Prevalence, predictors and clinical impact of unique and multiple chronic total occlusion in non-infarct-related artery in patients presenting with ST-elevation myocardial infarction
Bibliographic record
Abstract
OBJECTIVES: To investigate the predictors and impact on long-term survival of one chronic total occlusion (CTO) or multiple CTOs in patients presenting with ST-elevation myocardial infarction (STEMI). DESIGN: Single-centre retrospective observational study. SETTING: University-based tertiary referral centre. PATIENTS: Between 2006 and 2011, a total of 2020 consecutive patients referred with STEMI were categorised into single vessel disease, multivessel disease (MVD) without CTO, with one CTO or with multiple CTOs. INTERVENTION: Primary percutaneous coronary intervention. MAIN OUTCOME MEASURE: The primary end-point was the 1-year mortality. RESULTS: The prevalence of single vessel disease, MVD without CTO, with one CTO or with multiple CTOs was 70%, 22%, 7.2% and 0.8%, respectively. Independent clinical predictors for the presence of CTO were cardiogenic shock (OR 5.05; 95% CI 3.29 to 7.64), prior myocardial infarction (OR 2.06; 95% CI 1.35 to 3.09), age >65 years (OR 1.94; 95% CI 1.40 to 2.71) and history of angina (OR 1.94; 95% CI 1.29 to 2.87). Mortality was worse in patients with multiple CTOs (76.5%) compared with those with one CTO (28.1%) or without CTO (7.3%) (p<0.0001). After adjustment for left ventricular ejection fraction and renal function, MVD was an independent predictor for 1-year mortality (HR: 1.81; 95% CI 1.18 to 2.77, p=0.007), but CTO was not (HR: 1.07; 95% CI 0.66 to 1.73, p=0.78). CONCLUSIONS: Simple clinical factors are associated with the presence of CTO in non-infarct-related artery in patients presenting with STEMI. In these patients, long-term survival was independently associated with MVD, left ventricular ejection fraction and renal function, but not with CTO per se.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".