision-recall curve [AUPRC] 0.55), and external sets of ECG images from Cedars Sinai (AUROC 90, AUPRC 0.53), outpatient YNHH clinics (AUROC 0.94, AUPRC 0.77), LRH (AUROC 0.90, AUPRC 0.88), Memorial Hermann Southeast Hospital (AUROC 0.91, AUPRC 0.88), Methodist Cardiology Clinic (AUROC 0.90, AUPRC 0.74), and ELSA-Brasil cohort (AUROC 0.95, AUPRC 0.45). An ECG suggestive of LV systolic dysfunction portended over 27-fold higher odds of LV systolic dysfunction on TTE (OR 27.5, 95% CI, 22.3-33.9 in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V2-V3), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with LV ejection fraction ≥ 40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (HR 3.9, 95% CI 3.3-4.7, median follow-up 3.2 years). Conclusions We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings. CLINICAL PERSPECTIVE What is New? A convolutional neural network model that accurately identifies LV systolic dysfunction from ECG images across subgroups of age, sex, and race. The model shows robust performance across multiple institutions and health settings, both applied to ECG image databases as well as directly uploaded single ECG images to a web-based application by clinicians. The approach provides information for both screening of LV systolic dysfunction and its risk based on ECG images alone. What are the clinical implications? Our model represents an automated screening strategy for LV systolic dysfunction on a variety of ECG layouts. With availability of ECG images in practice, this approach overcomes implementation challenges of deploying an interoperable screening tool for LV systolic dysfunction in resource-limited settings. This model is available in an online format to facilitate real-time screening for LV systolic dysfunction by clinicians.

Background and Aims: AI-enhanced 12-lead ECG can detect a range of structural heart diseases (SHDs) but has a limited role in community-based screening. We developed and externally validated a noise-resilient single-lead AI-ECG algorithm that can detect SHD and predict the risk of their development using wearable/portable devices. Methods: Using 266,740 ECGs from 99,205 patients with paired echocardiographic data at Yale New Haven Hospital, we developed ADAPT-HEART, a noise-resilient, deep-learning algorithm, to detect SHD using lead I ECG. SHD was defined as a composite of LVEF<40%, moderate or severe left-sided valvular disease, and severe LVH. ADAPT-HEART was validated in four community hospitals in the US, and the population-based cohort of ELSA-Brasil. We assessed the model's performance as a predictive biomarker among those without baseline SHD across hospital-based sites and the UK Biobank. Results: The development population had a median age of 66 [IQR, 54-77] years and included 49,947 (50.3%) women, with 18,896 (19.0%) having any SHD. ADAPT-HEART had an AUROC of 0.879 (95% CI, 0.870-0.888) with good calibration for detecting SHD in the test set, and consistent performance in hospital-based external sites (AUROC: 0.852-0.891) and ELSA-Brasil (AUROC: 0.859). Among those without baseline SHD, high vs. low ADAPT-HEART probability conferred a 2.8- to 5.7-fold increase in the risk of future SHD across data sources (all P<0.05). Conclusions: We propose a novel model that detects and predicts a range of SHDs from noisy single-lead ECGs obtainable on portable/wearable devices, providing a scalable strategy for community-based screening and risk stratification for SHD.