Choice of stent and outcomes after treatment of drug‐eluting stent restenosis in highly complex lesions
Bibliographic record
Abstract
OBJECTIVES: Our aim was to compare the outcomes of a same versus different drug-eluting stent (DES) implantation strategy for the treatment of DES instent restenosis (ISR). BACKGROUND: The absence of clear data renders the treatment of DES ISR one of the most challenging situations in interventional cardiology. METHODS: We identified all cases of DES ISR treated with a second DES between January 2004 and January 2009. The lesions were divided into those treated with the same DES as the initial one that restenosed and those treated with a different DES. The main end-point was repeat target lesion revascularization (TLR). RESULTS: We included 116 patients with a total of 132 lesions. The patient population was highly complex: 55.5% with diabetes, 56% with type-C lesions, 15.9% with lesions previously stented with BMS and 18.2% with fluoroscopic evidence of stent fracture. A same and different stent strategy was conducted in 41 lesions (31%) and 91 lesions (69%), respectively. Overall TLR was 31.1% and occurred in 46.3% of patients treated with the same stent and 24.4% of those with a different stent (P = 0.012). Multivariable analysis found same stent strategy (OR 2.84, 95%CI 1.23-6.57;P = 0.014) and occurrence of stent fracture (OR 4.03, 95%CI 1.33-12.01;P = 0.012) to be the only independent predictors of TLR after a median follow-up of 20.4 [12.1-30.2] months. CONCLUSIONS: In highly complex lesions, DES implantation for DES ISR is linked to a high need of future revascularization. An association between implanting a DES type other than the original and lower rate of TLR is suggested.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".