Variations in Use of Optimal Medical Therapy in Patients With Nonobstructive Coronary Artery Disease: A Population‐Based Study
Bibliographic record
Abstract
BACKGROUND: There is a paucity of data on the need for optimal medical therapy (OMT) in nonobstructive coronary artery disease . We sought to understand if there was variation in the use of OMT between hospitals for patients with nonobstructive coronary artery disease, the factors associated with such variation, and its clinical consequences. METHODS AND RESULTS: Using a population-level clinical registry in Ontario, Canada, we identified all patients >66 years undergoing coronary angiography for the indication of stable angina, who had nonobstructive coronary artery disease between November 1, 2010, and October 31, 2013. Hierarchical multivariable logistic models were developed to identify the factors associated with OMT use, with median odds ratio used to quantify the degree of variation between hospitals not explained by the modeled risk factors. Clinical outcomes of interest were all-cause mortality and rehospitalization, with follow-up until March 31, 2015. Our cohort consisted of 5413 patients, of whom 2554 (47.2%) were receiving OMT within 1 year. There was a 2-fold variation in OMT across hospitals (30.4%-61.8%). The variation between hospitals was fully explained by preangiography medication use (median odds ratio of 1.21 in the null model and 1.03 in the full model). There was no difference in risk-adjusted mortality (hazard ratio, 0.94; 95% confidence interval, 0.76-1.16); however, patients receiving OMT had a lower risk of all-cause hospital readmission (hazard ratio, 0.89; 95% confidence interval, 0.84-0.95). CONCLUSIONS: There is wide variation in the use of OMT in patients with nonobstructive coronary artery disease, the major driver of which is differences in baseline medication use.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".