Preoperative Frailty Is an Independent Risk Factor for Postinduction Hypotension in Older Patients Undergoing Noncardiac Surgery: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Intraoperative hypotension is a risk factor for perioperative adverse outcomes and is highly prevalent in older patients. Frailty has been associated with hemodynamic instability but its impact on postinduction hypotension is unclear. Therefore, we assessed the association between frailty and postinduction hypotension in older patients. METHODS: We retrospectively evaluated electronic medical records of patients aged ≥65 years who were assessed for preoperative frailty and underwent noncardiac surgery under general anesthesia. Reported Edmonton Frail Scale (REFS) scores were used to stratify patients into a nonfrail (REFS scores 0-5), prefrail (6-7), and frail (8-18) groups. Postinduction hypotension was defined as a mean blood pressure below 65 mmHg or 20% from baseline occurring within the first 20 minutes after anesthesia induction and evaluated using multivariate logistic regression analysis. RESULTS: Independent factors related to postinduction hypotension in our sample (421 patients) were status of frail (REFS score ≥8) compared to nonfrail (odds ratio [OR], 2.73; 95% confidence interval [CI], 1.44-5.18; p = .002), lower baseline mean blood pressure in the operating room (OR, 0.98; 95% CI, 0.96-0.999; p = .034) and at the presurgical center (OR, 0.96; 95% CI, 0.94-0.99; p = .003), and orthopedic (compared to urologic) surgery (OR, 2.22; 95% CI, 1.14-4.30; p = .019). CONCLUSION: Preoperative frail status based on REFS scores is associated with postinduction hypotension. Frailty screening tool for older patients may enhance traditional risk calculators and improve patient selection for noncardiac surgery under general anesthesia.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".