Outpatient Prescription Practices in Patients with Atrial Fibrillation (From the NCDR PINNACLE Registry)
Bibliographic record
Abstract
This study sought to evaluate inappropriate prescribing practices in an atrial fibrillation (AF) population, as outlined by the 2016 ACC/AHA Clinical Performance and Quality Measures for Adults with Atrial Fibrillation or Atrial Flutter document. The 2016 AF quality measures document specified medications to avoid in certain AF populations, including aspirin and anticoagulant combination therapy in patients without cardiovascular disease, and non-dihydropyridine calcium channel blockers in patients with reduced ejection fraction. Using data from the NCDR PINNACLE registry, a national outpatient cardiology practice registry, we assessed rates of inappropriate prescription of two types of medications among AF outpatients from 5/1/2008-5/1/2016. Overall rates of inappropriate prescription and variation by practice were calculated. Patient and practice factors associated with inappropriate prescription were assessed in adjusted analyses. A total of 107,759 of 658,250 (16.4%) patients without cardiovascular disease were inappropriately prescribed an antiplatelet and anticoagulant together, and 5,731 of 150,079 (3.8%) patients with reduced ejection fraction were inappropriately prescribed a non-dihydropyridine calcium channel blocker. Overall, 14.8% of AF patients were prescribed medications that were not recommended. Both patient and practice factors were associated with inappropriate prescribing, and the adjusted practice-level median odds ratio for inappropriate prescription was 1.70 (95% CI: 1.61-1.82), indicating a 70% likelihood that 2 random practices would treat identical AF patients differently. In a large registry of AF patients treated in cardiology practices, overall rates of inappropriate prescription practices, as defined by the 2016 AF quality measures, were relatively low, but significant practice variation was present.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".