Predictors of sinus rhythm after electrical cardioversion of atrial fibrillation: results from a data mining project on the Flec-SL trial data set

Oto, Emre; Okutucu, Sercan; Katircioglu-Öztürk, Deniz; Güvenir, H. Altay; Karaağaoğlu, Ergün; Borggrefe, Martin; Breithardt, Günter; Goette, Andreas; Ravens, Ursula; Steinbeck, Gerhard; Wegscheider, Karl; Oto, Ali̇; Kirchhof, Paulus

doi:10.1093/europace/euw144

Cited by 8 publications

(7 citation statements)

References 22 publications

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“…Different classifiers, such as SVM, random forest, and logistic regression, were used to predict AF. Oto et al (2017) used ML to predict the recovery of sinus rhythm after cardioversion of AF. Kamel et al used ML to analyze clinical and population data to identify features and patterns that could help determine the underlying mechanism of embolic strokes of undetermined sources.…”

Section: Resultsmentioning

confidence: 99%

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Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

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Atrial fibrillation (AF) affects more than 30 million individuals worldwide, making it the most prevalent cardiac arrhythmia on a global scale. This systematic review summarizes recent advancements in applying artificial intelligence (AI) techniques for AF detection, prediction, and guiding treatment selection and risk stratification. In adherence with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses), a total of 171 pertinent studies conducted between 2013 and 2023 were examined. Studies applying machine learning (ML) and deep learning (DL) to electrocardiogram (ECG), photoplethysmography (PPG), wearable data, and other sources were evaluated. For AF detection, most works employed DL (48 studies) and ML (28 studies) on ECG data. DL methods directly analyzed ECG waveforms and outperformed approaches relying on hand‐crafted features. For prediction and risk stratification, 22 studies used ML while 7 leveraged DL on ECG. An emerging trend showed the growing potential of PPG for AF screening. Overall, AI demonstrated promising capabilities across various AF‐related tasks. However, real‐world implementation faces challenges including a lack of interpretability, the need for multimodal data integration, prospective performance validation, and regulatory compliance. Future research directions involve quantifying model uncertainty, enhancing transparency, and conducting population‐based clinical trials to facilitate translation into practice.This article is categorized under: Application Areas > Health Care Application Areas > Science and Technology Technologies > Artificial Intelligence

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Section: Resultsmentioning

confidence: 99%

“…employed a ML-based approach to predict AF among older patients using electronic medical records. Different classifiers, such as SVM, random forest, and logistic regression, were used to predict AF Oto et al (2017). used ML to predict the recovery of sinus rhythm after cardioversion of AF.…”

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confidence: 99%

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Salvi,

Acharya,

Seoni

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

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“…Incorporating machine learning systems to EMR for AF may be useful to determine the behavior of physiological data and the temporal relationships associated with risk factors. Cox proportional hazard regression and survival analysis have been employed to predict the response of pharmacological and electrical cardioversion therapies for AF ( 58 , 59 ), however there are no reports that apply these machine learning techniques to identify AF risk factors. Our study is the first study to report the use of machine learning and survival analysis to develop clusters and risk models for AF in a Latin–American population.…”

Section: Discussionmentioning

confidence: 99%

Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease

Silva

Morillo²,

Leite-Castro³

et al. 2022

Front. Cardiovasc. Med.

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BackgroundPatients with sleep apnea (SA) and coronary artery disease (CAD) are at higher risk of atrial fibrillation (AF) than the general population. Our objectives were: to evaluate the role of CAD and SA in determining AF risk through cluster and survival analysis, and to develop a risk model for predicting AF.MethodsElectronic medical record (EMR) database from 22,302 individuals including 10,202 individuals with AF, CAD, and SA, and 12,100 individuals without these diseases were analyzed using K-means clustering technique; k-nearest neighbor (kNN) algorithm and survival analysis. Age, sex, and diseases developed for each individual during 9 years were used for cluster and survival analysis.ResultsThe risk models for AF, CAD, and SA were identified with high accuracy and sensitivity (0.98). Cluster analysis showed that CAD and high blood pressure (HBP) are the most prevalent diseases in the AF group, HBP is the most prevalent disease in CAD; and HBP and CAD are the most prevalent diseases in the SA group. Survival analysis demonstrated that individuals with HBP, CAD, and SA had a 1.5-fold increased risk of developing AF [hazard ratio (HR): 1.49, 95% CI: 1.18–1.87, p = 0.0041; HR: 1.46, 95% CI: 1.09–1.96, p = 0.01; HR: 1.54, 95% CI: 1.22–1.94, p = 0.0039, respectively] and individuals with chronic kidney disease (CKD) developed AF approximately 50% earlier than patients without these comorbidities in a period of 7 years (HR: 3.36, 95% CI: 1.46–7.73, p = 0.0023). Comorbidities that contributed to develop AF earlier in females compared to males in the group of 50–64 years were HBP (HR: 3.75 95% CI: 1.08–13, p = 0.04) CAD and SA in the group of 60–75 years were (HR: 2.4 95% CI: 1.18–4.86, p = 0.02; HR: 2.51, 95% CI: 1.14–5.52, p = 0.02, respectively).ConclusionMachine learning based algorithms demonstrated that CAD, SA, HBP, and CKD are significant risk factors for developing AF in a Latin–American population.

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“…In a recent paper by Oto et al, the authors used a data mining algorithm to identify predictors of recurrence of persistent AF. 24 To our knowledge, no studies have so far applied data mining algorithms to develop sex-specific prediction models for successful electrical cardioversion. Another future approach may be the use of personalised computational modelling of arrhythmogenesis, which was recently applied among patients with persistent AF to identify ablation targets.…”

Section: Discussionmentioning

confidence: 99%

Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

et al. 2020

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ObjectiveElectrical cardioversion is frequently performed to restore sinus rhythm in patients with persistent atrial fibrillation (AF). However, AF recurs in many patients and identifying the patients who benefit from electrical cardioversion is difficult. The objective was to develop sex-specific prediction models for successful electrical cardioversion and assess the potential of machine learning methods in comparison with traditional logistic regression.MethodsIn a retrospective cohort study, we examined several candidate predictors, including comorbidities, biochemistry, echocardiographic data, and medication. The outcome was successful cardioversion, defined as normal sinus rhythm immediately after the electrical cardioversion and no documented recurrence of AF within 3 months after. We used random forest and logistic regression models for sex-specific prediction.ResultsThe cohort comprised 332 female and 790 male patients with persistent AF who underwent electrical cardioversion. Cardioversion was successful in 44.9% of the women and 49.9% of the men. The prediction errors of the models were high for both women (41.0% for machine learning and 48.8% for logistic regression) and men (46.0% for machine learning and 44.8% for logistic regression). Discrimination was modest for both machine learning (0.59 for women and 0.56 for men) and logistic regression models (0.60 for women and 0.59 for men), although the models were well calibrated.ConclusionsSex-specific machine learning and logistic regression models showed modest predictive performance for successful electrical cardioversion. Identifying patients who will benefit from cardioversion remains challenging in clinical practice. The high recurrence rate calls for thoroughly informed shared decision-making for electrical cardioversion.

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Predictors of sinus rhythm after electrical cardioversion of atrial fibrillation: results from a data mining project on the Flec-SL trial data set

Cited by 8 publications

References 22 publications

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023)

Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease

Role for machine learning in sex-specific prediction of successful electrical cardioversion in atrial fibrillation?

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