Association of Multimorbidity With Frailty in Older Adults for Elective Non-Cardiac Surgery
Bibliographic record
Abstract
Introduction Frailty is associated with adverse surgical outcomes. While existing studies describe the prevalence of multimorbidity and frailty in the community, the surgical population may have more severe disease and significant surgical stress. This study aims to describe the distribution of frailty and multimorbidity in the older surgical population and examine if specific comorbidities are more strongly associated with frailty. Methods This is a single-centre retrospective cohort study using an electronic database in the preoperative evaluation clinic, conducted in Singapore General Hospital, Singapore. All patients above 70 years old going for elective non-cardiac surgery were included. Demographics and comorbidities were analysed for their association with frailty according to the Edmonton Frail Scale. Results A total of 1396 out of 1398 patients were analyzed. The overall incidence of frailty was 27.8% and multimorbidity was 63.4%. Factors independently associated with frailty were age (adjusted Odds Ratio [aOR] = 1.07), female gender (aOR = 1.67), type 2 diabetes mellitus (aOR = 1.69), chronic kidney disease (aOR = 1.47), end-stage renal failure (aOR = 3.58), history of cerebrovascular accident or transient ischemic attack (aOR = 1.87), moderate anaemia (aOR = 2.11), dementia (aOR = 6.38), depression (aOR = 3.82), and peptic ulcer disease (aOR = 1.98). The presence of multi-morbidity was significantly associated with frailty, with overall increasing strength of association. Conclusion As the number of comorbidities increases, the odds of frailty increase. Only a small proportion of those with multimorbidity accumulate enough biological deficits to develop frailty, putting them at higher risk than with solely multimorbidity or frailty. Dementia and depression are comorbidities with strong associations that have yet to see coordinated interventional efforts in the preoperative setting.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".