Optimizing rotational atherectomy in high‐risk percutaneous coronary interventions: Insights from the PROTECT ΙΙ study
Bibliographic record
Abstract
OBJECTIVE: To study rotational atherectomy (RA) outcomes in patients undergoing high-risk PCI randomized to receive hemodynamic support using either IABP or Impella 2.5 in the PROTECT II trial. BACKGROUND: RA of heavily calcified lesions is often necessary for complex PCI but can be associated with slow-flow, hypotension, and higher risk of periprocedural MI. METHODS: We compared baseline, angiographic, procedural characteristics, and outcomes of patients treated with and without RA. We examined also RA technique and outcomes. RESULTS: RA was used in 52 of 448 patients (32 with Impella vs 20 with IABP, P = 0.08). RA patients were older (72 vs. 67 yo, P = 0.0009), more likely to have prior CABG (48 vs. 32%, P = 0.017), higher STS (8.1 vs. 5.7, P = 0.012) and higher SYNTAX scores (37 vs. 29, P < 0.0001). At 90 days, RA use was associated with higher incidence of MI but no mortality difference. RA was used more aggressively with Impella resulting in higher rate of periprocedural MI (P < 0.01), with no difference in mortality between groups (P = 0.78). Repeat revascularization occurred less frequently with Impella (P < 0.001). There were no differences in 90-day major adverse events between IABP and Impella in patients undergoing RA (P = 0.29). In patients not treated with RA, fewer MAEs were observed with Impella compared with IABP (P = 0.03). CONCLUSIONS: Patients who were treated with RA had more comorbidities, and more complex and extensive coronary artery disease. In patients with Impella, more aggressive RA use resulted in fewer revascularization events but higher incidence of periprocedural MI.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".