Key result
Random Forest model outperforms traditional HCM-AF score in predicting new-onset AF with 0.77 AUC.
Why the study?
HCM significantly increases the risk of new-onset AF, prompting development of an ML model to improve AF prediction and investigate electrophysiological and non-traditional preclinical predictors.
Does a machine learning model improve the prediction of new-onset atrial fibrillation compared to the HCM-AF score in patients with hypertrophic cardiomyopathy?
Cohort (n=1,014)
No
Does a machine learning model improve the prediction of new-onset atrial fibrillation compared to the HCM-AF score in patients with hypertrophic cardiomyopathy?
Effect estimate: AUC 0.770 (95% CI 0.699-0.840)
Absolute Event Rate: 0.77% vs 0.692%
p-value: p=0.012
A machine learning-based Random Forest model significantly improves the prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy compared to the traditional HCM-AF score.
Background Hypertrophic cardiomyopathy (HCM) significantly increases the risk of new-onset atrial fibrillation (AF) through distinct pathogenic mechanisms. This study develops a machine learning (ML) model to improve AF prediction in patients with HCM and investigates electrophysiological abnormalities and non-traditional factors as preclinical predictors. The findings aim to inform early warning systems and precision management strategies for AF in this patient cohort. Methods This retrospective cohort study consecutively enrolled patients diagnosed with HCM who had no prior history of AF from 2014 to 2023. A two-step feature selection procedure involving the Boruta algorithm and LASSO regression was implemented, and four machine-learning based algorithms were trained and compared against the reference model of the HCM-AF score. The Shapley Additive Explanations (SHAP) method was utilized for interpretability. Results A total of 225 patients (annual incidence ratio 3.42%, 225/1014) developed new-onset AF during the median follow-up period of 6.50 years. Twelve clinical variables were identified through the feature selection process, with the random forest model demonstrating the best overall performance with area under the receiver-operating characteristic curve (AUC) 0.770 and 95% confidence interval (CI) of 0.699-0.840, outperforming the reference model (AUC 0.692, 95% CI 0.598-0.786) (Delong’s P = 0.012). The SHAP analysis revealed that indices of heart failure, age, left atrial size, followed by a history of cardiac implantable electronic devices, frequent atrial premature contractions, P-wave terminal force in lead V 1 , P-wave duration, PR interval, and left ventricular remodeling were associated with a significantly higher model-predicted risk of AF. Conclusions The Random Forest model outperformed the HCM-AF score with robust predictive ability, validating P-wave indices as preclinical AF biomarkers through ML model for the first time. This integrated model offers a practical tool for early AF risk stratification in HCM, highlighting the clinical value of early symptomatic monitoring. Future multicenter validation is needed.
No takes yet. Share an insight, caveat, or question.
Ding et al. (2026) conducted a cohort in Hypertrophic cardiomyopathy (n=1,014). Random Forest machine learning model vs. HCM-AF score was evaluated on Prediction of new-onset clinical atrial fibrillation (AUC) (AUC 0.770, 95% CI 0.699-0.840, p=0.012). The Random Forest machine learning model outperformed the traditional HCM-AF score in predicting new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy, achieving an AUC of 0.770 versus 0.692.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: