Derivation and Validation of Prognosis-Based Age Cutoffs to Define Elderly in Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: The age cutoff to define elderly is controversial in cardiac surgery, empirically ranging from ≥65 to ≥80 years. Beyond semantics, this has important implications as a starting point for clinical care pathways and inclusion in trials. We sought to characterize the relationship between age and adverse outcomes in patients undergoing cardiac surgery and to derive and validate prognosis-based age cutoffs. METHODS AND RESULTS: Six thousand five hundred seventy one consecutive adult patients undergoing cardiac surgery at 3 hospitals in the United States and Canada were included in the cohort. Logistic regression models and generalized additive models with thin-plate splines were fit to the data. The age distribution was 50 to 59 years in 1244 (18.9%), 60 to 69 years in 2144 (32.6%), 70 to 79 years in 2000 (30.4%), ≥80 years in 1183 (18.0%) patients. After controlling for sex and type of operation, the relationship between age and 30-day operative mortality was found to be nonlinear. Receiver operating characteristic analysis showed that the optimal cutoffs to identify older patients at higher risk of operative mortality were greater than 74, 78, and 75 years for isolated coronary bypass, isolated valve surgery, and coronary bypass plus valve surgery, respectively. These age cutoffs were validated in an independent cohort. CONCLUSIONS: The relationship between age and operative mortality is not linear, manifesting a steeper rise after age 75 for coronary bypass and approaching octogenarian age for isolated valve surgery. Rather than using arbitrary age cutoffs to define elderly, the outcomes-based cutoff of ≥75 years should be used to identify the population of older adults that has higher risk and may benefit from preoperative geriatric evaluation and optimization.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".