Influence of preoperative frailty on quality of life after cardiac surgery: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Frailty has emerged as an important prognostic marker of increased mortality after cardiac surgery, but its association with quality of life (QoL) and patient-centered outcomes is not fully understood. We sought to evaluate the association between frailty and such outcomes in older patients undergoing cardiac surgery. METHODS: This systematic review included studies evaluating the effect of preoperative frailty on QoL outcomes after cardiac surgery amongst patients 65 years and older. The primary outcome was patient's perceived change in QoL following cardiac surgery. Secondary outcomes included residing in a long-term care facility for 1 year, readmission in the year following the intervention, and discharge destination. Screening, inclusion, data extraction, and quality assessment were performed independently by two reviewers. Meta-analyses based on the random-effects model were conducted. The evidential quality of findings was assessed with the GRADE profiler. RESULTS: After the identification of 3105 studies, 10 observational studies were included (1580 patients) in the analysis. Two studies reported on the change in QoL following cardiac surgery, which was higher for patients with frailty than for patients without. Preoperative frailty was associated with both hospital readmission (pooled odds ratio [OR] 1.48 [0.80-2.74], low GRADE level) as well as non-home discharge (pooled OR 3.02 [1.57-5.82], moderate GRADE level). CONCLUSION: While evidence in this field is limited by heterogeneity of frailty assessment and non-randomized data, we demonstrated that baseline frailty may possibly be associated with improved QoL, but with increased readmission as well as discharge to a non-home destination following cardiac surgery. These patient-centered outcomes are important factors when considering interventional options for older patients. STUDY REGISTRATION: OSF registries (https://osf.io/vm2p8).
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".