60 Examining the impact of obesity ≥30 kg/m2 on acute kidney injury following cardiac surgery
Bibliographic record
Abstract
Background Postoperative acute kidney injury (AKI) is a frequent and serious consequence of cardiac surgery. A complex and multifactorial issue, improved understanding of its aetiological components will aid development of preventative strategies prospectively. The global prevalence of obesity has reached epidemic proportions and is particularly associated with the pathogenesis of cardiovascular disease. We undertook to investigate the association of obesity and the risk of AKI development following cardiac surgery. Methods 432 patients who underwent cardiac surgery with cardiopulmonary bypass between October 2009 and August 2010 were included in the final retrospective analysis. Obesity was defined as BMI ≥ 30 kg/m2. Acute kidney injury (AKI) was defined as a creatinine increase of ≥ 25% from baseline at 48 h post surgery. Results The overall incidence of AKI was 29.9% (n = 129). The incidence of diabetes was significantly higher in the obese cohort (24.3 vs. 10.8% p = 0.0005). There was an increased incidence of post-operative renal impairment in the obese vs. non-obese cohort, however this was not statistically significant (39 vs. 25.9%, p = 0.07). Univariate analysis of independent predictors of postoperative AKI revealed significant associations between obesity (BMI >30 kg/m2) (OR 1.80, 95% CI 1.17–2.79, p = 0.01), cerebrovascular disease (OR 1.83, 95% CI 1.10–2.03, p = 0.02), hyperlipidaemia (OR 0.59, 95% CI 0.39–0.89, p = 0.02) and a history of smoking (OR 2.82, 95% CI 1.79–4.44, p < 0.0001). Multivariate logistic regression revealed that BMI >30 kg/m2 was independently associated with the development of postoperative AKI (OR 1.81, 95% CI 1.11–2.95, p = 0.02) as were age (OR 0.98 95% CI 0.96–1.0, p = 0.03) and cardiopulmonary bypass time (OR 0.99, 95% CI 0.98–1.0, p = 0.03). Conclusion Obesity ≥ 30 kg/m2 is independently associated with an increased risk of AKI following cardiac surgery. Further understanding of the molecular basis of this association is critical to the design of preventative strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".