Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation

Abstract

Atrial fibrillation (AF) cases are expected to increase over the next several decades, due to the rise in the elderly population. One promising treatment option for AF is catheter ablation, which is increasing in use. We investigated the hospital readmissions data for AF patients undergoing catheter ablation, and used machine learning models to explore the risk factors behind these readmissions. We analyzed data from the 2013 Nationwide Readmissions Database on cases with AF, and determined the relative importance of factors in predicting 30-day readmissions for AF with catheter ablation. Various machine learning methods, such as k-nearest neighbors, decision tree, and support vector machine were utilized to develop predictive models with their accuracy, precision, sensitivity, specificity, and area under the curve computed and compared. We found that the most important variables in predicting 30-day hospital readmissions in patients with AF undergoing catheter ablation were the age of the patient, the total number of discharges from a hospital, and the number of diagnoses on the patient’s record, among others. Out of the methods used, k-nearest neighbor had the highest prediction accuracy of 85%, closely followed by decision tree, while support vector machine was less desirable for these data. Hospital readmissions for AF with catheter ablation can be predicted with relatively high accuracy, utilizing machine learning methods. As patient age, the total number of hospital discharges, and the total number of patient diagnoses increase, the risk of hospital readmissions increases.

Prediction of Atrial Fibrillation in Hospitalized Elderly Patients With Coronary Heart Disease and Type 2 Diabetes Mellitus Using Machine Learning: A Multicenter Retrospective Study

“…Machine learning algorithms could deeply mine and analyze big data, and it had been widely used in disease predictions and prognostication (15)(16)(17). RF is one of the widely used algorithms in machine learning.…”

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

“… 103 focused on a more specific risk stratification model of young patients with hypertension. Hospital readmissions data for AF patients undergoing catheter ablation was investigated by Hung et al ., to estimate the risk factors behind 90- 104 and 30-day 105 hospital readmissions.…”

Machine Learning-Predicted Progression to Permanent Atrial Fibrillation After Catheter Ablation

“…However, only a study reported risk model derived from over 1,000 patient population (6), but the follow-up duration was relatively short of investigating the progression to permanent AF (mean 2.5 years). Based on a variety of sources of complex patient information, including electronic medical records (EMRs) and imaging data, artificial intelligence (AI) has been used to detect AF and to predict ablation outcomes (7)(8)(9)(10)(11)(12). Furthermore, AI has been suggested for predicting invasive parameters or invasive cardiovascular outcomes (13).…”