Use of Intravascular Imaging to Guide Percutaneous Coronary Interventions: Experience from a Single, High-Volume Canadian Centre
Bibliographic record
Abstract
Background Intravascular imaging (IVI) improves outcomes in complex percutaneous coronary intervention (PCI) and is recommended by the latest guidelines. However, data about its real-world application remains limited. Methods We conducted a retrospective audit of 300 consecutive PCI cases. Lesions were classified as complex if they involved >1 following characteristics: bifurcation, severe calcifications, CTO, long lesions, ostial location and involvement of the LM. IVI use was analyzed by lesion subtype and individual operator. Results Of 300 consecutive PCI cases, 146 (49%) were classified as complex PCI. IVI was used in 53% of complex PCIs and 23% of noncomplex PCIs. Among patients undergoing complex PCI, IVI was most frequently performed in CTOs (86%) and LM (76%), but its use remained below 50% for bifurcations, severe calcified and long lesions. IVI-guided PCI was associated with higher contrast use (215 ± 78 mL vs. 179 ± 65 mL, P=0.003) and longer procedural duration (72 ± 32 minutes vs. 51 ± 22 minutes, P < 0.001) and varied widely across operators, ranging from 0-79% in the overall population (p<0.001) to 0-90% in complex lesions (p=0.004). Notably, IVI adoption declined with increasing operator age (OR 0.88 per +1 year; 95% CI 0.78–0.98), whereas it increased with lesion complexity (OR 2.34 per additional complexity feature; 95% CI 1 1.62–3.39). Conclusions Despite current evidence and guideline recommendations, IVI use showed notable variation across operators. Standardizing IVI utilization through education, protocols, and system-level support will be essential to promote guideline-concordant practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 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".