Immunosuppression and Clostridioides (Clostridium) difficile Infection Risk in Metabolic and Bariatric Surgery Patients
Bibliographic record
Abstract
BACKGROUND: Immunosuppressant use increases risk of Clostridioides (Clostridium) difficile infection. To date, no studies have analyzed the relationship between immunosuppressant use and C difficile infections after metabolic and bariatric surgery (MBS). METHODS: A retrospective analysis of the 2015-2018 MBSAQIP data was conducted. The MBSAQIP data include information from 854 affiliated practices in the US and Canada. Initial sample size was 760,076 MBS patients. After excluding participants due to missing variables (n = 188,106) and the use of surgical procedures other than Roux-en-Y gastric bypass and sleeve gastroplasty (n = 129,712), final analyses were performed on 442,258 participants. Logistic regression models generated the odds of C difficile infection developing post MBS, according to immunosuppressant status (positive or negative). RESULTS: Unadjusted logistic regression analysis showed that patients using immunosuppressants were 95% more likely to have postoperative C difficile infection (odds ratio 1.945; 95% CI, 1.230 to 3.075; p < 0.001) vs MBS patients not taking immunosuppressants. After adjusting for age, sex, ethnicity, preoperative BMI, diabetes status, and surgical procedure type, the association remained unaffected (adjusted odds ratio 1.956; 95% CI, 1.236 to 3.095; p < 0.01). Patients who completed the laparoscopic Roux-en-Y gastric bypass procedure had more than double the odds of C difficile infection developing compared with those who completed the laparoscopic sleeve gastrectomy procedure (odds ratio 2.183; 95% CI, 1.842 to 2.587; p < 0.0001). CONCLUSIONS: Our results using a population-based sample of MBS patients showed that those taking immunosuppressants have a significantly higher risk of developing Clostridioides (Clostridium) difficile infection postoperatively. These findings suggest that patients using immunosuppressants should be closely monitored both pre and post procedure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".