Predictors of Vancomycin-Resistant Enterococcus (VRE) Bacteremia in Ontario, Canada
Bibliographic record
Abstract
To determine predictors of vancomycin resistant enterococcus (VRE) bacteremia in Ontario, Canada. Ontario hospitals are mandated to report VRE bacteremias to a public reporting database. All confirmed VRE bacteremias between January 2009 - December 2013 were linked to provincial health care administrative data sources. A population-based, nested case–control study was performed to determine predictors of VRE bacteremia. Cases were patients with VRE bacteremia and controls were patients with at least one hospital admission during the study period. Each case was matched with up to three controls using frequency matching on age, sex and aggregated diagnosis group. Associations between patient- and hospital-level predictors and VRE bacteremia were estimated by Generalized Estimating Equations and summarized using odds ratios (OR) (adjusted for age, sex, Charlson score, intensive care unit (ICU) admission, length of stay, hospital admission, comorbidities, hospital size and hospital type) and corresponding 95% confidence intervals (CI) in SAS. In total, 232 patients had a VRE bacteremia during the study period; 217 cases were successfully linked to administrative data sources and there were 651 controls. Mean age of cases was 61 years (SD 17) vs. 60 years for controls (SD 21). The proportion of male cases and controls was 60%. Length of stay for cases was longer than controls (median 39 days [range 1–539 days] vs. 3 days [range 1 – 136 days], P < 0.001) and 82% of cases died within 30 days vs. 21% of controls (P < 0.001). In adjusted analyses, patient-level predictors of VRE bacteremia included: organ transplant (OR 18.93 [95% CI 8.37 – 42.79), cancer (OR 9.56 [95% CI 4.61 – 19.79]), ICU admission (OR 7.45 [95% CI 3.57 – 15.54]), heart disease (OR 5.03 [95% CI 1.92 – 13.18]) and length of stay (OR 1.08 per day [95% CI 1.03 – 1.12]); COPD (OR 3.10 [95% CI 0.86 – 11.20]) and diabetes (OR 2.35 [95% CI 0.72 – 7.64]) were not significant predictors. Hospital-level predictors included hospital size ≥800 beds (OR 10.64 [95% CI 2.34 – 48.25]) and teaching hospitals (OR 4.20 [95% CI 1.65 – 10.74]). Immunocompromised and patients admitted to ICU are at highest risk of VRE bacteremia, particularly at large hospitals and teaching hospitals. These results may help inform clinical decisions and infection prevention programs. 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".