A “no‐option” left main PCI registry: Outcomes and predictors of in hospital mortality—utility of the logistic EuroSCORE
Bibliographic record
Abstract
BACKGROUND: Although high-risk left main PCI populations have been previously described, there is little data describing outcomes and the role of the logistic EuroSCORE in surgical turndown cohorts or patients in extremis due to acute infarction or cardiogenic shock from left main ischemia. METHODS: Consecutive patients with unprotected LM PCI who were surgical turndowns or in extremis were included in this retrospective cohort from 2004 to 2009 at two tertiary centers. Predictors of in-hospital mortality were identified utilizing routine and stepwise logistic regression. RESULTS: There were a total of 56 patients with mean age of 69 (±13). There were 23 (41%) patients with cardiogenic shock. The mean logistic EuroSCORE was 23.5% ± 21%. In-hospital death occurred in 12 (21%) patients, largely restricted to the shock subgroup (11/12). Univariate predictors of mortality included peak CK levels (P = 0.01), transfusion (P = 0.01), cardiogenic shock (P < 0.002), male gender (P = 0.027), and logistic EuroSCORE (P = 0.01). Stepwise logistic regression yielded logistic EuroSCORE (P = 0.04, OR: 1.25 (95% CI: 1.01-1.56) for every 5% increase) and peak CK level (P = 0.001, OR: 1.23 (95% CI: 1.09-1.40) for every 500 unit increase) as independent predictors of in-hospital mortality. The AUC ROC for logistic EuroSCORE was 0.73; and for logistic EuroSCORE plus peak CK level was 0.89. CONCLUSION: PCI appears to be a reasonable option in the high risk "no option" LM population, with the logistic EuroSCORE and peak CK levels being independent predictors of in-hospital mortality. Specifically, the logistic EuroSCORE and peak CK level combined discriminate in-hospital mortality with a high degree of certainty.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".