Abstract 17946: How Common is New Onset Atrial Fibrillation in Single Chamber ICD Patients? Sub-analysis From the PainFree SST Study
Bibliographic record
Abstract
Introduction: Nearly 75% of ICD recipients have no history of atrial fibrillation (AF) at the time of initial implantation. Yet, many of these patients will develop new onset AF after implant which can lead to serious consequences if not detected and treated. To date, detection algorithms in single-chamber ICDs are not able to detect AF. We investigate how common is new onset AF in single chamber (SC) ICD recipients compared to CRT-D and dual chamber (DC) ICD recipients. Methods: A total of 924 patients with no history of AF from the PainFree SST study (CRT-D: N=321, DC ICD: N=303, SC ICD: N=300) were investigated. We used data from dual-chamber ICDs to estimate the incidence of new-onset AT/AF in SC ICD patients. A propensity score weighted analysis was performed using DC ICD data to match the baseline data of SC ICD patients. Patients with pacing indication were excluded from this evaluation because they would not be indicated for SC ICD. AT/AF-related complications include AT/AF-related serious adverse events, thromboembolic events, progress to AF lasting 7 days or more as well as inappropriate shocks due to AT/AF. Simulated SC ICD results were compared to actual DC and CRT-D results. Results: For AF episodes lasting 6 minutes or more, the incidence rates in CRT-D, DC ICD, and the estimated SC ICD were 28.0%, 23.4%, and 20.5% at 24 months, respectively (Table). The estimated incidence rates of AT/AF episodes in patients with SC ICD were comparable to those of DC ICD in each episode duration. Once the patients experienced first AT/AF episode, the complications were common (Table). Conclusions: AT/AF was frequently detected following implant in patients with no AF history receiving ICD or CRT-D and is associated subsequent AT/AF-related complications even in SC ICD recipients. Therefore, AT/AF monitoring in all ICDs patients may identify those at risk for these complications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".