Drug eluting stents for the treatment of bare metal in-stent restenosis: long-term outcomes in real world practice
Bibliographic record
Abstract
AIMS: Drug eluting stents (DES) have had a great impact in reducing in-stent restenosis (ISR) in de novo lesions. However, long-term data regarding effectiveness and safety of these stents in treating bare metal stent (BMS) ISR are limited. We report long-term clinical outcomes in a cohort of patients with BMS-ISR treated with DES between April 2002 and December 2003 at our institution. METHODS AND RESULTS: Sixty-nine consecutive patients with significant BMS-ISR were treated with DES implantation. Sirolimus DES were used in 43 patients and paclitaxel DES in 26. All patients were followed up to determine the incidence of major adverse cardiac event (MACE) rates (all-cause death, myocardial infarction, or target vessel revascularisation [TVR]), angina class and the need for clinically driven angiography. The mean age of the cohort was 58.6 ± 10.8 years; 68% were male, 33% were diabetic, 50% had hypertension, 78% were on statin therapy and 59% were current (19%) or previous (41%) smokers. The clinical presentation of ISR was with chronic stable angina in 54 patients, 12 had a non-ST elevation acute coronary syndrome and three presented with ST-elevation myocardial infarction. Multivessel stenting was performed in 21 patients and bifurcation stenting in seven patients. Over a mean follow period of 4.9 years, the first event MACE rate was 20% (17 events in 14 patients - eight deaths of which three were cardiac, two non-fatal myocardial infarctions and seven TVR). Excluding non-cardiac death, the adjusted MACE rate was 14.5% (12 events in 10 patients). At long-term follow-up, mean Canadian angina class decreased from 2.3 ± 0.7 pre-procedure to 1.2 ± 0.4, 65% of patients were angina free and 80% were free of MACE. No differences in long-term outcomes were observed between patients receiving paclitaxel and sirolimus DES. CONCLUSIONS: The use of DES for the treatment of BMS-ISR is safe and effective over a mean follow-up period of nearly five years. To our knowledge, this represents the longest follow-up data of real world patients treated in a single interventional centre.
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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 | 0.005 |
| 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 | 0.000 |
| 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".