Mattia Corianò, Corrado Lanera, Laura De Michieli, Martina Perazzolo Marra, Sabino Iliceto, Dario Gregori, Francesco Tona
Abstract
Prediction of major arrhythmic events (MAEs) in dilated cardiomyopathy represents an unmet clinical goal. Computational models and artificial intelligence (AI) are new technological tools that could offer a significant improvement in our ability to predict MAEs. In this proof-of-concept study, we propose a deep learning (DL)-based model, which we termed Deep ARrhythmic Prevention in dilated cardiomyopathy (DARP-D), built using multidimensional cardiac magnetic resonance data (cine videos and hypervideos and LGE images and hyperimages) and clinical covariates, aimed at predicting and tracking an individual patient’s risk curve of MAEs (including sudden cardiac death, cardiac arrest due to ventricular fibrillation, sustained ventricular tachycardia lasting ≥30 s or causing haemodynamic collapse in <30 s, appropriate implantable cardiac defibrillator intervention) over time.
Introduction
Dilated cardiomyopathy (DCM) is characterized by left ventricular (LV) or biventricular dilation and systolic dysfunction unexplained by coronary artery disease (CAD) or abnormal loading conditions [1, 2]. The aetiology of DCM represents a tangle where a genetic predisposition interacts with extrinsic factors, resulting in a wide spectrum of phenotypes with different natural histories and arrhythmic risks. Therefore, the true prevalence is difficult to evaluate, estimated at 1 in 2700 individuals [3, 4]. The five-year mortality rate ranges between 21% and 28%, with a relevant amount of major arrhythmic events (MAEs), particularly sudden cardiac death (SCD), the incidence of which stands at approximately 12%, accounting for 25–35% of all deaths [5]. Discrimination between patients at a high or low risk for MAE is challenging.
Materials and method
We retrospectively collected data from consecutive patients referred to the Cardiology Department of the University Hospital of Padua from June 2002 to November 2019 with a diagnosis of DCM. The diagnosis was based on the 1995 World Health Organization/International Society and Federation of Cardiology criteria [15]. Inclusion criteria were as follows: depressed LVEF systolic function (<50%); an angiographic study showing the absence of flow-limiting CAD (defined as ≥50% luminal stenosis on coronary angiography); the absence of either valvular or hypertensive heart disease and congenital heart disease; and patients.
Results
The overall cohort consisted of 154 patients, with a median age of 49 years and a median follow-up time of 60 months. The baseline characteristics of the cohort are shown in Table 1. In summary, males were more represented (71%), and the most common risk factor was arterial hypertension (37%), followed by smoking habits (35%). A positive familial history of cardiomyopathy and SCD was present in 17% and 5% of patients, respectively. The majority of patients presented few symptoms (NYHA I, 88%) and were in sinus rhythm (86%). All patients took heart failure (HF) medication, mainly β-blockers and angiotensin-converting enzyme inhibitors.
Discussion
We present an innovative approach to predict MAEs, termed DARP-D, which uses a deep NN “survival” model for the risk assessment of fatal arrhythmia in DCM. The model was trained using two types of input data, CMR sequences and clinical covariates. The choice of the clinical variables was made considering the current knowledge about risk factors and comorbidities associated with MAEs. In fact, all variables are well recognized independent factors of MAEs in DCM [32]. Moreover, our cohort showed baseline characteristics that were similar to other cohorts represented in clinical trials and prospective registers of DCM [33–35]. This similarity was marked by the outcome analysis, with an analogue percentage of MAEs and overall mortality occurring during the follow-up.
Conclusions
In this study, we presented a DL technology, DARP-D, trained on a cohort of patients with DCM and capable of learning from clinical covariables and CMR hypervideos and hyperimages, returning a specific per-patient time-dependent risk of MAEs. Our approach could represent a fundamental change in the prevention of arrhythmic death in DCM. However, the low number of patients, MAEs and epoch of training make the model a promising prototype but not ready for clinical usage. Further research is needed to improve, stabilize, and validate the performance of the DARP-D to convert it from an AI experiment to a daily used tool.
Citation: Corianò M, Lanera C, De Michieli L, Perazzolo Marra M, Iliceto S, Gregori D, et al. (2024) Deep learning-based prediction of major arrhythmic events in dilated cardiomyopathy: A proof of concept study. PLoS ONE 19(2): e0297793. https://doi.org/10.1371/journal.pone.0297793
Editor: Vincenzo Lionetti, Scuola Superiore Sant’Anna, ITALY
Received: July 21, 2023; Accepted: January 12, 2024; Published: February 29, 2024
Copyright: © 2024 Corianò et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: https://github.com/UBESP-DCTV/cardio.mri.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297793#sec024
