Addition of Frailty and Disability to Cardiac Surgery Risk Scores Identifies Elderly Patients at High Risk of Mortality or Major Morbidity
Bibliographic record
Abstract
Background- Cardiac surgery risk scores perform poorly in elderly patients, in part because they do not take into account frailty and disability which are critical determinants of health status with advanced age. There is an unmet need to combine established cardiac surgery risk scores with measures of frailty and disability to provide a more complete model for risk prediction in elderly patients undergoing cardiac surgery. Methods and Results- This was a prospective, multicenter cohort study of elderly patients (≥70 years) undergoing coronary artery bypass and/or valve surgery in the United States and Canada. Four different frailty scales, 3 disability scales, and 5 cardiac surgery risk scores were measured in all patients. The primary outcome was the STS composite end point of in-hospital postoperative mortality or major morbidity. A total of 152 patients were enrolled, with a mean age of 75.9±4.4 years and 34% women. Depending on the scale used, 20-46% of patients were found to be frail, and 5-76% were found to have at least 1 disability. The most predictive scale in each domain was: 5-meter gait speed ≥6 seconds as a measure of frailty (odds ratio [OR], 2.63; 95% confidence interval [CI], 1.17-5.90), ≥3 impairments in the Nagi scale as a measure of disability (OR, 2.98; 95% CI, 1.35-6.56) and either the Parsonnet score (OR, 1.08; 95% CI, 1.04-1.13) or Society of Thoracic Surgeons Predicted Risk of Mortality or Major Morbidity (STS-PROMM) (OR, 1.05; 95% CI, 1.01-1.09) as a cardiac surgery risk score. Compared with the Parsonnet score or STS-PROMM alone, (area under the curve, 0.68-0.72), addition of frailty and disability provided incremental value and improved model discrimination (area under the curve, 0.73-0.76). Conclusions- Clinicians should use an integrative approach combining frailty, disability, and risk scores to better characterize elderly patients referred for cardiac surgery and identify those that are at increased 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".