Integrating ECG Into the Mayo Score Enhances Genotype Prediction in Hypertrophic Cardiomyopathy.

Area of Science:

• Cardiology
• Genetics
• Medical Diagnostics

Background:

• Genetic testing is vital for hypertrophic cardiomyopathy (HCM) management but faces accessibility challenges.
• Current prediction models like the Mayo HCM Genotype Predictor Score lack electrophysiological data.
• Integrating electrocardiogram (ECG) parameters may improve genotype prediction accuracy in HCM.

Purpose of the Study:

• To develop and validate an enhanced genotype prediction model for HCM by incorporating ECG parameters.
• To compare the performance of the novel ECG-integrated model against the existing Mayo HCM Genotype Predictor Score.

Main Methods:

• Retrospective analysis of 466 Japanese HCM patients.
• Identification of significant ECG variables using multivariable logistic regression and bootstrap aggregation.
• Development and internal validation of a novel point-based score (Mayo-ECG) using cross-validation.

Main Results:

• The Mayo-ECG score demonstrated superior discriminative performance (AUC 0.81) compared to the Mayo score (AUC 0.76).
• The novel score effectively stratified genotype positivity across different score levels (7.1% to 91.4%).
• The Mayo-ECG model showed better overall fit (AIC 439 vs. 479) and good internal calibration.

Conclusions:

• The Mayo-ECG score significantly improves genotype prediction in HCM patients.
• This simple, ECG-integrated model outperforms the conventional score and can help prioritize genetic testing.
• The Mayo-ECG score offers a valuable tool for optimizing diagnostic strategies in hypertrophic cardiomyopathy.