Severe Obesity and Prolonged Postoperative Mechanical Ventilation in Elderly Vascular Surgery Patients
Bibliographic record
Abstract
Background: is increasingly prevalent in elderly surgical patients. Although older age is associated with prolonged postoperative mechanical ventilation (PPMV), the contribution of obesity to this complication in the elderly has not been explored. We investigated the association of severe obesity with the PPMV and the role of severe obesity on mortality risk in patients requiring PPMV. Methods: (National Surgical Quality Improvement Program (NSQIP) 2015 - 2018). PPMV was defined as requirement of postoperative mechanical ventilation for longer than 48 h following surgery. We examined the association between severe obesity and PPMV, using univariable and multivariable logistic regression. Results: We studied 34,936 patients who were ≥ 65 years of age. The incidence of PPMV was 2.0% (624/31,700) in normal weight patients and 2.8% (92/3,236) in severely obese patients (odds ratio (OR): 1.46; 95% confidence interval (CI): 1.17 - 1.82, P = 0.001). Multivariable analysis, controlling for confounders, estimated a 56% relative increase in the risk of PPMV in severely obese patients, relative to their normal weight peers (OR: 1.56; 95% CI: 1.22 - 1.99, P = 0.001). In normal weight patients, the risk of mortality was multiplied by 23 times in patients who required PPMV (39.6% vs. 2.64%; OR: 23.10; 95% CI: 18.96 - 28.16; P < 0.001). In severely obese patients, PPMV multiplied the risk of mortality by 25 times (30.4% vs. 1.6%; OR: 25.26, 95% CI: 13.44 - 47.50; P < 0.001). Conclusions: Severe obesity increased the odds of PPMV. Although the incidence of PPMV was low, its requirement conferred up to 25 times greater risk of postoperative mortality, underscoring the need for perioperative mitigation strategies to minimize PPMV risk in elderly patients undergoing vascular surgery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".