Abstract 215: Use of Chronic Medications among Patients with Non-Valvular Atrial Fibrillation
Bibliographic record
Abstract
Introduction: Patients with non-valvular atrial fibrillation (NVAF) typically have other chronic comorbid conditions. However, little is known about the dosing regimens of medications that are prescribed to NVAF patients. Objectives: Estimate the proportion of real world NVAF patients with at least 1 CHADS 2 risk factor for stroke that may take chronic medications more than once per day. Methods: Using claims data from the Truven Health MarketScan® Commercial Claims and Encounters database, which contains health insurance claims data on a national sample of privately insured individuals, we identified NVAF patients with CHADS 2 ≥ 1 during the period January 1, 2008 through September 30, 2012. Chronic medications were identified as those for which the patient had at least a 90 day supply during the study period, excluding oral anticoagulants. A “medication portfolio” was developed for each NVAF patient characterizing the chronic prescription medications taken on a regular basis. Information from the medication’s FDA approved product label and pharmacy claims data were used to add detail to each patient’s medication list. This detail included the medication name, recommended dosage and administration, and prescribed strength, quantity, and days supply. Patients were identified as taking medications multiple times per day if they were prescribed a medication with a >1 time per day dosing regimen per the product label, or one medication typically taken in the morning and another medication typically taken in the evening, or, in cases where the typical administration was unclear from the label or from clinical practice, were prescribed more than 1 pill per day, while the total daily strength of the medication was equal to an available strength of a single pill. The proportion of NVAF patients that took medications multiple times per day was then determined. Results: Overall, 324,172 NVAF patients were selected for the study. The mean age of the patients was 75.3 years and over half (54.6%) of the patients were male. Of these patients, 299,716 (92.5%) were prescribed chronic medications and 215,527 (66.5%) were identified as possibly taking medications more than once per day. Overall, among patients who were prescribed chronic medications, 71.9% were identified as possibly taking their medications more than once per day. Conclusion: Patients with NVAF typically have other chronic conditions and a large proportion of these patients may be on treatment regimens that require taking medications more than once per day.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".