Derivation and validation of predictive indices for 30-day mortality after coronary and valvular surgery in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Coronary artery bypass grafting (CABG) and surgical aortic valve replacement (AVR) are the 2 most common cardiac surgery procedures in North America. We derived and externally validated clinical models to estimate the likelihood of death within 30 days of CABG, AVR or combined CABG + AVR. METHODS: We obtained data from the CorHealth Ontario Cardiac Registry and several linked population health administrative databases from Ontario, Canada. We derived multiple logistic regression models from all adult patients who underwent CABG, AVR or combined CABG + AVR from April 2017 to March 2019, and validated them in 2 temporally distinct cohorts (April 2015 to March 2017 and April 2019 to March 2020). RESULTS: The derivation cohorts included 13 435 patients who underwent CABG (30-d mortality 1.73%), 1970 patients who underwent AVR (30-d mortality 1.68%) and 1510 patients who underwent combined CABG + AVR (30-d mortality 3.05%). The final models for predicting 30-day mortality included 15 variables for patients undergoing CABG, 5 variables for patients undergoing AVR and 5 variables for patients undergoing combined CABG + AVR. Model discrimination was excellent for the CABG (c-statistic 0.888, optimism-corrected 0.866) AVR (c-statistic 0.850, optimism-corrected 0.762) and CABG + AVR (c-statistic 0.844, optimism-corrected 0.776) models, with similar results in the validation cohorts. INTERPRETATION: Our models, leveraging readily available, multidimensional data sources, computed accurate risk-adjusted 30-day mortality rates for CABG, AVR and combined CABG + AVR, with discrimination comparable to more complex American and European models. The ability to accurately predict perioperative mortality rates for these procedures will be valuable for quality improvement initiatives across institutions.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".