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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".