Low-speed rotational atherectomy with substantial debulking and long-term survival: a retrospective observational study of 889 consecutive patients
Bibliographic record
Abstract
BACKGROUND In recent years, a series of studies have investigated long-term outcome of rotational atherectomy (RA). However, only little attention was given to the procedural details of RA technique, especially with regard to platform speed. AIMS The aim of the study was to evaluate the impact of low-speed RA with substantial debulking before stenting on long-term mortality. METHODS A group of 356 patients with a high plaque burden underwent substantial debulking with RA with low platform speed of 135 000 rpm and was compared with a group of 553 patients treated without RA in the same time period. In both cohorts lesion preparation was followed by stent implantation. The endpoint was all-cause mortality up to 80 months with a mean (SD) follow-up of 49 (24) months. RESULTS Despite the fact that patients treated with RA were significantly older and presented more unfavorable lesion characteristics than non-RA patients, there was no significant difference in long-term survival between groups. A propensity analysis with 279 matched pairs showed that long-time survival in RA patients was better than in non-RA patients (hazard ratio, 0.52; CI, 0.32-0.85; P <0.01). CONCLUSIONS Substantial debulking with RA continues to play a role in the treatment of calcified coronary arteries. We hypothesize that RA with low platform speed and substantial debulking with a burr-to-artery ratio of up to 0.64 results in favorable long-time survival in patients with high plaque burden. However, this can only be proved in a prospective randomized trial.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".