Effect of Detecting and Isolating <i>Clostridium difficile</i> Carriers at Hospital Admission on the Incidence of <i>C difficile</i> Infections
Bibliographic record
Abstract
IMPORTANCE: Clostridium difficile infection (CDI) is a major cause of health care-associated infection worldwide, and new preventive strategies are urgently needed. Current control measures do not target asymptomatic carriers, despite evidence that they can contaminate the hospital environment and health care workers' hands and potentially transmit C difficile to other patients. OBJECTIVE: To investigate the effect of detecting and isolating C difficile asymptomatic carriers at hospital admission on the incidence of health care-associated CDI (HA-CDI). DESIGN, SETTING, AND PARTICIPANTS: We performed a controlled quasi-experimental study between November 19, 2013, and March 7, 2015, in a Canadian acute care facility. Admission screening was conducted by detecting the tcdB gene by polymerase chain reaction on a rectal swab. Carriers were placed under contact isolation precautions during their hospitalization. MAIN OUTCOMES AND MEASURES: Changes in HA-CDI incidence level and trend during the intervention period (17 periods of 4 weeks each) were compared with the preintervention control period (120 periods of 4 weeks each) by segmented regression analysis and autoregressive integrated moving average (ARIMA) modeling. Concomitant changes in the aggregated HA-CDI incidence at other institutions in Québec City, Québec (n = 6) and the province of Québec (n = 94) were also examined. RESULTS: Overall, 7599 of 8218 (92.5%) eligible patients were screened, among whom 368 (4.8%) were identified as C difficile carriers. During the intervention, 38 patients (3.0 per 10 000 patient-days) developed an HA-CDI compared with 416 patients (6.9 per 10 000 patient-days) during the preintervention control period (P < .001). There was no immediate change in the level of HA-CDIs on implementation (P = .92), but there was a significant decrease in trend over time of 7% per 4-week period (rate ratio, 0.93; 95% CI, 0.87-0.99 per period; P = .02). ARIMA modeling also detected a significant effect of the intervention, represented by a gradual progressive decrease in the HA-CDI time series by an overall magnitude of 7.2 HA-CDIs per 10 000 patient-days. We estimated that the intervention had prevented 63 of the 101 (62.4%) expected cases. By contrast, no significant decrease in HA-CDI rates occurred in the control groups. CONCLUSIONS AND RELEVANCE: Detecting and isolating C difficile carriers was associated with a significant decrease in the incidence of HA-CDI. If confirmed in subsequent studies, this strategy could help prevent HA-CDI.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".