A National Survey of the Prevalence and Impact of Clostridium difficile Infection Among Hospitalized Inflammatory Bowel Disease Patients
Bibliographic record
Abstract
BACKGROUND: We sought to determine nationwide, population-based trends in rates of Clostridium difficile (C. difficile) infection among hospitalized inflammatory bowel disease (IBD) patients in the United States, and to determine its mortality and economic impact. METHODS: We analyzed discharge records from the Nationwide Inpatient Sample, and used the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes to identify Crohn's disease (CD) and ulcerative colitis (UC) cases, and cases of C. difficile infection between 1998 and 2004. Temporal patterns of C. difficile incidence in IBD patients were compared to non-IBD gastroenterology patients and all-hospitalized patients. The impact of C. difficile on in-hospital mortality and resource utilization was quantified using multiple regression analysis. RESULTS: The prevalence of C. difficile among UC patients (37.3 per 1,000, 95% confidence interval [CI] 34.0-40.7 per 1,000) was higher than that among CD patients (10.9 per 1,000, 95% CI 9.9-12.0 per 1,000), non-IBD gastrointestinal (GI) patients (4.8 per 1,000, 95% CI 4.6-5.0 per 1,000), and general medical patients (4.5 per 1,000, 95% CI 4.2-4.7 per 1,000). C. difficile incidence nearly doubled among UC patients (26.6 per 1,000 to 51.2 per 1,000) over 7 yr. After adjustment for confounders, C. difficile infection was associated with greater mortality among patients with UC (odds ratio [OR] 3.79, 95% CI 2.84-5.06), but not CD (OR 1.66, 95% CI 0.75-3.66). C. difficile was also associated with 65% and 46% longer lengths of stay, which correlated with 63% and 46% higher average hospital charges, for CD and UC patients, respectively. CONCLUSIONS: C. difficile infection is a growing public health issue among hospitalized IBD patients, especially those with UC, and is associated with higher mortality and resource utilization, prompting the need for better preventative measures and early detection.
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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 | 0.003 |
| 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 | 0.001 |
| 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".