ArticleAnnals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc2026
Dynamic Risk Assessment for the Development of Persistent Atrial Fibrillation Using Statistical and Machine Learning Approaches.
Article in Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCardiac implantable electronic devices (CIEDs) frequently detect brief, often subclinical atrial fibrillation (AF), but their value for predicting progression to persistent AF remains uncertain. Statistical and machine learning (ML) approaches may enable dynamic risk stratification using this longitudinal device data.
objectiveTo develop a risk stratification model using clinical and CIED-derived AF burden measured over a rolling 6-month window to predict progression to persistent AF.
methodsWe analyzed continuous CIED data from 1985 patients without prior persistent AF implanted between 2016 and 2024 at a tertiary medical center. AF burden and clinical variables were summarized using overlapping 6-month rolling windows to estimate 1-year risk of persistent AF. Associations were evaluated using Kaplan-Meier and Cox proportional hazards models. A gradient-boosted decision tree model (XGBoost) was used to predict progression.
resultsDuring a mean follow-up of 1192 days, 874 patients (44%) developed paroxysmal AF, of whom 257 (29%) progressed to persistent AF after a mean of 813 days. Patients with no AF or < 1 h/day of AF in the prior 6 months had > 97% 1-year freedom from persistent AF, whereas those with > 8 h/day had a 63% progression rate. Higher AF burden was strongly associated with progression (maximum HR 8.66, p < 0.001). The ML model demonstrated high predictive performance (sensitivity 99.4%, specificity 95.7%).
conclusionCIED-detected AF burden is strongly associated with progression to persistent AF. ML-based analysis of 6-month device data enables accurate, point-in-time risk stratification to support earlier and more targeted clinical management.
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.