Observational studyScientific reports2022
Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices.
Observational study in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Artificial intelligence in cardiology: an updated systematic review with ethical considerations and challenges in implementing artificial intelligence models.Annals of medicine and surgery (2012) · 2026Article
- A paradigm shift toward full-cycle management of atrial fibrillation: integrating digital twins and artificial intelligence.Frontiers in cardiovascular medicine · 2026Review
- Clinical relevance and management of atrial high-rate episodes: a narrative review.Archivos de cardiologia de Mexico · 2025Review
- Artificial intelligence-powered advancements in atrial fibrillation diagnostics: a systematic review.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2025Review
- New-Onset Atrial Fibrillation in Patients with Pacemakers and the Implications of Hepatic Impairment.Life (Basel, Switzerland) · 2025Article
- The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation.Reviews in cardiovascular medicine · 2023Review
- Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries.Journal of multidisciplinary healthcare · 2023Review
- Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
To assess the utility of machine learning (ML) algorithms in predicting clinically relevant atrial high-rate episodes (AHREs), which can be recorded by a pacemaker. We aimed to develop ML-based models to predict clinically relevant AHREs based on the clinical parameters of patients with implanted pacemakers in comparison to logistic regression (LR). We included 721 patients without known atrial fibrillation or atrial flutter from a prospective multicenter (11 tertiary hospitals) registry comprising all geographical regions of Korea from September 2017 to July 2020. Predictive models of clinically relevant AHREs were developed using the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm. Model prediction training was conducted by seven hospitals, and model performance was evaluated using data from four hospitals. During a median follow-up of 18 months, clinically relevant AHREs were noted in 104 patients (14.4%). The three ML-based models improved the discrimination of the AHREs (area under the receiver operating characteristic curve: RF: 0.742, SVM: 0.675, and XGB: 0.745 vs. LR: 0.669). The XGB model had a greater resolution in the Brier score (RF: 0.008, SVM: 0.008, and XGB: 0.021 vs. LR: 0.013) than the other models. The use of the ML-based models in patient classification was associated with improved prediction of clinically relevant AHREs after pacemaker implantation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.