973. Inter-facility Patient Sharing and Clostridium difficile Incidence in the Ontario Hospital Network: A 13-Year Longitudinal Cohort Study of 116 Hospitals
Bibliographic record
Abstract
Abstract Background Inter-facility patient movement plays an important role in the dissemination of antimicrobial resistance and C. difficile infection (CDI) throughout healthcare systems. However, the relative performance of different patient sharing metrics for predicting CDI incidence is not known. We compared 3 different measures of inter-facility patient sharing as they relate to CDI incidence in Ontario facilities. Methods A retrospective cohort analysis was used to predict incident CDI (ICD-10 = A04.7 identified from Discharge Abstract Database records) across Ontario hospitals (Nhospitals = 116) between April 1, 2003 to March 31, 2016. Patients with a stay of <3 days and those with a history of CDI in the prior 90 days were excluded from the risk set but not from patient sharing metrics. Poisson regression models with facility-level random effects were used to predict facility CDI incidence (per 1,000 admissions) and measure the percent change in facility-level variance (PCV). The 3 metrics of inter-facility patient sharing included: (1) “importation”—the rate of patients with a discharge from another distinct facility in prior 90 days, (2) “incidence-weighted importation”—equal to importation weighted by the incidence of CDI in the previous facility, and (3) “case importation”—importation of patients with a history of CDI. Results Over the 13-year period, we observed 58,427 cases of healthcare-associated CDI among 12,750,000 admissions. Facility CDI incidence ranged from 2.9 to 19.6 per 1,000 admissions (6.8-fold range). Patient sharing metrics were strongly related to facility CDI incidence (figure). In models adjusting for facility risk factors, all 3 measures still explained an important portion of inter-facility variation in CDI incidence: importation (PCV = 5%, P = 0.01), incidence-weighted importation (PCV = 15%, P < 0.001), and “case importation” (PCV = 48%, P < 0.001). Conclusion We observed a substantial variation in facility CDI incidence that was explained by linkages between acute care facilities, especially linkage to other facilities with a high incidence of CDI. Facility infection prevention staff should consider incorporating the facility CDI incidence into risk stratification assessments of patient transfers. Disclosures All authors: No reported disclosures.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".