114Prevalence Of Frailty And Its Association With The Composite Outcome Of Mortality At 90- Day And Readmission At 30-Day In Older Surgical Patients
Bibliographic record
Abstract
With the current demographic trends, there will be a rising number of older people presenting with acute surgical problems. While older age is associated with increased surgical mortality, the extent to which associated frailty has impact on mortality and other important outcome of readmission is less well researched. Therefore, we set out to assess if frailty predicts these outcomes of older patients presenting to hospital with surgical emergencies. We examined the risk for mortality at 90 days or readmission at 30 days with factors of: frailty; length of hospitalisation; readmissions; polypharmacy and other potential confounders in older acute general surgical population using Older Persons Surgical Outcomes Collaboration 2013 and 2014 data. The frailty was measured using the validated 7-point Canadian study of health and ageing clinical frailty score and categorised into three groups; very fit, (1-2); frail (3-4); and very frail (5-7). Multivariable logistic regression fitting a parsimonious forward stepping approach of nested models using a likelihood ratio test (p < 0.05) was performed. The 742 recruited patients had a mean age of 77.2 years (SD = 8.2 years), 54% (401/742) were female. Prevalence of frailty was 31.4% (233) not frail, 39.8% (295) frail, and 28.8% (214) very frail in this unselected sample of surgical emergency admissions during the study periods. Only frailty, site and abnormal albumin included in the regression were predictive of mortality and/or readmission (MR). Compared to those not frail, those that were frail and very frail had increased odds of MR of 2.1 (95% CI 1.3–3.3; P = 0.001) and 3.3 (95% CI 2.2–4.9; P < 0.0001), respectively. Abnormal albumin increased the odds of MR of 56% (95% CI 1.1–2.3; P = 0.019). Approximately two thirds of acute surgical admissions are frail older people in the UK setting. There appear to be a clear dose response relationship between frailty, albumin and mortality and readmission in this population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".