Using Optical Coherence Tomography to Identify Lipid and Its Impact on Interventions and Clinical Events ― A Scoping Review ―
Bibliographic record
Abstract
BACKGROUND: Optical coherence tomographic (OCT) imaging has enabled identification of lipid, with increasing interest in how it may affect coronary interventions and clinical outcomes. This review summarizes the available evidence around OCT identification of lipid and its effect on interventions, clinical events, and the natural history of coronary disease. METHODS AND RESULTS: We conducted a scoping review using the Medline, HealthStar, and Embase databases for articles published between 1996 and 2021. We screened 1,194 articles and identified 51 for inclusion in this study, summarizing the key findings. The literature supports a common OCT definition of lipid as low-signal regions with diffuse borders, validated against histology and other imaging modalities with acceptable intra- and inter-rater reliability. There is evidence that OCT-identified lipid at the site of stent implantation increases the risk of edge dissection, incomplete stent apposition, in-stent tissue protrusion, decreased coronary flow after stenting, side branch occlusion, and post-procedural cardiac biomarker increases. In mostly retrospective studies, lipid indices measured at non-stented sites are associated with plaque progression and the development of recurrent ischemic events. CONCLUSIONS: There is extensive literature supporting the ability of OCT to identify lipid and demonstrating a substantial impact of lipid on percutaneous coronary intervention outcomes. Future work to prospectively evaluate the effect of the characteristics of lipid-rich plaques on long-term clinical outcomes is needed.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".