The effect of obesity on the outcome of thoracic endovascular aortic repair: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Obesity is a well-known predictor for poor postoperative outcomes of vascular surgery. However, the association between obesity and outcomes of thoracic endovascular aortic repair (TEVAR) is still unclear. This systematic review and meta-analysis was performed to assess the roles of obesity in the outcomes of TEVAR. Methods We systematically searched the Web of Science and PubMed databases to obtain articles regarding obesity and TEVAR that were published before July 2023. The odds ratio (OR) or hazard ratio (HR) was used to assess the effect of obesity on TEVAR outcomes. Body mass index (BMI) was also compared between patients experiencing adverse events after TEVAR and those not experiencing adverse events. The Newcastle–Ottawa Scale was used to evaluate the quality of the enrolled studies. Results A total of 7,849 patients from 10 studies were included. All enrolled studies were high-quality. Overall, the risk of overall mortality (OR = 1.49, 95% CI [1.02–2.17], p = 0.04) was increased in obese patients receiving TEVAR. However, the associations between obesity and overall complications (OR = 2.41, 95% CI [0.84–6.93], p = 0.10) and specific complications were all insignificant, including stroke (OR = 1.39, 95% CI [0.56–3.45], p = 0.48), spinal ischemia (OR = 0.97, 95% CI [0.64–1.47], p = 0.89), neurological complications (OR = 0.13, 95% CI [0.01–2.37], p = 0.17), endoleaks (OR = 1.02, 95% CI [0.46–2.29], p = 0.96), wound complications (OR = 0.91, 95% CI [0.28–2.96], p = 0.88), and renal failure (OR = 2.98, 95% CI [0.92–9.69], p = 0.07). In addition, the patients who suffered from postoperative overall complications (p < 0.001) and acute kidney injury (p = 0.006) were found to have a higher BMI. In conclusion, obesity is closely associated with higher risk of mortality after TEVAR. However, TEVAR may still be suitable for obese patients. Physicians should pay more attention to the perioperative management of obese patients.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".