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Treated, hospital-onset Clostridiodes difficile infection: An evaluation of predictors and feasibility of benchmarking comparing 2 risk-adjusted models among 265 hospitals.
Yu, Kalvin C; Ye, Gang; Edwards, Jonathan R; Dantes, Raymund; Gupta, Vikas; Ai, ChinEn; Betz, Kristina; Benin, Andrea L.
Afiliación
  • Yu KC; Becton, Dickinson and Company, Franklin Lakes, New Jersey.
  • Ye G; Becton, Dickinson and Company, Franklin Lakes, New Jersey.
  • Edwards JR; Centers for Disease Control and Prevention, Atlanta, Georgia.
  • Dantes R; Centers for Disease Control and Prevention, Atlanta, Georgia.
  • Gupta V; Emory University School of Medicine, Atlanta, Georgia.
  • Ai C; Becton, Dickinson and Company, Franklin Lakes, New Jersey.
  • Betz K; Becton, Dickinson and Company, Franklin Lakes, New Jersey.
  • Benin AL; Centers for Disease Control and Prevention, Atlanta, Georgia.
Infect Control Hosp Epidemiol ; 45(1): 48-56, 2024 Jan.
Article en En | MEDLINE | ID: mdl-37449415
ABSTRACT

OBJECTIVES:

To evaluate the incidence of a candidate definition of healthcare facility-onset, treated Clostridioides difficile (CD) infection (cHT-CDI) and to identify variables and best model fit of a risk-adjusted cHT-CDI metric using extractable electronic heath data.

METHODS:

We analyzed 9,134,276 admissions from 265 hospitals during 2015-2020. The cHT-CDI events were defined based on the first positive laboratory final identification of CD after day 3 of hospitalization, accompanied by use of a CD drug. The generalized linear model method via negative binomial regression was used to identify predictors. Standardized infection ratios (SIRs) were calculated based on 2 risk-adjusted models a simple model using descriptive variables and a complex model using descriptive variables and CD testing practices. The performance of each model was compared against cHT-CDI unadjusted rates.

RESULTS:

The median rate of cHT-CDI events per 100 admissions was 0.134 (interquartile range, 0.023-0.243). Hospital variables associated with cHT-CDI included the following higher community-onset CDI (CO-CDI) prevalence; highest-quartile length of stay; bed size; percentage of male patients; teaching hospitals; increased CD testing intensity; and CD testing prevalence. The complex model demonstrated better model performance and identified the most influential predictors hospital-onset testing intensity and prevalence, CO-CDI rate, and community-onset testing intensity (negative correlation). Moreover, 78% of the hospitals ranked in the highest quartile based on raw rate shifted to lower percentiles when we applied the SIR from the complex model.

CONCLUSIONS:

Hospital descriptors, aggregate patient characteristics, CO-CDI burden, and clinical testing practices significantly influence incidence of cHT-CDI. Benchmarking a cHT-CDI metric is feasible and should include facility and clinical variables.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Infección Hospitalaria / Clostridioides difficile / Infecciones por Clostridium Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male Idioma: En Revista: Infect Control Hosp Epidemiol Asunto de la revista: DOENCAS TRANSMISSIVEIS / ENFERMAGEM / EPIDEMIOLOGIA / HOSPITAIS Año: 2024 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Infección Hospitalaria / Clostridioides difficile / Infecciones por Clostridium Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Humans / Male Idioma: En Revista: Infect Control Hosp Epidemiol Asunto de la revista: DOENCAS TRANSMISSIVEIS / ENFERMAGEM / EPIDEMIOLOGIA / HOSPITAIS Año: 2024 Tipo del documento: Article