PM297 A WHF-Sponsored Pilot Study of a Mobile Health Intervention to Improve Secondary Prevention of Coronary Heart Disease in China: The Takemeds Study
Bibliographic record
Abstract
Decisions to use rhythm control in atrial fibrillation (AF) should generally be dictated by patient factors, such as quality of life, heart failure, and other comorbidities. Whether or not other factors affect decisions about the use of rhythm control, and catheter ablation in particular, is unknown.A cohort of all patients diagnosed with nonvalvular AF were identified from the National Cardiovascular Data Registry's Practice Innovation and Clinical Excellence (PINNACLE) AF registry of US outpatient cardiology practices during the study period from May 1, 2008, to December 31, 2014. Overall and practice-specific rates of rhythm control (cardioversion, antiarrhythmic drug therapy, or catheter ablation) were assessed. We assessed patient and practice factors associated with rhythm control and determined the relative contribution of patient, practice, and unmeasured practice factors with its use.Among 511,958 PINNACLE AF patients, 22.3% were treated with rhythm control and 2.9% underwent catheter ablation. Significant practice variation in rhythm control was present (median rate of rhythm control across practices 22.8%, range 0.2%-62.9%). Significant patient factors associated with rhythm control therapy included white (vs nonwhite) race (odds ratio [OR] 2.43, P < .001), private (vs nonprivate) insurance (OR 1.04, P < .001), and whether a patient was seen by an electrophysiologist (OR 1.77, P < .001). In an analysis of the relative contribution of patient, practice, and unmeasured practice factors with rhythm control, the contribution of unmeasured practice factors (95% range OR 0.29-3.44) exceeded that of either patient (95% range OR 0.46-2.30) or practice (95% range OR 0.15-2.77) factors.One in 5 AF patients in the PINNACLE registry received rhythm control, and 1 in 50 received catheter ablation, suggesting that rhythm control may be underused. A variety of measured and unmeasured practice factors unrelated to patient characteristics play a disproportionate role in the use of rhythm control treatment decisions. Understanding the drivers of these decisions may identify inappropriate treatment variation and better inform optimal use of these therapies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".