Pharmacy quality alliance measure: adherence to non-warfarin oral anticoagulant medications
Bibliographic record
Abstract
BACKGROUND: The Pharmacy Quality Alliance (PQA) recently endorsed adherence to non-warfarin anticoagulant agents as a new performance measure, but the Medicare Part D Star Ratings program has not yet adopted the measure. The current study aims to assess the real-world adherence to medication of patients who used non-vitamin-K-antagonist oral anticoagulants (NOACs) based on the PQA's adherence measure. METHODS: Healthcare claims from the Humana database during the year of 2013 were analyzed. Patients older than 18 with ≥2 dispensings of NOAC agents, at least 180 days apart between two NOAC dispensings in 2013 (a criterion to include chronic users), with ≥60 days of supply, and ≥180 days of continuous enrollment prior to the index NOAC were identified. The PQA measure on the index therapy was calculated as the percentage of patients who had a proportion of days covered (PDC) ≥0.8 during their follow-up. RESULTS: A total of 9948 NOAC users (rivaroxaban: n = 4194, dabigatran: n = 5489, apixaban: n = 265) were identified. For rivaroxaban users, the proportion of patients with a PDC ≥0.8 (PQA measure) at 75.4% was significantly higher compared to dabigatran users (67.6%; P < 0.001) and higher compared to apixaban users (70.6%; P = 0.076). When allowing switches to other NOAC agents in the PQA measure, rivaroxaban users had a significantly higher PQA measure at 76.9% compared to both dabigatran (72.9%; P < 0.001) and apixaban (71.3%; P = 0.037) users. Multivariate logistic regression analyses corroborated the findings that rivaroxaban had a significantly higher adherence compared to the other NOAC agents. LIMITATIONS: Claims data may have contained inaccuracies, possible change in patterns over time, and the impossibility of knowing whether all supplied tablets were taken. CONCLUSION: Based on the PQA's adherence measure, rivaroxaban users were found to have a higher adherence compared to dabigatran and apixaban users. Healthcare providers may want to consider the impact of anticoagulation selection on their ability to achieve quality metrics.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".