[Study on predicting new onset heart failure events in patients with hypertrophic cardiomyopathy using machine learning algorithms based on clinical and magnetic resonance features].

<b>Objective:</b> To explore the value of predicting new-onset heart failure events in patients with hypertrophic cardiomyopathy (HCM) using clinical and cardiac magnetic resonance (CMR) features based on machine learning algorithms. <b>Methods:</b> The study was a retrospective cohort study. Patients with a confirmed diagnosis of HCM who underwent CMR examinations at Beijing Anzhen Hospital from May 2017 to March 2021 were selected and randomly divided into the training set and the validation set in a ratio of 7∶3. Clinical data and CMR parameters (including conventional parameters and radiomics features) were collected. The endpoint events were heart failure hospitalization and heart failure death, with follow-up ending in January 2023. Features with high stability and <i>P</i> value<0.05 in univariate Cox regression analysis were selected. Subsequently, three machine learning algorithms-random forest, decision tree, and XGBoost-were used to build heart failure event prediction models in the training set. The model performance was then evaluated using the independent validation set, with the performance assessed based on the concordance index. <b>Results:</b> A total of 462 patients were included, with a median age of 51 (39, 62) years, of whom 332 (71.9%) were male. There were 323 patients in the training set and 139 in the validation set. The median follow-up time was 42 (28, 52) months. A total of 44 patients (9.5% (44/462)) experienced endpoint events (8 cases of heart failure death and 36 cases of heart failure hospitalization), with 31 events in the training set and 13 in the validation set. Univariate Cox regression analysis identified 39 radiomic features, 4 conventional CMR parameters (left ventricular end-diastolic volume index, left ventricular end-systolic volume index, left ventricular ejection fraction, and late gadolinium enhancement ratio), and 1 clinical feature (history of non-sustained ventricular tachycardia) that could be included in the machine learning model. In the prediction models built with the training set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.966 (95%<i>CI</i> 0.813-0.995), 0.956 (95%<i>CI</i> 0.796-0.992), and 0.973 (95%<i>CI</i> 0.823-0.996), respectively. In the validation set, the concordance indices for the random forest, decision tree, and XGBoost models were 0.854 (95%<i>CI</i> 0.557-0.964), 0.706 (95%<i>CI</i> 0.399-0.896), and 0.703 (95%CI 0.408-0.890), respectively. <b>Conclusion:</b> Integrating clinical and CMR features of HCM patients through machine learning aids in predicting heart failure events, with the random forest model showing superior performance.