Predicting operative mortality in octogenarians for isolated coronary artery bypass grafting surgery: a retrospective study
Bibliographic record
Abstract
BACKGROUND: Available cardiac surgery risk scores have not been validated in octogenarians. Our objective was to compare the predictive ability of the Society of Thoracic Surgeons (STS) score, EuroSCORE I, and EuroSCORE II in elderly patients undergoing isolated coronary artery bypass grafting surgery (CABG). METHODS: All patients who underwent isolated CABG (2002 - 2008) were identified from the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) registry. All patients aged 80 and older (n = 304) were then matched 1:2 with a randomly selected control group of patients under age 80 (n = 608 of 4732). Risk scores were calculated. Discriminatory accuracy of the risk models was assessed by plotting the areas under the receiver operator characteristic (AUC) and comparing the observed to predicted operative mortality. RESULTS: Octogenarians had a significantly higher predicted mortality by STS Score (3 ± 2% vs. 1 ± 1%; p < 0.001), additive EuroSCORE (8 ± 3% vs. 4 ± 3%; p < 0.001), logistic EuroSCORE (15 ± 14% vs. 5 ± 6%; p < 0.001), and EuroSCORE II (4 ± 3% vs. 2 ± 2%; p < 0.001) compared to patients under age 80 years. Observed mortality was 2% and 1% for patients age 80 and older and under age 80, respectively (p = 0.323). AUC revealed areas for STS, additive and logistic EuroSCORE I and EuroSCORE II, respectively, for patients age 80 and older (0.671, 0.709, 0.694, 0.794) and under age 80 (0.829, 0.750, 0.785, 0.845). CONCLUSION: All risk prediction models assessed overestimated surgical risk, particularly in octogenarians. EuroSCORE II demonstrated better discriminatory accuracy in this population. Inclusion of new variables into these risk models, such as frailty, may allow for more accurate prediction of true operative risk.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".