Disability-free survival after major cardiac surgery: a population-based retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Cardiovascular research has traditionally been dedicated to "tombstone" outcomes, with little attention dedicated to the patient's perspective. We evaluated disability-free survival as a patient-defined outcome after cardiac surgery. METHODS: We conducted a retrospective cohort study of patients aged 40 years and older who underwent coronary artery bypass grafting (CABG) or single or multiple valve (aortic, mitral, tricuspid) surgery in Ontario between Oct. 1, 2008, and Dec. 31, 2016. The primary outcome was disability (a composite of stroke, 3 or more nonelective hospital admissions and admission to a long-term care facility) within 1 year after surgery. We assessed the procedure-specific risk of disability using cumulative incidence functions, and the relative effect of covariates on the subdistribution hazard using Fine and Gray models. RESULTS: The study included 72 824 patients. The 1-year incidence of disability and death was 2431 (4.6%) and 1839 (3.5%) for CABG, 677 (6.5%) and 539 (5.2%) for single valve, 118 (9.0%) and 140 (10.7%) for multiple valve, 718 (9.0%) and 730 (9.2%) for CABG and single valve, and 87 (13.1%) and 94 (14.1%) for CABG and multiple valve surgery, respectively. With CABG as the reference group, the adjusted hazard ratios for disability were 1.34 (95% confidence interval [CI] 1.21-1.48) after single valve, 1.43 (95% CI 1.18-1.75) after multiple valve, 1.38 (95% CI 1.26-1.51) after CABG and single valve, and 1.78 (95% CI 1.43-2.23) after CABG and multiple valve surgery. Combined CABG and multiple valve surgery, heart failure, creatinine 180 μmol/L or greater, alcohol use disorder, dementia and depression were independent risk factors for disability. INTERPRETATION: The cumulative incidence of disability was lowest after CABG and highest after combined CABG and multiple valve surgery. Our findings point to a need for models that predict personalized disability risk to enable better patient-centred care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".