Key result
Machine learning and new SV3+SV4 ECG criteria improve LVH detection, achieving ~96% accuracy in males.
Why the study?
The study was conducted to evaluate the impact of age, sex, and hypertension on improving ECG diagnosis of left ventricular hypertrophy.
Does incorporating age, sex, and hypertension into ECG criteria and machine learning models improve the diagnostic accuracy for left ventricular hypertrophy?
Observational (n=159)
Does incorporating age, sex, and hypertension into ECG criteria and machine learning models improve the diagnostic accuracy for left ventricular hypertrophy?
Incorporating clinical variables like age, sex, and hypertension into ECG criteria and machine learning models enhances the diagnostic accuracy for left ventricular hypertrophy.
Answers are grounded in this paper and cite their sources.
Age, sex, and hypertension may refine accuracy of select ECG LVH criteria; leaves open prospective validation of ML models.
OBJECTIVE: To evaluate the impact of age, sex and hypertension to improve ECG diagnosis of Left Ventricular Hypertrophy (LVH). METHODS: The study evaluated 14 different QRS voltage criteria as well as our recently proposed criteria of S in V3 plus S in V4 in a population of 159 patients, of whom 14.5 % had echocardiographic evidence of LVH. Statistical analyses assessed the influence of age, sex, and hypertension on the sensitivity and specificity of each criterion. In addition, a machine learning model was used for enhanced diagnostic accuracy. RESULTS: The new SV3 + SV4 criterion had the highest F1 and AUC scores. Among traditional ECG criteria, the Peguero criterion showed the highest sensitivity (0.438), while Wilson and Mazeloni criteria demonstrated the highest specificity (0.9412). The new SV3 + SV4 criterion with sex-specific cut offs achieves a sensitivity of 0.500 and specificity of 0.809 in females, while in males, sensitivity reached 0.556 with specificity at 0.910. Multiple regression analysis indicated that age, sex, and hypertension significantly improved the diagnostic performance of specific criteria, including Sokolow-Lyon, Romhilt voltage, Murphy, and Grant criteria. However, other criteria were not impacted by considering age, sex, or hypertension. ML analysis improved diagnostic accuracy with clinical variables, with the highest performance in males with the addition of age (accuracy 0.959, sensitivity 0.556, and specificity 1.00). CONCLUSION: Considering age, sex, and hypertension can enhance the diagnostic performance of certain ECG criteria and especially in a ML model for LVH. Findings support a more individualized approach for LVH diagnosis in diverse patient populations.
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Yoosefi et al. (2025) conducted an observational in Left Ventricular Hypertrophy (LVH) (n=159). New SV3 + SV4 ECG criterion and machine learning models incorporating age, sex, and hypertension vs. Traditional ECG criteria without clinical variables was evaluated on Diagnostic accuracy (sensitivity, specificity, F1, AUC) for left ventricular hypertrophy. A new SV3+SV4 ECG criterion with sex-specific cut-offs achieved 0.500 sensitivity and 0.809 specificity in females, while a machine learning model with age reached 0.959 accuracy in males.
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