Influence of intensive lipid‐lowering on CT derived fractional flow reserve in patients with stable chest pain: Rationale and design of the FLOWPROMOTE study
Bibliographic record
Abstract
Abstract Introduction Coronary CT angiography (CTA) derived fractional flow reserve (FFRCT) shows high diagnostic performance when compared to invasively measured FFR. Presence and extent of low attenuation plaque density have been shown to be associated with abnormal physiology by measured FFR. Moreover, it is well established that statin therapy reduces the rate of plaque progression and results in morphology alterations underlying atherosclerosis. However, the interplay between lipid lowering treatment, plaque regression, and the coronary physiology has not previously been investigated. Aim To test whether lipid lowering therapy is associated with significant improvement in FFRCT, and whether there is a dose–response relationship between lipid lowering intensity, plaque regression, and coronary flow recovery. Methods Investigator driven, prospective, multicenter, randomized study of patients with stable angina, coronary stenosis ≥50% determined by clinically indicated first‐line CTA, and FFRCT ≤ 0.80 in whom coronary revascularization was deferred. Patients are randomized to standard (atorvastatin 40 mg daily) or intensive (rosuvastatin 40 mg + ezetimibe 10 mg daily) lipid lowering therapy for 18 months. Coronary CTA scans with blinded coronary plaque and FFRCT analyses will be repeated after 9 and 18 months. The primary endpoint is the 18‐month difference in FFRCT using (1) the FFRCT value 2 cm distal to stenosis and (2) the lowest distal value in the vessel of interest. A total of 104 patients will be included in the study. Conclusion The results of this study will provide novel insights into the interplay between lipid lowering, and the pathophysiology in coronary artery disease.
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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 | 0.019 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".