Delay in Filling First Clopidogrel Prescription After Coronary Stenting Is Associated With an Increased Risk of Death and Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Patients frequently experience difficulties with medication compliance after hospital discharge. We investigated the effect of a delay in filling a first clopidogrel prescription after hospital discharge on clinical outcomes subsequent to coronary stenting. METHODS AND RESULTS: Hospital administrative, community pharmacy, and cardiac revascularization data were determined for all patients receiving a coronary stent in British Columbia 2004-2006 with follow-up out to 2 years. Cox's proportional hazard regression analysis, adjusting for baseline demographics and procedural variables, was performed to examine the effects of delay in filling a clopidogrel prescription after hospital discharge on clinical outcomes.Of 15 629 patients treated with coronary stents, 3599 received at least 1 drug-eluting stent (DES), whereas 12 030 received bare metal stents (BMS) alone. In total, 1064 (30%) and 3758 (31%) patients in the DES and BMS groups, respectively, failed to fill a prescription within 3 days of discharge (median, 1 day; interquartile range [IQR], 1 to 3). After regression analysis, a delay of >3 days was predictive of mortality and recurrent myocardial infarction (MI) irrespective of stent type (DES: hazard ratio [HR], 2.4; 95% confidence interval [CI], 1.7 to 3.4; and HR, 2.0; 95% CI, 1.5 to 2.7, respectively, and BMS: HR, 2.2; 95% CI, 1.9 to 2.6; and HR, 1.8; 95% CI, 1.5 to 2.1, respectively). This excess hazard was greatest in the 30-day period immediately after hospital discharge (mortality: HR, 5.5; 95% CI, 3.5 to 8.6; and MI: HR, 3.1; 95% CI, 2.4 to 4.0, for all patients). CONCLUSIONS: Delays in patients filling their first prescription for clopidogrel after coronary stenting are common and associated with adverse clinical outcomes, irrespective of stent type. Strategies to reduce delays have the potential to improve clinical outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".