Application of artificial intelligence in predicting major arrhythmic events based on clinical and left ventricular electro-anatomical mapping data
Authors: Valeri Y , Compagnucci P , Volpato G , Veri P , Pecorari E , D'angelo L , Luciani L , Pirani F , Campanelli F , Parisi Q , Cipolletta L , Dello Russo A , Piva F , Casella M 
Published: May 2024

Abstract

Introduction
Artificial intelligence (AI) is a ground-breaking frontier in the analysis of intricate and extensive datasets, also within the medical domain. AI can be used to meticulously and systematically analyse data derived from electro-anatomical mapping, scrutinizing the numerical data extracted from the mapping engineering files.

Purpose
The primary objective of the project is to utilize machine learning methods to identify potential correlations between clinical, procedural, and substrate-mapping variables with major arrhythmic cardiac events (MaCE).

Methods
Machine learning algorithms were implemented using a database of 220 patients who underwent endocardial left ventricular electro-anatomical mapping (EAM). A total of 223 clinical-procedural variables were collected (analysis conducted for the 61 most interesting variables) and substrate mapping engineering files/data were extrapolated for analysis.
Combinations of 4 and 5 variables are explored, resulting in 487,635 and 5,461,512 combinations, respectively. All the results obtained from all the combinations and different methods were evaluated by calculating the AUC (parameter indicating the probability that the prediction is in the correct order).
For each patient, the numerical data extracted from the EAM files were assessed, specifically focusing on:
1. The late potentials extension and dispersion (%LatArea).
2. Point-to-point difference of the bipolar potential compared to the unipolar potential, with identification of regions where multiple points with high differences are clustered (%UniBip)
3. Areas of deceleration, characterized by very early and very late potentials within a short range, indicating a substantial difference in the activation timing (GradVal).

Results
Analyzing the best six 4-variable logistic regression models, we note that the variables that are most frequently correlated with a MaCE during follow-up are "NYHA class", "PAPs" and "GradVal" (AUC of the best models was: 0,96; 0,94; 0,91; 0,90; 0,90; 0,90).
Analyzing the best six 5-variable logistic regression models emerges that the variables most frequently correlated with a MaCE during follow-up are "NYHA class", "PAPs", "GradVal", "Arrhythmic Storm", "TAPSE" and "%UniBip" (AUC of the best models was: 0,97; 0,93; 0,92; 0,92; 0,92; 0,92).
EAM data added to clinical data increases the number of regression variables and the model complexity; "GradVal" and "%UniBip" seem to have greater relevance.

Conclusion
The study successfully employs machine learning algorithms to identify key variables influencing the prediction of MaCE. The analysis of EAM numerical data through AI can allow for the stratification of MaCE risk. The application of artificial intelligence allows to analyse a large amount of computer data that would not be analysable by human capabilities alone. Ongoing refinement and expansion of the dataset can enhance the models' predictive capabilities for clinical applications.