Key result
Advanced 12-lead ECG score accurately identifies diastolic dysfunction with an AUC of 0.91.
Why the study?
Early detection of diastolic dysfunction by electrocardiography is valuable but methods for accurate and explainable identification are lacking.
Does an explainable advanced ECG score accurately detect diastolic dysfunction and predict cardiovascular events in patients with normal systolic function?
Cross-Sectional (n=414)
No
Does an explainable advanced ECG score accurately detect diastolic dysfunction and predict cardiovascular events in patients with normal systolic function?
Effect estimate: AUC 0.91 (95% CI 0.88-0.94)
A novel machine learning-derived advanced ECG score can accurately detect diastolic dysfunction and strongly predicts future cardiovascular events.
May aid noninvasive detection of diastolic dysfunction; leaves open clinical utility and event prediction in preserved systolic function.
BACKGROUND AND AIMS Diastolic dysfunction is a precursor to heart failure with preserved ejection fraction (HFpEF), and early detection by electrocardiography (ECG) would be valuable. We hypothesised that an explainable advanced ECG (A-ECG) score could accurately detect diastolic dysfunction with clinically meaningful diagnostic and prognostic performance. METHODS A derivation cohort was included after standard 12-lead ECG and echocardiography demonstrating normal systolic function, and either the presence or absence of diastolic dysfunction (≥grade 2, 2025 guidelines). A multivariable machine learning A-ECG diastolic dysfunction score (0-100%, positive score defined as >50%) was derived using elastic net with nested resampling. Performance for identifying diastolic dysfunction was assessed in a separate external validation cohort. Prognostic performance for predicting cardiovascular events including mortality was evaluated in the UK Biobank. RESULTS A 7-measure A-ECG diastolic dysfunction score derived in the derivation cohort (n=414) performed excellently in the validation cohort (n=3,418, area under the receiver operating characteristics curve [95% confidence interval] 0.87 [0.86-0.88]). Survival analysis using spline modelling adjusted for age, sex, and cardiovascular risk factors in the UK Biobank (n=27,239, 966 events, follow-up 1.9 [0.7–4.4] years, age 66±8 years, 50% female) showed that over the range of the score (0-100%), the hazard ratio for events increased linearly to 4 (p<0.001) CONCLUSIONS Explainable A-ECG analysis of the standard 12-lead ECG can be used to accurately identify diastolic dysfunction. The score reflecting the continuous probability (0-100%) of having diastolic dysfunction has a linearly increasing and strong adjusted prognostic association with cardiovascular events.
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McColl et al. (2023) conducted a cross-sectional in Diastolic dysfunction (n=414). 6-measure advanced electrocardiography (A-ECG) score vs. Echocardiography (reference standard) was evaluated on Identification of diastolic dysfunction (AUC) (AUC 0.91, 95% CI 0.88-0.94). A 6-measure advanced electrocardiography score derived from a standard 12-lead ECG accurately identified diastolic dysfunction with an area under the receiver operating characteristics curve of 0.91.
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