Key result
Cardiac Hypertrophy Computer-based model detects echocardiographic LVH with ~71% accuracy, matching state-of-the-art criteria.
Why the study?
Left ventricular hypertrophy detected by echocardiography predicts mortality, and machine learning applied to comprehensive computer-based ECG data could establish new criteria to detect it.
Does a machine learning-based ECG model (CHCM) improve the detection of echocardiographic left ventricular hypertrophy compared to standard ECG criteria?
Observational (n=595)
Does a machine learning-based ECG model (CHCM) improve the detection of echocardiographic left ventricular hypertrophy compared to standard ECG criteria?
A novel machine learning-based ECG model (CHCM) provides comparable or slightly improved accuracy for detecting echocardiographic left ventricular hypertrophy compared to traditional ECG criteria.
BACKGROUND: Left ventricular hypertrophy detected by echocardiography (Echo-LVH) is an independent predictor of mortality. Integration of the Philips DXL-16 algorithm into the electrocardiogram (ECG) extensively analyses the electricity of the heart. Machine learning techniques such as the C5.0 could lead to a new decision tree criterion to detect Echo-LVH. OBJECTIVES: To search for a new combination of ECG parameters predictive of Echo-LVH. The final model is called the Cardiac Hypertrophy Computer-based model (CHCM). METHODS: We extracted the 458 ECG parameters provided by the Philips DXL-16 algorithm in patients with Echo-LVH and controls. We used the C5.0 ML algorithm to train, test, and validate the CHCM. We compared its diagnostic performance to validate state-of-the-art criteria in our patient cohort. RESULTS: We included 439 patients and considered an alpha value of 0.05 and a power of 99%. The CHCM includes T voltage in I (≤0.055 mV), peak-to-peak QRS distance in aVL (>1.235 mV), and peak-to-peak QRS distance in aVF (>0.178 mV). The CHCM had an accuracy of 70.5% (CI95%, 65.2-75.5), a sensitivity of 74.3%, and a specificity of 68.7%. In the external validation cohort (n = 156), the CHCM had an accuracy of 63.5% (CI95%, 55.4-71), a sensitivity of 42%, and a specificity of 82.9%. The accuracies of the most relevant state-of-the-art criteria were: Romhilt-Estes (57.4%, CI95% 49-65.5), VDP Cornell (55.7%, CI95%47.6-63.7), Cornell (59%, CI95%50.8-66.8), Dalfó (62.9%, CI95%54.7-70.6), Sokolow Lyon (53.9%, CI95%45.7-61.9), and Philips DXL-16 algorithm (54.5%, CI95%46.3-62.5). CONCLUSION: ECG computer-based data and the C5.0 determined a new set of ECG parameters to predict Echo-LVH. The CHCM classifies patients as Echo-LVH with repolarization abnormalities or LVH with increased voltage. The CHCM has a similar accuracy, and is slightly more sensitive than the state-of-the-art criteria.
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Garza‐Salazar et al. (2021) conducted an observational in Left ventricular hypertrophy (n=595). Cardiac Hypertrophy Computer-based model (CHCM) vs. State-of-the-art ECG criteria was evaluated on Diagnostic accuracy for detecting echocardiographic left ventricular hypertrophy (95% CI 65.2-75.5). The Cardiac Hypertrophy Computer-based model detected echocardiographic left ventricular hypertrophy with 70.5% accuracy (95% CI 65.2-75.5), performing similarly to state-of-the-art criteria.
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