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Diagnostic and prognostic value of ECG-predicted hypertension-mediated left ventricular hypertrophy using machine
Hafiz Naderi1,2,3, Julia Ramírez1,4,5, Stefan Van Duijvenboden1,6
1William Harvey Research Institute, Queen Mary University of London, Charterhouse Square.
Insights
Machine learning models using electrocardiograms (ECG) can classify hypertension-mediated left ventricular hypertrophy (LVH) phenotypes. This ECG-based approach aids in early detection and risk stratification for cardiovascular outcomes.
Area of Science:
- Cardiology
- Medical Informatics
- Biomedical Engineering
Background:
- Hypertension-induced left ventricular hypertrophy (LVH) presents diverse phenotypes with distinct prognoses.
- Cardiac magnetic resonance (CMR) identifies these LVH phenotypes but is not widely accessible.
- Electrocardiogram (ECG) is a routine diagnostic tool, yet its ability to differentiate LVH phenotypes remains unclear.
Purpose of the Study:
- To develop and validate machine learning (ML) models for classifying hypertension-mediated LVH phenotypes using ECG data.
- To assess the association of ECG-predicted LVH phenotypes with incident cardiovascular outcomes.
Main Methods:
- Utilized ECG biomarkers from 20,439 hypertensive patients in the UK Biobank (UKB).
- Developed classification models (logistic regression, SVM, random forest) integrating ECG and clinical data.
- Externally validated models in the Study of Health in Pomerania (SHIP) cohort and assessed associations with major adverse cardiovascular events (MACE) and heart failure.
Main Results:
- Machine learning models demonstrated comparable classification performance, with SVM achieving an accuracy of 0.79.
- The models showed superior prediction for eccentric LVH.
- ECG-predicted eccentric LVH was significantly associated with an increased risk of heart failure in the UKB test set.
Conclusions:
- ECG-based ML classifiers offer a promising, accessible screening strategy for early detection of hypertension-mediated LVH phenotypes.
- This approach can aid in identifying patients at higher risk for adverse cardiovascular events.
Objective:
Four hypertension-mediated left ventricular hypertrophy (LVH) phenotypes have been reported using cardiac magnetic resonance (CMR): normal LV, LV remodelling, eccentric and concentric LVH, with varying prognostic implications. The electrocardiogram (ECG) is routinely used to detect LVH; however, its capacity to differentiate between LVH phenotypes is unknown. This study aimed to classify hypertension-mediated LVH from the ECG using machine learning and test for associations of ECG-predicted phenotypes with incident cardiovascular outcomes.
Methods:
ECG biomarkers were extracted from the 12-lead ECG of 20 439 hypertensive patients in UK Biobank (UKB). Classification models integrating ECG and clinical variables were built using logistic regression, support vector machine (SVM), and random forest. The models were trained in 80% of the participants, and the remaining 20% formed the test set. External validation was sought in 877 hypertensive participants from the Study of Health in Pomerania (SHIP). In the UKB test set, we tested for associations between ECG-predicted LVH phenotypes and incident major adverse cardiovascular events (MACE) and heart failure.
Results:
Among UKB participants 19 408 had normal LV, 758 LV remodelling, 181 eccentric and 92 concentric LVH. Classification performance of the three models was comparable in UKB. SVM (accuracy 0.79, sensitivity 0.59, specificity 0.87, AUC 0.69) was taken forward for external validation with similar results in SHIP. There was superior prediction of eccentric LVH in both cohorts. In the UKB test set, ECG-predicted eccentric LVH was associated with heart failure (hazard ratio 3.42, 95% CI 1.06-9.86).
Conclusion:
ECG-based ML classifiers represent a potentially accessible screening strategy for the early detection of hypertension-mediated LVH phenotypes.
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