Factors that predict 30-day readmission after bariatric surgery: experience of a publicly funded Canadian centre
Bibliographic record
Abstract
Background: Hospital readmissions after bariatric surgery can significantly increase health care costs. Rates of readmission after bariatric surgery have ranged from 0.6% to 11.3%, but the rate of complications and the factors that predict readmission have not been well characterized in Canada. The objective of this study was to characterize readmission rates and the factors that predict 30-day readmission in a Canadian centre. Methods: A retrospective study was performed on all patients who underwent bariatric surgery between 2010 and 2015 in a single Canadian centre. Procedures included laparoscopic Roux-en-Y gastric bypass (LRYGB), laparoscopic sleeve gastrectomy (LSG) and laparoscopic adjustable gastric banding (LAGB). Prospectively collected data were extracted from an administrative database. Multivariable logistic regression analysis was performed to determine which factors predict 30-day readmission. Results: A total of 1468 patients had bariatric surgery (51.0% LRYGB, 40.5% LSG, 8.6% LAGB) during the 6-year study period, with an overall 30-day readmission rate of 7.5%. LRYGB was associated with a higher readmission rate (11.4%) than LSG (3.7%) or LAGB (1.6%). Common reasons for readmission were infection (24.8%), pain (17.4%) and nausea or vomiting (10.1%). Multivariable analysis identified 3 factors that independently predicted readmission: length of stay greater than 4 days (odds ratio [OR] 2.18, 95% confidence interval [CI] 1.03-4.63, p = 0.042), LRYGB (OR 5.21, 95% CI 1.19-22.73, p = 0.028) and acute renal failure (OR 14.10, 95% CI 1.07-186.29, p = 0.045). Conclusion: Readmissions after bariatric surgery were most commonly caused by potentially preventable factors, such as pain, nausea or vomiting. Strategies to identify and address factors associated with readmission may reduce readmissions and health care costs after bariatric surgery in a publicly funded health care system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".