Continuous versus Intermittent Monitoring of Ventricular Rate in Patients with Permanent Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: Ventricular rate control (VRC) is an important treatment strategy for patients with permanent atrial fibrillation (AF). We assessed the prevalence of poor VRC and the adequacy of various intermittent monitoring regimens to accurately characterize VRC during permanent AF. METHODS: We retrospectively analyzed data from dual chamber implantable cardioverter defibrillator (ICD) and cardiac resynchronization therapy defibrillator (CRT-D) patients in the Medtronic Discovery™ Link having permanent AF (AF burden >23 hours/day) and ≥ 365 consecutive days of device data. Poor VRC was defined as a day with the mean ventricular rate during AF >100 beats/minute (bpm) for ICD patients and >90 bpm for CRT-D patients. Intermittent monitoring regimens were simulated from continuous device data by randomly selecting subsets of days in which data were available for analysis. Assessments of poor VRC were computed after replicating 1,000 simulations. RESULTS: ICD (n = 1,902, age = 71 ± 10) and CRT-D (n = 3,397, age = 72 ± 9) patients were included and followed for 365 days. The prevalence of poor VRC was 24.8% among ICD patients and 28.6% among CRT-D patients. Significantly more patients were identified as having poor VRC with continuous monitoring compared to all intermittent monitoring regimens (sensitivity range = 8%-31%). Furthermore, 11.6% of ICD patients and 17.9% of CRT-D patients experienced ≥ 7 days with poor VRC, to which the sensitivities of annual 7- and 21-day recordings were <7% and <20%, respectively. CONCLUSIONS: A significant proportion of permanent AF patients experience poor VRC that would be missed with random intermittent monitoring. Whether improved knowledge of VRC with continuous monitoring will lead to improved outcomes compared to intermittent monitoring requires further study.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".