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Record W3097507138 · doi:10.1017/ice.2020.1138

Central-line–Associated Bloodstream Infections Among Adult Intensive Care Unit Patients in Canadian Hospitals, 2011–2018

2020· article· en· W3097507138 on OpenAlexaffabout
Wallis Rudnick, Lynn Johnston, Jocelyn A. Srigley, Jun Chen Collet, Jeannette Comeau, Chelsey Ellis, Charles Frenette, Bonita E. Lee, Joanne M. Langley, Marie-Astrid Lefebvre, Allison McGeer, Jennifer Parsonage, Donna Penney, Michelle Science, Anada Silva, Stephanie Smith, Kathryn N. Suh, Linda Pelude, CNISP PHAC

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2020
Typearticle
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsOttawa HospitalHospital for Sick ChildrenAlberta Health ServicesIzaak Walton Killam Health CentreStollery Children's HospitalHorizon Health NetworkPublic Health Agency of CanadaMcGill University Health CentreB.C. Women's Hospital & Health CentreUniversity of AlbertaProvincial Health Services AuthorityNova Scotia Health AuthorityUniversity of TorontoMoncton HospitalDalhousie University
Fundersnot available
KeywordsMedicineIntensive careEmergency medicineIntensive care unitBloodstream infectionCentral lineEpidemiologyPsychological interventionIntensive care medicineInternal medicine

Abstract

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Background: Nosocomial central-line–associated bloodstream infections (CLABSIs) are an important cause of morbidity and mortality in hospitalized patients. CLABSI surveillance establishes rates for internal and external comparison, identifies risk factors, and allows assessment of interventions. Objectives: To determine the frequency of CLABSIs among adult patients admitted to intensive care units (ICUs) in CNISP hospitals and evaluate trends over time. Methods: CNISP is a collaborative effort of the Canadian Hospital Epidemiology Committee, the Association of Medical Microbiologists and Infectious Disease Canada and the Public Health Agency of Canada. Since 1995, CNISP has conducted hospital-based sentinel surveillance of healthcare-associated infections. Overall, 55 CNISP hospitals participated in ≥1 year of CLABSI surveillance. Adult ICUs are categorized as mixed ICUs or cardiovascular (CV) surgery ICUs. Data were collected using standardized definitions and collection forms. Line-day denominators for each participating ICU were collected. Negative-binomial regression was used to test for linear trends, with robust standard errors to account for clustering by hospital. We used the Fisher exact test to compare binary variables. Results: Each year, 28–42 adult ICUs participated in surveillance (27–37 mixed, 6–8 CV surgery). In both mixed ICUs and CV-ICUs, rates remained relatively stable between 2011 and 2018 (Fig. 1). In mixed ICUs, CLABSI rates were 1.0 per 1,000 line days in 2011, and 1.0 per 1,000 line days in 2018 (test for linear trend, P = .66). In CV-ICUs, CLABSI rates were 1.1 per 1,000 line days in 2011 and 0.8 per 1,000 line days in 2018 (P = .19). Case age and gender distributions were consistent across the surveillance period. The 30-day all-cause mortality rate was 29% in 2011 and in 2018 (annual range, 29%–35%). Between 2011 and 2018, the percentage of isolated microorganisms that were coagulase-negative staphylococci (CONS) decreased from 31% to 18% (P = .004). The percentage of other gram-positive organisms increased from 32% to 37% (P = .34); Bacillus increased from 0% to 4% of isolates and methicillin-susceptible Staphylococcus aureus from 2% to 6%). The gram-negative organisms increased from 21% to 27% (P = .19). Yeast represented 16% in 2011 and 18% in 2018; however, the percentage of yeast that were Candida albicans decreased over time (58% of yeast in 2011 and 30% in 2018; P = .04). Between 2011 and 2018, the most commonly identified species of microorganism in each year were CONS (18% in 2018) and Enterococcus spp (18% in 2018). Conclusions: Ongoing CLABSI surveillance has shown stable rates of CLABSI in adult ICUs from 2011 to 2018. The causative microorganisms have changed, with CONS decreasing from 31% to 18%. Funding: CNISP is funded by the Public Health Agency of Canada. Disclosures: Allison McGeer reports funds to her for studies, for which she is the principal investigator, from Pfizer and Merck, as well as consulting fees from Sanofi-Pasteur, Sunovion, GSK, Pfizer, and Cidara.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.390

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.026
GPT teacher head0.316
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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