Mechanisms of periprocedural myocardial necrosis following Rotablator® and saphenous vein graft PCI
Bibliographic record
Abstract
Objective: Rotational atherectomy device (Rotablator®) or saphenous vein graft (SVG) percutaneous coronary intervention (PCI) commonly results in elevated creatinine kinase-MB (CK-MB) levels and is associated with worse outcome. We have assessed the prevalence and mechanistic causes of procedural myocardial necrosis (PMN) following Rotablator® or SVG PCI. Methods: We assessed 46 cases of Rotablator® and 230 cases involving SVG PCI among the 7,700 total PCIs observed during a 12-year period (1996-2007). Following analysis of angiogram results, procedural data, and the delay between PCI and necrosis, we mechanistically classified PMN as follows: cryptogenic, immediate failure, side branch occlusion, stent thrombosis, prolonged ischemia, delayed failure, or none to communicate. Results: The incidence of elevated CK-MB greater than the upper limit of normal (ULN) after Rotablator® and SVG PCI was 19% and 7%, respectively. Important periprocedural myocardial infarction (important PMI; CK-MB >5 times ULN) was significant for both Rotablator® (56%) and SVG (70%) PCI. Also, we found that the mechanisms contributing to PMN in Rotablator® (n=9) were most often cryptogenic (56%), followed by immediate failure (22%) and prolonged ischemia (22%). For SVG PCI (n=17), cryptogenic (47%) was also the most common mechanism, followed by prolonged ischemia (29%), immediate failure (12%), stent thrombosis (6%), and none to communicate (6%). Conclusions: Rotablator® and SVG PCI led to increased risk for PMN and important PMI in comparison to the standard population. Also, we observed that the main mechanism of PMN for both Rotablator® and SVG PCI was cryptogenic, with micro-embolization likely acting as the primary etiology.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".