Frailty in the over 65’s undergoing elective surgery (FIT-65) – a three-day study examining the prevalence of frailty in patients presenting for elective surgery
Bibliographic record
Abstract
BACKGROUND: Frailty increases the risk of perioperative complications, length of stay, and the need for assisted-living after discharge. As the UK population ages the number of frail patients presenting for elective surgery in the UK is likely to grow. Despite the potential benefits of early diagnosis, frailty is not uniformly screened for in UK elective surgical patients and its prevalence remains unclear. The primary aim of this study was to assess the prevalence of frailty in patients aged over 65 years undergoing elective surgery. METHODS: We performed a prospective cross-sectional observational study in eight UK hospitals. Data were collected over three consecutive days with follow-up at 30 days. HRA approval was obtained (REC 20/SC/0121) and signed informed consent obtained. Participants were eligible for inclusion if they were 65 years or older and undergoing elective surgery. Pre-operative data were collected from hospital notes by anaesthetic trainees. A member of the research team blinded to the pre-operative dataset screened each participant for frailty pre-operatively using the Reported Edmonton Frail Scale (REFS). Post-operative data were collected from the notes on day of surgery and at 30 days. Participants were defined as "frail" if they scored 8 or more on the REFS. RESULTS: Two hundred twenty eight participants were recruited during the study period of whom 218 proceeded to surgery. There were 103 females and 115 males. Median age was 75 years (interquartile range 70-80). Thirty-seven participants (17.0%) were identified as frail. Frail patients were older, had a higher ASA score, were more likely to have carers and were more likely to be anaemic or present with ECG abnormalities. There were no differences in gender, BMI, place of residence or smoking status for patients identified as frail versus non-frail. There was no difference in length-of-stay between frail and non-frail patients, although those identified as frail were less likely to be discharged to their own home. CONCLUSION: We found the prevalence of frailty in a mixed population of elective surgical patients aged 65 or over to be 17.0%. Furthermore, we found the REFS to be a practical tool for pre-operative frailty screening. Frail patients presented for elective surgery with modifiable co-morbidities which could have been optimised pre-operatively. Early screening could highlight frail patients, allowing time for pre-operative planning and evidence-based optimisations of comorbidities. We therefore encourage the adoption of frailty assessment as a routine part of pre-operative assessment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".