Frailty Factors and Outcomes in Vascular Surgery Patients
Bibliographic record
Abstract
OBJECTIVE: To describe and critique tools used to assess frailty in vascular surgery patients, and investigate its associations with patient factors and outcomes. BACKGROUND: Increasing evidence shows negative impacts of frailty on outcomes in surgical patients, but little investigation of its associations with patient factors has been undertaken. METHODS: Systematic review and meta-analysis of studies reporting frailty in vascular surgery patients (PROSPERO registration: CRD42018116253) searching Medline, Embase, CINAHL, PsycINFO, and Scopus. Quality of studies was assessed using Newcastle-Ottawa scores (NOS) and quality of evidence using Grading of Recommendations Assessment, Development, and Evaluation criteria. Associations of frailty with patient factors were investigated by difference in means (MD) or expressed as risk ratios (RRs), and associations with outcomes expressed as odds ratios (ORs) or hazard ratios (HRs). Data were pooled using random-effects models. RESULTS: Fifty-three studies were included in the review and only 8 (15%) were both good quality (NOS ≥ 7) and used a well-validated frailty measure. Eighteen studies (62,976 patients) provided data for the meta-analysis. Frailty was associated with increased age [MD 4.05 years; 95% confidence interval (CI) 3.35, 4.75], female sex (RR 1.32; 95% CI 1.14, 1.54), and lower body mass index (MD -1.81; 95% CI -2.94, -0.68). Frailty was associated with 30-day mortality [adjusted OR (AOR) 2.77; 95% CI 2.01-3.81), postoperative complications (AOR 2.16; 95% CI 1.55, 3.02), and long-term mortality (HR 1.85; 95% CI 1.31, 2.62). Sarcopenia was not associated with any outcomes. CONCLUSION: Frailty, but not sarcopenia, is associated with worse outcomes in vascular surgery patients. Well-validated frailty assessment tools should be preferred clinically, and in future research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".