| Title: | Predicting left ventricular hypertrophy from the 12-lead electrocardiogram in the UK Biobank imaging study using machine learning |
| Journal: | European Heart Journal - Digital Health |
| Published: | 1 Jun 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37538142/ |
| DOI: | https://doi.org/10.1093/ehjdh/ztad037 |
| URL: | https://academic.oup.com/ehjdh/advance-article-pdf/doi/10.1093/ehjdh/ztad037/50634764/ztad037.pdf |
| Citations: | 4 (4 in last 2 years) as of 8 Aug 2024 |
Publication 11528
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
Aims: Left ventricular hypertrophy (LVH) is an established, independent predictor of cardiovascular disease. Indices derived from the electrocardiogram (ECG) have been used to infer the presence of LVH with limited sensitivity. This study aimed to classify LVH defined by cardiovascular magnetic resonance (CMR) imaging using the 12-lead ECG for cost-effective patient stratification.</p>
Methods and results: We extracted ECG biomarkers with a known physiological association with LVH from the 12-lead ECG of 37 534 participants in the UK Biobank imaging study. Classification models integrating ECG biomarkers and clinical variables were built using logistic regression, support vector machine (SVM) and random forest (RF). The dataset was split into 80% training and 20% test sets for performance evaluation. Ten-fold cross validation was applied with further validation testing performed by separating data based on UK Biobank imaging centres. QRS amplitude and blood pressure (P < 0.001) were the features most strongly associated with LVH. Classification with logistic regression had an accuracy of 81% [sensitivity 70%, specificity 81%, Area under the receiver operator curve (AUC) 0.86], SVM 81% accuracy (sensitivity 72%, specificity 81%, AUC 0.85) and RF 72% accuracy (sensitivity 74%, specificity 72%, AUC 0.83). ECG biomarkers enhanced model performance of all classifiers, compared to using clinical variables alone. Validation testing by UK Biobank imaging centres demonstrated robustness of our models.</p>
Conclusion: A combination of ECG biomarkers and clinical variables were able to predict LVH defined by CMR. Our findings provide support for the ECG as an inexpensive screening tool to risk stratify patients with LVH as a prelude to advanced imaging.</p>
10 Authors
- Hafiz Naderi
- Julia Ramírez
- Stefan van Duijvenboden
- Esmeralda Ruiz Pujadas
- Nay Aung
- Lin Wang
- Choudhary Anwar Ahmed Chahal
- Karim Lekadir
- Steffen E Petersen
- Patricia B Munroe
1 Application
| Application ID | Title |
|---|---|
| 2964 | Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound |
Enabling scientific discoveries that improve human health