Libor Pastika

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.

Importance: Hypertension underpins significant global morbidity and mortality. Early lifestyle intervention and treatment are effective in reducing adverse outcomes. Artificial intelligence-enhanced electrocardiography (AI-ECG) has been shown to identify a broad spectrum of subclinical disease and may be useful for predicting incident hypertension. Objective: To develop an AI-ECG risk estimator (AIRE) to predict incident hypertension (AIRE-HTN) and stratify risk for hypertension-associated adverse outcomes. Design, Setting, and Participants: This was a development and external validation prognostic cohort study conducted at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Massachusetts, a secondary care setting. External validation was conducted in the UK Biobank (UKB), a UK-based volunteer cohort. AIRE-HTN was trained and tested to predict incident hypertension using routinely collected ECGs from patients at BIDMC between 2014 and 2023. The algorithm was then evaluated to risk stratify patients for hypertension- associated adverse outcomes and externally validated on UKB data between 2014 and 2022 for both incident hypertension and risk stratification. Main Outcomes and Measures: AIRE-HTN, which uses a residual convolutional neural network architecture with a discrete-time survival loss function, was trained to predict incident hypertension. Results: AIRE-HTN was trained on 1 163 401 ECGs from 189 539 patients (mean [SD] age, 57.7 [18.7] years; 98 747 female [52.1%]) at BIDMC. A total of 19 423 BIDMC patients composed the test set and were evaluated for incident hypertension. From the UKB, AIRE-HTN was tested on 65 610 ECGs from same number of participants (mean [SD] age, 65.4 [7.9] years; 33 785 female [51.5%]). A total of 35 806 UKB patients were evaluated for incident hypertension. AIRE-HTN predicted incident hypertension (BIDMC: n = 6446 [33%] events; C index, 0.70; 95% CI, 0.69-0.71; UKB: n = 1532 [4%] events; C index, 0.70; 95% CI, 0.69-0.71). Performance was maintained in individuals without left ventricular hypertrophy and those with normal ECGs (C indices, 0.67-0.72). AIRE-HTN was significantly additive to existing clinical risk factors in predicting incident hypertension (continuous net reclassification index, BIDMC: 0.44; 95% CI, 0.33-0.53; UKB: 0.32; 95% CI, 0.23-0.37). In adjusted Cox models, AIRE-HTN score was an independent predictor of cardiovascular death (hazard ratio [HR] per standard deviation, 2.24; 95% CI, 1.67-3.00) and stratified risk for heart failure (HR, 2.60; 95% CI, 2.22-3.04), myocardial infarction (HR, 3.13; 95% CI, 2.55-3.83), ischemic stroke (HR, 1.23; 95% CI, 1.11-1.37), and chronic kidney disease (HR, 1.89; 95% CI, 1.68-2.12), beyond traditional risk factors. Conclusions and Relevance: Results suggest that AIRE-HTN, an AI-ECG model, can predict incident hypertension and identify patients at risk of hypertension-related adverse events, beyond conventional clinical risk factors.

BACKGROUND: Females are typically underserved in cardiovascular medicine. The use of sex as a dichotomous variable for risk stratification fails to capture the heterogeneity of risk within each sex. We aimed to develop an artificial intelligence-enhanced electrocardiography (AI-ECG) model to investigate sex-specific cardiovascular risk. METHODS: In this retrospective cohort study, we trained a convolutional neural network to classify sex using the 12-lead electrocardiogram (ECG). The Beth Israel Deaconess Medical Center (BIDMC) secondary care dataset, comprising data from individuals who had clinically indicated ECGs performed in a hospital setting in Boston, MA, USA collected between May, 2000, and March, 2023, was the derivation cohort (1 163 401 ECGs). 50% of this dataset was used for model training, 10% for validation, and 40% for testing. External validation was performed using the UK Biobank cohort, comprising data from volunteers aged 40-69 years at the time of enrolment in 2006-10 (42 386 ECGs). We examined the difference between AI-ECG-predicted sex (continuous) and biological sex (dichotomous), termed sex discordance score. FINDINGS: AI-ECG accurately identified sex (area under the receiver operating characteristic 0·943 [95% CI 0·942-0·943] for BIDMC and 0·971 [0·969-0·972] for the UK Biobank). In BIDMC outpatients with normal ECGs, an increased sex discordance score was associated with covariate-adjusted increased risk of cardiovascular death in females (hazard ratio [HR] 1·78 [95% CI 1·18-2·70], p=0·006) but not males (1·00 [0·63-1·58], p=0·996). In the UK Biobank cohort, the same pattern was seen (HR 1·33 [95% CI 1·06-1·68] for females, p=0·015; 0·98 [0·80-1·20] for males, p=0·854). Females with a higher sex discordance score were more likely to have future heart failure or myocardial infarction in the BIDMC cohort and had more male cardiac (increased left ventricular mass and chamber volumes) and non-cardiac phenotypes (increased muscle mass and reduced body fat percentage) in both cohorts. INTERPRETATION: Sex discordance score is a novel AI-ECG biomarker capable of identifying females with disproportionately elevated cardiovascular risk. AI-ECG has the potential to identify female patients who could benefit from enhanced risk factor modification or surveillance. FUNDING: British Heart Foundation.

Abstract 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 R 2 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.

Abstract Aims Most artificial intelligence-enhanced electrocardiogram (AI-ECG) models used to predict adverse events including death require that the ECGs be stored digitally. However, the majority of clinical facilities worldwide store ECGs as images. Methods and results A total of 1 163 401 ECGs (189 539 patients) from a secondary care data set were available as both natively digital traces and PDF images. A digitization pipeline extracted signals from PDFs. Separate 1D convolutional neural network (CNN) models were trained on natively digital or digitized ECGs, with a discrete-time survival loss function to predict time to mortality. A 2D CNN model was trained on 310 × 868 px ECG images. External validation was performed in 958 954 ECGs (645 373 patients) from a Brazilian primary care cohort and 1022 ECGs (1022 patients) from a Chagas disease cohort. The image 2D CNN model and digitized 1D CNN model performed comparably to natively digital 1D CNN model in internal [C-index 0.780 (0.779–0.781), 0.772 (0.771–0.774), and 0.775 (0.774–0.776), respectively] and external validation. Models trained on natively digital 1D ECGs had comparable performance when applied to digitized 1D ECGs [C-index 0.773 (0.771–0.774)]. Conclusion Both the image 2D CNN and digitized 1D CNN enable mortality prediction from ECG images, with comparable performance to natively digital 1D CNN. Models trained on natively digital 1D ECGs can also be applied to digitized 1D ECGs, without any significant loss in performance. This work allows AI-ECG mortality prediction to be applied in diverse global settings lacking digital ECG infrastructure.