Predictors of Clostridioides difficile Infection Among Asymptomatic, Colonized Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Asymptomatic patients colonized with Clostridioides difficile are at risk of developing C. difficile infection (CDI), but the factors associated with disease onset are poorly understood. Our aims were to identify predictors of hospital-onset CDI (HO-CDI) among colonized patients and to explore the potential benefits of primary prophylaxis to prevent CDI. METHODS: We conducted a retrospective cohort study in a tertiary academic institution. Colonized patients were identified by detecting the tcdB gene by polymerase chain reaction on a rectal swab. Univariate and multivariate logistic regression analyses were used to identify predictors of HO-CDI. RESULTS: There were 19 112 patients screened, from which 960 (5%) colonized patients were identified: 513 met the inclusion criteria. Overall, 39 (7.6%) developed a HO-CDI, with a 30-day attributable mortality of 15%. An increasing length of stay (adjusted odds ratio [aOR] per day, 1.03; P = .006), exposure to multiple classes of antibiotics (aOR per class, 1.45; P = .02), use of opioids (aOR, 2.78; P = .007), and cirrhosis (aOR 5.49; P = .008) were independently associated with increased risks of HO-CDI, whereas the use of laxatives was associated with a lower risk of CDI (aOR 0.36; P = .01). Among the antimicrobials, B-lactam with B-lactamase inhibitors (OR 3.65; P < .001), first-generation cephalosporins (OR 2.38; P = .03), and carbapenems (OR 2.44; P = .03) correlated with the greatest risk of HO-CDI. By contrast, patient age, the use of proton pump inhibitors, and the use of primary prophylaxis were not significant predictors of HO-CDI. CONCLUSIONS: This study identifies several factors that are associated with CDI among colonized patients. Whether modifying these variables could decrease the risk of CDI should be investigated.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".