Impact of intracoronary imaging-guided percutaneous coronary intervention on procedural outcomes among complex patient groups
Bibliographic record
Abstract
Abstract Background Intracoronary imaging (ICI) has been previously shown to improve survival and clinical outcomes after percutaneous coronary intervention (PCI). However, whether this prognostic benefit is sustained across different indications/patient groups remains unclear. Methods All PCI procedures performed in England and Wales between 1st April 2014 and 31st March 2020 were retrospectively analysed. The association between ICI use and in-hospital MACCE (major adverse cardiovascular and cerebrovascular outcomes; composite of all-cause mortality, stroke and reinfarction) and mortality was examined using multivariable logistic regression analysis for each imaging-recommended indication (stent thrombosis (ST), in-stent restenosis, stent length>60mm, acute coronary syndrome (ACS) indications, chronic total occlusion, left main stem (LMS) intervention, renal failure and bioresorbable vascular scaffolds (BVS)). Results Of 555,398 PCI procedures, 10.8% (n=59,752) were performed under ICI guidance. ICI use doubled between 2014 (7.8%) and 2020 (17.5%). ICI use was highest for BVS (44.7%) and LMS PCI (41.2%) cases and lowest in ACS (9%). Overall, the odds ratios (OR) of in-hospital MACCE and mortality were only reduced with ICI-guided PCI in cases with an imaging-recommended indication (OR 0.75 95% confidence interval (CI) 0.69-0.81 and OR 0.69 95%CI 0.63-0.76, respectively). Only specific imaging-recommended indications were associated with reduced MACCE and mortality, including LMS PCI (OR 0.45 95%CI 0.39-0.52 and 0.41 95%CI 0.35-0.48, respectively), ACS (OR 0.76 95%CI 0.70-0.82 and 0.70 95%CI 0.63-0.77), stent length>60mm (OR 0.75 95%CI 0.59-0.94 and 0.72 95%CI 0.54-0.95). ST was only associated with lower mortality (OR: 0.69 95%CI 0.52-0.91) while renal failure was associated with reduced MACCE (OR 0.77 95%CI 0.60-0.99) but not mortality. (Figure 1) Conclusion The utilisation of ICI has more than doubled over a seven-year period at a national level but remains low, with less than 1-in-5 procedures performed under ICI guidance. In-hospital survival was better with ICI-guided than angiography-guided PCI, albeit only for specific indications.Figure 1
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".