Heart valve surgery and the obesity paradox: A systematic review
Bibliographic record
Abstract
Obesity has been associated with increased incidence of comorbidities and shorter life expectancy, and it has generally been assumed that patients with obesity should have inferior outcomes after surgery. Previous literature has often demonstrated equivalent or even improved rates of mortality after cardiac surgery when compared to their lower-weight counterparts, coined the obesity paradox. Herein, we aim to review the literature investigating the impact of obesity on surgical valve interventions. PubMed and Embase were systematically searched for articles published from 1 January 2000 to 15 October 2021. A total of 1315 articles comparing differences in outcomes between patients of varying body mass index (BMI) undergoing valve interventions were reviewed and 25 were included in this study. Patients with higher BMI demonstrated equivalent or reduced rates of postoperative myocardial infarction, stroke, reoperation rates, acute kidney injury, dialysis and bleeding. Two studies identified increased rates of deep sternal wound infection in patients with higher BMI, although the majority of studies found no significant difference in deep sternal wound infection rates. The obesity paradox has described counterintuitive outcomes predominantly in coronary artery bypass grafting and transcatheter aortic valve replacement. Recent literature has identified similar trends in other heart valve interventions. While the obesity paradox has been well characterized, its causes are yet to be identified. Further study is essential in order to identify the causes of the obesity paradox so patients of all body sizes can receive optimal care.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".