Device-associated infections in Canadian acute-care hospitals from 2009 to 2018
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAIs) pose a serious risk to patient safety and quality of care.The Canadian Nosocomial Infection Surveillance Program (CNISP) conducts national surveillance of HAIs at sentinel acute-care hospitals across Canada.This report provides an overview of 10 years of Canadian data on the epidemiology of select deviceassociated HAIs.Methods: Over 40 hospitals submitted data between 2009 and 2018 for hip and knee surgical site infections (SSIs), cerebrospinal fluid shunt SSIs, paediatric cardiac SSIs and/or central lineassociated bloodstream infections (CLABSIs).Counts, rates, patient and hospital characteristics, as well as pathogen distributions and antimicrobial susceptibilities are presented.Results: A total of 4,300 device-associated infections were reported.Central line-associated bloodstream infections were the most common device-associated HAI reported (n=2,973, 69%) and hip and knee arthroplasty infections were the most common SSIs reported (66% of SSIs).Our findings show decreasing CLABSI rates in neonatal intensive care units (4.2 to 1.9 per 1,000 line-days, p<0.0001) and decreasing knee SSI rates (0.69 to 0.30 infections per 100 surgeries, p=0.007).Rates of device-associated HAIs have remained relatively consistent over the 10-year surveillance period.Overall, 4,599 pathogens were identified from device-associated HAI; 70% of these were related to CLABSIs.Coagulase-negative staphylococci (29%) and Staphylococcus aureus (14%) were the most frequently reported pathogens.Gram-positive pathogens represented 68% of identified pathogens, gram-negative pathogens represented 22% and fungi represented 9%.Conclusion: Understanding the national burden of device-associated HAIs is essential for developing and maintaining benchmark rates for informing infection and prevention control and antimicrobial stewardship policies and programs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".