Abstract 12822: Prediction of New-Onset Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy Using RNA-Sequencing of Plasma Small Non-Coding RNAs

Abstract

Background: It is known that about 20% of patients with hypertrophic cardiomyopathy (HCM) develop atrial fibrillation (AF), which can lead to stroke and worsening of heart failure. The current clinical model to predict new-onset AF in patients with HCM (HCM-AF score) offers only modest accuracy. RNA-sequencing (RNA-Seq) can determine concentrations of thousands of small non-coding RNAs (sncRNAs) in plasma. Hypothesis/Aims: To test the hypothesis that RNA-Seq of plasma sncRNAs predicts new-onset AF in HCM. Methods: In this prospective, multi-center cohort study, we conducted RNA-Seq of 3,740 plasma sncRNAs at enrollment on 283 patients with HCM without a prior history of AF. The outcome was new-onset AF. We developed a sncRNA-Seq-based LASSO classification model (sncRNA-Seq-based model) to predict new-onset AF using data from one institution (training set, n=193). We tested the predictive ability of the model in independent samples from the other institutions (test set, n=90). We also developed a clinical model with LASSO using all components of HCM-AF score - i.e., left atrial diameter, age, age at HCM diagnosis, and New York Heart Association functional class ≥2. We then compared the area under the receiver-operating characteristic curve (AUC) between the sncRNA-Seq-based model and the clinical model in the test set. Results: During a median follow-up of 2.8 [1.9-5.1] years, 25 patients (13%) in the training set and 7 (8%) in the test set developed new-onset AF. Using the sncRNA-Seq-based model developed in the training set, the AUC to predict new-onset AF in the test set was 0.82 (95% confidence interval [CI] 0.71-0.94, Figure ). This model outperformed the clinical model (AUC 0.59; 95% CI 0.38-0.81; Delong’s test P=0.02). Conclusion: This study serves as the first to demonstrate the ability of plasma sncRNA-Seq to predict new-onset AF in patients with HCM. The prediction model may help physicians identify patients with HCM who are at high risk of developing new-onset AF.