Age, Not Sex, Modifies the Effect of Frailty on Long-term Outcomes After Cardiac Surgery
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence of frailty in surgical patients and determine whether age and sex modify the relationship between frailty and long-term mortality. BACKGROUND: Frailty is a complex and prevalent clinical syndrome. The cardiac surgery literature consists mostly of small, single-center studies, and the epidemiology of frailty remains to be fully elucidated in a real-world surgical population. METHODS: This retrospective cohort study included patients who underwent coronary artery bypass grafting, and/or aortic, mitral or tricuspid valve surgery in Ontario, Canada, between 2008 and 2016. The primary outcome was all-cause mortality. Survival probabilities were calculated using the Kaplan-Meier method, and the association of covariates with the hazard of death was assessed using multivariable Cox proportional hazard models. Frailty was assessed using the Johns Hopkins Adjusted Clinical Groups frailty-defining diagnoses indicator. RESULTS: Of 72,824 patients, 11,685 (16%) were frail. At median 5 ± 2 years of follow-up, 2921 (25.0%) frail patients and 8637 (14.1%) non-frail patients had died [adjusted hazard ratio 1.60; 95% confidence interval (CI), 1.53-1.68]. The adjusted hazard ratio was highest in patients who underwent isolated mitral (2.18; 95% CI, 1.71-2.77) and mitral + aortic valve surgery (1.85; 95% CI, 1.33-2.58) and lowest after coronary artery bypass grafting + mitral valve surgery (1.38; 95% CI, 1.11-1.70). Age, but not sex, modified the effect of frailty on mortality; such that the rate of death decreased linearly with increasing patient age. CONCLUSIONS: We observed a high prevalence of frailty in patients undergoing cardiac surgery, and a statistically significant association between frailty and long-term mortality after cardiac procedures. Importantly, the rate of death was inversely proportional to age, such that frailty had a stronger adverse impact on younger patients. Our findings highlight the need to incorporate frailty into the preoperative risk stratification and investigate strategies to support younger patients who are frail.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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".