Immediate and Midterm Efficacy and Safety of Intravascular Lithotripsy for Calcified In‐Stent Restenosis: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background In‐stent restenosis (ISR) remains a major challenge in coronary revascularization, especially in lesions with heavy calcification, where recurrence rates are high. The lack of data on newer treatment options often creates dilemmas for both physicians and patients. Intravascular lithotripsy (IVL) is a relatively new technique that has shown promise in managing calcified ISR. This meta‐analysis aims to evaluate the procedural success and clinical outcomes of IVL in this setting. Methods Following PRISMA 2020 guidelines, a systematic search was conducted on PubMed, ScienceDirect, and SCOPUS. Studies reporting procedural success and major adverse cardiovascular events (MACE) of IVL for calcified ISR during follow‐up were included. The Newcastle‐Ottawa Scale was used to assess study quality. Data analysis was performed using RevMan 5.4.0 and SPSS v25. Results Five studies with a total of 207 patients and 212 lesions treated were analyzed. Acute procedural success was 85% (95% CI 0.76–0.91, I ² = 42.0%). At 1‐year follow‐up, major adverse cardiac events (MACE) included myocardial infarction (MI) in 6% of patients (95% CI 0.02–0.16, I ² = 52.5%), target lesion revascularization in 13% (95% CI 0.08–0.20, I ² = 45.8%), and cardiac death in 4% (95% CI 0.02–0.08, I ² = 0.0%). The rate of periprocedural MI was 1.5% (95% CI 0.01‐0.05, I 2 = 0.0%), while no reflow phenomenon was 0.5% (95% CI 0.01‐0.06, I 2 = 0.0%), with no other procedural complication occured. The overall incidence of MACE was 16% (95% CI 0.07–0.33, I ² = 69.8%). Conclusion IVL shows potential as a treatment option for for calcified‐ISR, demonstrating high acute procedural success and acceptable 1‐year clinical outcomes. The 1‐year TLR (13%) and MACE (16%) rates compare favorably with historical data for non‐IVL strategies. Further large‐scale, controlled studies with longer follow‐up are needed to further validate its long‐term safety and effectiveness.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".