Frailty and poor functional status are common in arterial vascular surgical patients and affect postoperative outcomes
Bibliographic record
Abstract
OBJECTIVES: Increasing numbers of older people are undergoing emergency and elective arterial vascular procedures. Many older patients are frail which is a recognised predictor of adverse postoperative outcomes in other surgical specialties. This study in older patients undergoing arterial vascular surgery examined; the prevalence of preoperative frailty; the clinical feasibility of preoperatively measuring frailty and functional status; the association between these characteristics and adverse postoperative outcome. METHODS: Prospective observational study in patients aged over 60 years undergoing elective and emergency arterial vascular surgery. Baseline measures of frailty (Edmonton Frail Scale), functional status (gait velocity, timed up and go, hand grip strength) and cognitive function (Montreal Cognitive Assessment) were obtained preoperatively. The primary outcome measure Length of Stay (LOS) and secondary outcome measures of postoperative morbidity (medical and surgical complications), functional status and postoperative in-hospital mortality were recorded. RESULTS: 125 patients were recruited. Frailty was common in this older surgical population (52% EFS score of ≥ 6.5) with high frailty scores observed (mean EFS 6.6, SD 3.05) and poor functional status (60% had TUG > 15 s, 45% had gait velocity of < 0.6 m/s). Higher preoperative EFS (> 6.5) was univariately associated with longer LOS (≥ 12 days), composite measures of postoperative infections, postoperative medical complications and adverse functional outcomes. EFS ≥ 6.5 was predictive of LOS ≥ 12 days, adjusted for age (AUC 0.660, CI 0.541-0.779, p = 0.010). This association between EFS ≥ 6.5 and LOS ≥ 12 days was strengthened with the addition of MoCA < 24 (AUC 0.695, CI 0.584-0.806, p = 0.002). CONCLUSIONS: Patients aged over 60 years admitted for arterial vascular surgery were frail, had impaired functional status and were cognitively impaired. This combination of preoperative characteristics was predictive of longer hospital length of stay and associated with adverse postoperative outcome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".