Incidence and Predictors of Outcome in the Treatment of In-Stent Restenosis with Drug-Eluting Balloons, a Real-Life Single-Centre Study
Bibliographic record
Abstract
Objectives: To determine the one-year and five-year occurrence and prognosticators of major adverse cardiac events (MACE: composition of all-cause death, myocardial infarction, target vessel revascularization, and vessel thrombosis), mortality, and target lesion revascularization (TLR) in patients with in-stent restenosis (ISR) treated with drug-eluting balloons (DEBs). Background: DEBs have become an emerging therapeutic option for ISR. We report the results of a single-center retrospective study on the treatment of ISR with DEB. Methods: 94 consecutive patients with ISR treated with the paclitaxel-eluting balloon were retrospectively studied between August 2011 and December 2019. Results: The one-year MACE rate was 11.8%, and the five-year MACE rate was 39.8%. The one-year mortality was 5.3%, and the five-year mortality rate was 21.5%. The one-year TLR rate was 4.3%, and the five-year rate was 18.7%. The univariable-Cox proportional hazard models for TLR showed lesion length, and the number of DEBs per vessel is associated with adverse outcomes with H.R. of 1.038 (1.007-1.069) and 4.7 (1.6-13.8), respectively. Conclusion: Our data indicate that at one year, DEBs provide an effective alternative to stenting for in-stent restenosis. Our five-year data, representing one of the longest-term follow-ups of DEB use, demonstrate high rates of MACE. The high five-year MACE reflects all-cause mortality in a high-risk population. This is offset by a reasonable five-year rate of TLR, indicating that DEB provides both short-term and long-term benefits in ISR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".