Dataset Information

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

ABSTRACT:

Introduction

We developed a prediction model for atrial fibrillation (AF) progression and tested whether machine learning (ML) could reproduce the prediction power in an independent cohort using pre-procedural non-invasive variables alone.

Methods

Cohort 1 included 1,214 patients and cohort 2, 658, and all underwent AF catheter ablation (AFCA). AF progression to permanent AF was defined as sustained AF despite repeat AFCA or cardioversion under antiarrhythmic drugs. We developed a risk stratification model for AF progression (STAAR score) and stratified cohort 1 into three groups. We also developed an ML-prediction model to classify three STAAR risk groups without invasive parameters and validated the risk score in cohort 2.

Results

The STAAR score consisted of a stroke (2 points, p = 0.003), persistent AF (1 point, p = 0.049), left atrial (LA) dimension ≥43 mm (1 point, p = 0.010), LA voltage <1.109 mV (2 points, p = 0.004), and PR interval ≥196 ms (1 point, p = 0.001), based on multivariate Cox analyses, and it had a good discriminative power for progression to permanent AF [area under curve (AUC) 0.796, 95% confidence interval (CI): 0.753-0.838]. The ML prediction model calculated the risk for AF progression without invasive variables and achieved excellent risk stratification: AUC 0.935 for low-risk groups (score = 0), AUC 0.855 for intermediate-risk groups (score 1-3), and AUC 0.965 for high-risk groups (score ≥ 4) in cohort 1. The ML model successfully predicted the high-risk group for AF progression in cohort 2 (log-rank p < 0.001).

Conclusions

The ML-prediction model successfully classified the high-risk patients who will progress to permanent AF after AFCA without invasive variables but has a limited discrimination power for the intermediate-risk group.

SUBMITTER: Park JW

PROVIDER: S-EPMC8890475 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Park Je-Wook JW Kwon Oh-Seok OS Shim Jaemin J Hwang Inseok I Kim Yun Gi YG Yu Hee Tae HT Kim Tae-Hoon TH Uhm Jae-Sun JS Kim Jong-Youn JY Choi Jong Il JI Joung Boyoung B Lee Moon-Hyoung MH Kim Young-Hoon YH Pak Hui-Nam HN

Frontiers in cardiovascular medicine 20220216

<h4>Introduction</h4>We developed a prediction model for atrial fibrillation (AF) progression and tested whether machine learning (ML) could reproduce the prediction power in an independent cohort using pre-procedural non-invasive variables alone.<h4>Methods</h4>Cohort 1 included 1,214 patients and cohort 2, 658, and all underwent AF catheter ablation (AFCA). AF progression to permanent AF was defined as sustained AF despite repeat AFCA or cardioversion under antiarrhythmic drugs. We developed a ...[more]

PMID: 35252393

Similar Datasets

Project description:BackgroundAlthough catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. Machine learning (ML) shows promising potential in optimizing the management and clinical outcomes of patients undergoing atrial fibrillation CA (AFCA).ObjectiveThis scoping review aimed to evaluate the current scientific evidence on the application of ML for managing patients undergoing AFCA, compare the performance of various models across specific clinical tasks within AFCA, and summarize the strengths and limitations of ML in this field.MethodsAdhering to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, relevant studies published up to October 7, 2023, were searched from PubMed, Web of Science, Embase, the Cochrane Library, and ScienceDirect. The final included studies were confirmed based on inclusion and exclusion criteria and manual review. The PROBAST (Prediction model Risk Of Bias Assessment Tool) and QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) methodological quality assessment tools were used to review the included studies, and narrative data synthesis was performed on the modeled results provided by these studies.ResultsThe analysis of 23 included studies showcased the contributions of ML in identifying potential ablation targets, improving ablation strategies, and predicting patient prognosis. The patient data used in these studies comprised demographics, clinical characteristics, various types of imaging (9/23, 39%), and electrophysiological signals (7/23, 30%). In terms of model type, deep learning, represented by convolutional neural networks, was most frequently applied (14/23, 61%). Compared with traditional clinical scoring models or human clinicians, the model performance reported in the included studies was generally satisfactory, but most models (14/23, 61%) showed a high risk of bias due to lack of external validation.ConclusionsOur evidence-based findings suggest that ML is a promising tool for improving the effectiveness and efficiency of managing patients undergoing AFCA. While guiding data preparation and model selection for future studies, this review highlights the need to address prevalent limitations, including lack of external validation, and to further explore model generalization and interpretability.

Project description:Abstract Funding Acknowledgements Type of funding sources: None. Introduction Atrial fibrillation catheter ablation (AFCA) improves sinus node function and may defer a permanent pacemaker (PPM) implantation. We explored the clinical and genetic characteristics of the patients with AF and sinus node dysfunction (SND) who eventually needed PPM implantation after AFCA. Methods Among 2,732 AF patients with genome-wide association study (GWAS, PMRA, Thermofisher scientific, MA, USA) data who underwent the first AFCA, 244 patients (65.0, IQR [59.0, 72.0] years, male 58.2%, paroxysmal AF 79.5%) had underlying SND before AFCA (class I indication for PPM). We retrospectively investigated clinical factors, single-nucleotide polymorphisms (SNPs), and their polygenic risk score (PRS) associated with PPM-AFCA. We defined the cut-off of PRS using the Youden index. Results During median 37.9 (IQR 20.3-77.9) months follow-up, 36 out of 244 patients (16.1%) with underlying SND eventually underwent PPM implantation after AFCA (PPM-AFCA). PPM-AFCA group a had a higher body mass index (p=0.013) and large left atrial (LA) dimension (p=0.006) than their counterpart. Among 44 SNPs related to AF and SND, the rs3922844 (p=0.003) near SCN5A and rs9320841 (p=0.011) and rs1015451 (p=0.046) near CJA1 were significantly associated with PPM-AFCA. The rate of PPM-AFCA implantation was significantly higher in the high PRS group (≥1.175, 25.6%) compared to the low PRS group (<1.175, 9.38%, p=0.002, Log-rank p=0.002). LA dimension (adjusted HR 1.06, 95% CI [1.00-1.13], p=0.044) and PRS (adjusted HR 2.69, 95% CI [1.68-4.31], p<0.001) were associated with a higher risk for PPM-AFCA after adjusting clinical factors including age, sex, AF type, beta blocker, and antiarrhythmic drugs after AFCA. Conclusions In AF patients with SND, 16% with the genetic background of SCN5A and CJA1 or large atrium eventually needed PPM implantation after AFCA. KM analysis for PM implantation SNPs associated with PM implantation

Dataset Information

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

Introduction

Methods

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets