Prediction of programmed ventricular stimulation inducibility using machine learning in post-myocardial infarction patients at risk for sudden cardiac arrest with preserved ejection fraction ≥40%

Abstract

Abstract Introduction Sudden cardiac death (SCD) in post myocardial infarction (post-MI) patients with a relatively preserved left ventricular ejection fraction (LVEF ≥40%) has 1% annual incidence. In the PRESERVE-EF study, we used a two-step SCD risk stratification approach to detect patients with a relatively preserved left ventricular ejection fraction ≥40% at risk for major arrhythmic events. Seven noninvasive risk factors (NIRFs) were extracted from ambulatory electrocardiography. Patients with at least one NIRF present were referred for invasive programmed ventricular stimulation (PVS). Inducible patients received an ICD. Purpose The present study examines the performance of machine learning technology for the prediction of the inducible patients in PRESERVE-EF study. Methods After first step screening with NIRFs, 152 out of 575 patients underwent PVS and 41 of them were inducible. For the present analysis, data from these 152 patients were analysed. We used machine learning of NIRFs to predict these inducible high risk patients. We selected as classification method the Nearest Neighbour (NN) algorithm, after experimentation with several classifiers. NN classifies each subject according to the class of the N nearest neighbours. For each subject, we created a vector with the following 7 features: SAECG Late Potentials, Ventricular Premature beats ≥30/hour, Non-sustained Ventricular Tachycardia ≥1 episode (s)/24 hours, Fredericia corrected QT interval ≥45 0ms, SDNN/HRV ≤75 ms, T Wave Alternans ≥65 μV, Combined Deceleration capacity (DC) ≤4.5 ms and Heart Rate Turbulence Onset (To) ≥0% and Heart Rate Turbulence Slope (Ts) ≤2.5 ms. Results The achieved accuracy reached up to 72.2% when N was set to 7. We had totally 144 samples, 41 of which were inducible high risk patients. Results were similar for other values of N. To ensure independence of train and test sets, we employed 10-fold cross validation. Conclusions Inducible on PVS patients in PRESERVE-EF study were predicted with machine learning classification of NIRFs. Funding Acknowledgement Type of funding sources: None.