Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease.

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.

Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients.

Aims: Implantable cardioverter defibrillator (ICD) therapies have been associated with increased mortality and should be minimized when safe to do so. We hypothesized that machine learning-derived ventricular tachycardia (VT) cycle length (CL) variability metrics could be used to discriminate between sustained and spontaneously terminating VT. Methods and results: In this single-centre retrospective study, we analysed data from 69 VT episodes stored on ICDs from 27 patients (36 spontaneously terminating VT, 33 sustained VT). Several VT CL parameters including heart rate variability metrics were calculated. Additionally, a first order auto-regression model was fitted using the first 10 CLs. Using features derived from the first 10 CLs, a random forest classifier was used to predict VT termination. Sustained VT episodes had more stable CLs. Using data from the first 10 CLs only, there was greater CL variability in the spontaneously terminating episodes (mean of standard deviation of first 10 CLs: 20.1 ± 8.9 vs. 11.5 ± 7.8 ms, P < 0.0001). The auto-regression coefficient was significantly greater in spontaneously terminating episodes (mean auto-regression coefficient 0.39 ± 0.32 vs. 0.14 ± 0.39, P < 0.005). A random forest classifier with six features yielded an accuracy of 0.77 (95% confidence interval 0.67 to 0.87) for prediction of VT termination. Conclusion: Ventricular tachycardia CL variability and instability are associated with spontaneously terminating VT and can be used to predict spontaneous VT termination. Given the harmful effects of unnecessary ICD shocks, this machine learning model could be incorporated into ICD algorithms to defer therapies for episodes of VT that are likely to self-terminate.

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study.

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.