Trends of Drain Placement During Revisional Bariatric Surgeries and Its Association with 30-Day Morbidity: An MBSAQIP Analysis of 64,495 Patients
Bibliographic record
Abstract
Background: Drains are often placed during bariatric procedures; however, their use in conversional or revisional bariatric surgery (CRBS) has not been thoroughly explored. Our study sought to identify the frequency of drain placement in CRBS, and characterize factors associated with drain placement and their influence on 30-day serious complications. Methods: Patients undergoing CRBS between 2020 and 2022 were included from the MBSAQIP database. Patients were placed into drain placed (DP) versus no drain (ND) cohorts and baseline characteristics and complication rate were compared. Multivariable logistic regression models were used to identify independent predictors of drain placement and complications. Results: of 64,495 included patients, drains were placed in 19.1% in 2020; this was down to 14.4% in 2022. Drain placement was associated with increased risk of multiple complications such as hemorrhage, readmission, surgical site infection, and gastrointestinal bleeding. On multivariate analysis, drain placement was an independent predictor of serious complications (aOR 1.45, p < 0.001), anastomotic leak (aOR 2.25, p < 0.001), organ space infection (aOR 2.12, p < 0.001), and reoperation (aOR 1.37, p < 0.001), as well as excess LOS (aOR 2.06, p < 0.001). Predictors of drain placement include older age, higher BMI, smoking status, history of venous thromboembolism, and procedural factors, such as undergoing non-sleeve revisional surgery or having an intraoperative leak test. Conclusions: Drain placement during CRBS surgical procedures is common and more likely in higher risk patients and anastomotic revisional procedures. Though the reasons for drain placement were not available, these data suggest that surgeons should be judicious in selecting patients for drain placement due to its association with increased LOS and postoperative morbidity in CRBS.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".