Scoring System for Identification of “Survival Advantage” after Successful Percutaneous Coronary Intervention in Patients with Chronic Total Occlusion
Bibliographic record
Abstract
Background: Percutaneous coronary intervention (PCI) is widely used in patients with chronic total occlusion (CTO), but its benefit in improving long-term outcomes is controversial. We aimed to develop a prediction score for grading “survival advantage” conferred by successful results of CTO-PCI and a scoring system for prediction of the influence of CTO-PCI results on major adverse cardiac and cerebrovascular events (MACCEs). Methods: Follow-up data of 2625 patients who underwent CTO-PCI at 65 Japanese centers were analyzed. An integer scoring system was developed by including statistical effect modifiers on the association between successful CTO-PCI and one-year mortality. Results: Follow-up at 12 months was completed in 2034 patients. During follow-up, 76 deaths (3.7%) occurred. Patients with successful CTO-PCI had a better one-year survival than patients with failed CTO-PCI (log rank P = 0.016). Effect modifiers for the association between successful procedure and one-year mortality included diabetes (P interaction = 0.043), multivessel disease (P interaction = 0.175), Canadian Cardiovascular Society class ≥2 (P interaction = 0.088), and prior myocardial infarction (MI) (P interaction = 0.117). Each component was assigned a single point and summed to develop the scoring system. The patients were then categorized to specify the prediction of survival advantage by successful PCI: ≤2 (normal) and ≥3 (distinct). The differences in one-year mortality between patients with successful and failed treatment were −0.7% and 11.3% for normal and distinct score categories, respectively. In the scoring system for MACCE, score components were prior MI (P interaction = 0.19), left anterior descending artery (LAD)-CTO (P interaction = 0.079), and reattempt of CTO-PCI (P interaction = 0.18). The differences in one-year MACCEs between successful and failed patients for each score category (0, 1, and ≥2) were −1.7%, 7.5%, and 15.1%, respectively. Conclusions: The novel scoring system assessing the advantage of successful PCI can be easily applied in patients with CTO. It is a valid instrument for clinical decision-making while assessing the survival advantage of CTO-PCI and the influence of procedural results on MACCEs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".