Impact of intracoronary assessments on revascularization decisions: A contemporary evaluation
Bibliographic record
Abstract
OBJECTIVES: To investigate the real-world implementation of intracoronary assessment (ICA) techniques and evaluate their impact on clinical decisions regarding the management of coronary artery disease (CAD) in contemporary practice. BACKGROUND: Coronary angiogram is the gold standard used to diagnose vessel stenosis and guide percutaneous coronary intervention (PCI); however, it is limited by its two-dimensional imaging capabilities. ICA techniques like intravascular ultrasound and optical coherence tomography capture the vessel in three-dimensional images. Comparatively, fractional flow reserve provides information on the physiologic significance of coronary stenosis. Both techniques may improve PCI outcomes if they routinely change physician behavior. METHODS: Patients who underwent ICA between August 2015 and March 2020 were included in the study. The primary outcome was the clinical impact of ICA on physician clinical decision making of a stenotic vessel. The secondary outcome was the clinical changes that occurred following ICA. RESULTS: A total of 1135 patients were included in the study. Physiologic assessment (PA) and image assessment (IA) were performed in 61.4% and 38.6% respectively. Management plans were changed in 38.1% and 23.9% of patients who received PA and IA. Over half of the management change resulted in physicians deciding to not intervene on the stenotic vessel. One-year outcome of these decisions showed no significant increase in major adverse cardiac events (hazard ratio [HR], 0.68; 95% confidence interval [CI], 0.40-1.15; p = 0.15) or unplanned revascularization (HR, 0.78; 95% CI, 0.35-1.74; p = 0.55) suggesting reliance on PA/IA data did not increase risk. CONCLUSION: Selected ICA alters physician management of CAD in one-third of patients being evaluated for revascularization-typically leading to fewer interventions. All cause death is numerally lower in patients that received a change in management. However, the 1-year outcome of these altered decisions does not appear to be significantly different.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".