Predictors and Clinical Impact of Atrial Fibrillation After Pacemaker Implantation in Elderly Patients Treated with Dual Chamber Versus Ventricular Pacing
Bibliographic record
Abstract
The Pacemaker Selection in the Elderly (PASE) trial was a prospective, multicenter, single blind, randomized comparison of single chamber, rate adaptive, ventricular pacing (VVIR) with dual chamber, rate adaptive pacing (DDDR) in 407 patients aged > or =65 years(mean 76 +/- 7 years, 60% male)with standard bradycardia indications for dual chamber pacemaker implantation. The incidence, predictors, and clinical consequences of atrial fibrillation (AF) developing after pacemaker implantation in the PASE trial were studied prospectively. During a median follow-up of 18 months, AF developed in 73 (18%) patients. Kaplan-Meier estimated cumulative incidences of AF in patients with sinus node dysfunction (n=176) at 18 months were 28% in the VVIR and 16% in the DDDR groups (P=0.08). After adjustment for other clinical variables using a Cox multivariate regression model, randomization to VVIR compared with DDDR pacing mode among patients with sinus node dysfunction was independently associated with a 2.6-fold increased relative risk (RR) of developing AF after pacemaker implantation (P=0.01). Other independent clinical risk factors for development of postimplant AF included a preimplant history of hypertension (P=0.02) or supraventricular tachyarrhythmias(P<0.04). Patients who developed AF had similar health related quality of life scores and cardiovascular functional status after 18 months of pacing as patients who remained free of AF. The RR of death, stroke, or heart failure hospitalization was not increased in patients who developed AF. Thus, in the elderly patients with sinus node dysfunction requiring permanent pacing, DDDR pacing mode protected against the development of AF. However, development of AF after pacemaker implantation in this population was not associated with a significant impact on quality-of-life, functional status, or other clinical endpoints during 18 months of follow-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".