Artificial intelligence-enhanced electrocardiography and sudden death risk factors in cardiac arrest survivors diagnosed with hypertrophic cardiomyopathy.

BACKGROUND

The characteristics of patients with hypertrophic cardiomyopathy (HCM) who present with sudden cardiac arrest as the initial manifestation of the disease are not well known.

OBJECTIVE

The purpose of this study was to evaluate artificial intelligence-enhanced electrocardiography (AI-ECG) and sudden cardiac death (SCD) risk factors in patients with HCM presenting with sudden cardiac arrest (SCA).

METHODS

We identified patients within the Mayo Clinic enterprise (2001-2022) who were newly diagnosed with HCM after surviving SCA. Index clinical, electrocardiographic, and imaging characteristics were documented. Risk models for SCD in HCM were retrospectively applied. An AI-ECG algorithm designed for the detection of HCM was also applied to the patients' first ECG obtained at our institution.

RESULTS

Twenty-seven patients met the inclusion criteria. Eight patients (30%) had documented cardiac symptoms preceding SCA. Twenty-four patients (89%) had an abnormal electrocardiogram at index evaluation, and 16 (59%) had ≥1 SCD risk factor that could have qualified them for an implantable cardioverter-defibrillator if HCM had been diagnosed before SCA. Retrospective application of the European Society of Cardiology 5-year HCM Risk-SCD tool yielded a median score of 4.6% (interquartile range 3.2%-7.2%), with 12 patients (44%) having an estimated SCD risk of <4%, implying a low risk. AI-ECG indicated a high prediction score for HCM in 26 patients (96%), suggesting its potential utility as an early detector of the disease if applied before SCA.

CONCLUSION

Among patients with SCA who were then newly diagnosed with HCM, conventional SCD risk factors were common but not universal in this post hoc assessment. AI-ECG may facilitate the early detection of HCM.