BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis

Stabenau, Hans Friedrich; Waks, Jonathan W.

doi:10.1016/j.cmpb.2023.107798

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…In order to understand the ECG morphologies associated with predicted survival we used two approaches. Median beats were extracted using the BRAVEHEART ECG analysis software as previously described (22).…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence–enabled electrocardiogram for mortality and cardiovascular risk estimation: An actionable, explainable and biologically plausible platform

Sau,

Pastika,

Sieliwonczyk

et al. 2024

Preprint

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Background and Aims Artificial intelligence-enhanced electrocardiograms (AI-ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions lack actionability at an individual patient level, explainability and biological plausibility. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform. Methods and Results The AIRE platform was developed in a secondary care dataset of 1,163,401 ECGs from 189,539 patients, using deep learning with a discrete-time survival model to create a subject-specific survival curve using a single ECG. Therefore, AIRE predicts not only risk of mortality, but time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil and the UK, including volunteers, primary care and secondary care subjects. AIRE accurately predicts risk of all-cause mortality (C-index 0.775 (0.773-0.776)), cardiovascular (CV) death 0.832 (0.831-0.834), non-CV death (0.749 (0.747-0.751)), future ventricular arrhythmia (0.760 (0.756-0.763)), future atherosclerotic cardiovascular disease (0.696 (0.694-0.698)) and future heart failure (0.787 (0.785-0.889))). Through phenome- 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 aging and metabolic syndrome. Conclusion 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- and long-term risk estimation.

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Section: Methodsmentioning

confidence: 99%

Artificial intelligence–enabled electrocardiogram for mortality and cardiovascular risk estimation: An actionable, explainable and biologically plausible platform

Sau,

Pastika,

Sieliwonczyk

et al. 2024

Preprint

Self Cite

View full text Add to dashboard Cite

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“…To understand the ECG morphologies associated with the AI-ECG BMI predictions, we trained a VAE, as previously described 73 , using median ECG beats. Median beats were extracted using the BRAVEHEART software, as previously described 74 . The VAE was based on a convolutional encoder/decoder architecture, which was inspired by architectures previously used for ECG analysis 75 , 76 .…”

Section: Methodsmentioning

confidence: 99%

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

Pastika,

Sau,

Patlatzoglou

et al. 2024

npj Digit. Med.

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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.

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The spatial ventricular gradient is associated with inducibility of ventricular arrhythmias during electrophysiology study

Isaza,

Stabenau,

Kramer

et al. 2024

Heart Rhythm

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BRAVEHEART: Open-source software for automated electrocardiographic and vectorcardiographic analysis

Cited by 6 publications

References 24 publications

Artificial intelligence–enabled electrocardiogram for mortality and cardiovascular risk estimation: An actionable, explainable and biologically plausible platform

Artificial intelligence–enabled electrocardiogram for mortality and cardiovascular risk estimation: An actionable, explainable and biologically plausible platform

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

The spatial ventricular gradient is associated with inducibility of ventricular arrhythmias during electrophysiology study

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