Preoperative Frailty and Quality of Life as Predictors of Postoperative Complications
Bibliographic record
Abstract
BACKGROUND: Prediction of postoperative complications has been based on assessing comorbidities. However, the evaluation of these comorbidities has not consistently identified those at higher risk of complications, primarily due to the inability to assess how these comorbidities affect functional status. We hypothesized that preoperative functional measures of patients' health status can predict postoperative complications. METHODS: A sample of patients undergoing general surgical operations were reviewed for age, gender, diagnosis (for severity), operations (for complexity), number of comorbidities, preoperative frailty (as determined by the Canadian Study of Health and Ageing Frailty Index), preoperative quality of life (as determined by the SF-36), occurrence of postoperative complications, number of postoperative complications, and severity of complications. Data were analyzed by linear and multiple logistic regression analyses, and the Mann-Whitney U test. RESULTS: Two hundred and twenty-six patients were evaluated, average age 61 ± 13 years, 47% male patients. Frailty Index (FI) correlated with number of comorbidities (r = 0.61, P < 0.001), and all of the domains of the SF-36. Patients who had postoperative complications had higher median preoperative FI than those would did not [0.075 (IQR 0.046-0.118) vs. 0.059 (IQR 0.045-0.089), P = 0.007]. Multiple logistic regression analysis demonstrated that operation complexity, FI, and the role-emotional domain were associated with and increased risk of postoperative complications, whereas the bodily pain domain was associated with a lower risk of postoperative complications. CONCLUSIONS: This study demonstrates that preoperative functional status as measured by FI and SF-36 may help identify patients at higher risk of postoperative complications. In our ageing population, use of such measures may help in better patient selection.
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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 | 0.006 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".