P1291 Are we ready for machine learning in HCM risk stratification?

Abstract

BACKGROUND Hypertrophic cardiomyopathy (HCM) is a heart disease characterized by hypertrophy of the left ventricular myocardium. The disease is the most common cause of sudden cardiac death (SCD) in young people and competitive athletes due to fatal ventricular arrhythmias, but in most patients, however, HCM has a benign course. Therefore, it is of the utmost importance to properly evaluate patients and identify those who would benefit from a cardioverter-defibrillator (ICD) implantation. The HCM SCD-Risk Calculator is a useful tool for estimating the 5-year risk of SCD. Parameters included in the model at evaluation are: age, maximum left ventricular wall thickness, left atrial dimension, maximum gradient in left ventricular outflow tract, family history of SCD, non-sustained ventricular tachycardia and unexplained syncope. Patients’ risk of SCD is classified as low (<4%), intermediate (4-<6%) or high (≥6%). Those in the high-risk group should have an ICD implantation. It can also be considered in the intermediate-risk group. However, the calculator still needs improvement and machine learning (ML) has the potential to fulfill this task. ML algorithm creates a model for solving a specific problem without explicit programming - instead it relies only on available data - by discovering patterns and relations. METHODS 252 HCM patients (aged 20-88 years, 49,6% were men) treated in our Department from 2005 to 2018, have been enrolled. The follow-up lasted 0-13 years (average: 3.8 years). SCD was defined as sudden cardiac arrest (SCA) or an appropriate ICD intervention. All parameters from HCM SCD-Risk Calculator have been obtained and the risk of SCD has been calculated for all patients during the first echocardiographic evaluation. ML model with variables from HCM SCD-Risk Calculator has been created. Both methods have been compared. RESULTS 20 patients reached an SCD end-point. 1 patient died due to SCA and 19 had an appropriate ICD intervention. Among them, there were respectively 6, 7 and 7 patients in the low, intermediate and high-risk group of SCD. 1 patient, who died, had a low risk. The ML model correctly assessed the SCD event only in 1 patient. According to ML, the risk of SCD ≤2.07% was a negative predictor. CONCLUSIONS The study did not show an advantage of ML over HCM SCD-Risk Calculator. Because of the characteristic of the dataset (approximately the same number of features and observations), the selection of machine learning algorithms was limited. Best results (evaluated using LOOCV) were achieved with a decision tree. We expect that bigger dataset would allow improving model performance because of strong regularization need in the current setup.