Use of Preventive Medications in Patients With Nonobstructive Coronary Artery Disease: Analysis of the PROMISE Trial
Bibliographic record
Abstract
Background Nonobstructive coronary artery disease (NOCAD) is commonly found on coronary computed tomography angiography (CCTA) during evaluation for coronary artery disease (CAD). There are no guidelines for the medical management of NOCAD, and practice is variable. We aimed to compare patterns of preventive medication use and continuation after identifying NOCAD vs normal coronaries or obstructive CAD on CCTA. Methods We analyzed data from the Pro spective M ulticenter I maging S tudy for E valuation of Chest Pain (PROMISE) trial dataset, restricted to patients with ≥2 follow-up visits after CCTA. We categorized patients as having either obstructive CAD, NOCAD, or normal coronaries. The primary outcome was the proportion of patients reporting continued use of combination preventive medications, defined as a statin, an antithrombotic, and a renin–angiotensin system blocker throughout follow-up after CCTA. Secondary outcomes included the proportion of visits reporting combination therapy and individual medications. Results We included 4388 patients, with a mean follow-up of 2.3 years. Most patients had NOCAD (48.6%), with normal coronaries in 38.9%, and obstructive CAD in 10.1%. Among NOCAD patients, the mean age was 61 years, and 47.2% were women. A total of 9.1% of NOCAD patients continued combination therapy, vs 12.4% with obstructive CAD, and 3.3% with normal coronaries ( P < 0.001), primarily due to lower use of statins and antithrombotic agents. Similarly, patients with obstructive CAD, NOCAD, and normal coronaries reported using combination therapy during a mean of 35%, 24%, and 9% of visits, respectively ( P < 0.001). Conclusions Few patients with NOCAD identified by CCTA used or continued combination preventive cardiovascular medications. Patients with NOCAD represent an at-risk population with potential for optimization of preventive medications.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".