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Hospital-onset bacteremia and fungemia: An evaluation of predictors and feasibility of benchmarking comparing two risk-adjusted models among 267 hospitals.
Yu, Kalvin C; Ye, Gang; Edwards, Jonathan R; Gupta, Vikas; Benin, Andrea L; Ai, ChinEn; Dantes, Raymund.
  • 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.
  • Gupta V; Becton, Dickinson and Company, Franklin Lakes, New Jersey.
  • Benin AL; Centers for Disease Control and Prevention, Atlanta, Georgia.
  • Ai C; Becton, Dickinson and Company, Franklin Lakes, New Jersey.
  • Dantes R; Centers for Disease Control and Prevention, Atlanta, Georgia.
Infect Control Hosp Epidemiol ; 43(10): 1317-1325, 2022 10.
Article in English | MEDLINE | ID: covidwho-2185255
ABSTRACT

OBJECTIVES:

To evaluate the prevalence of hospital-onset bacteremia and fungemia (HOB), identify hospital-level predictors, and to evaluate the feasibility of an HOB metric.

METHODS:

We analyzed 9,202,650 admissions from 267 hospitals during 2015-2020. An HOB event was defined as the first positive blood-culture pathogen on day 3 of admission or later. We used the generalized linear model method via negative binomial regression to identify variables and risk markers for HOB. 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 plus additional measures of blood-culture testing practices. Performance of each model was compared against the unadjusted rate of HOB.

RESULTS:

Overall median rate of HOB per 100 admissions was 0.124 (interquartile range, 0.00-0.22). Facility-level predictors included bed size, sex, ICU admissions, community-onset (CO) blood culture testing intensity, and hospital-onset (HO) testing intensity, and prevalence (all P < .001). In the complex model, CO bacteremia prevalence, HO testing intensity, and HO testing prevalence were the predictors most associated with HOB. The complex model demonstrated better model performance; 55% of hospitals that ranked in the highest quartile based on their raw rate shifted to a lower quartile when the SIR from the complex model was applied.

CONCLUSIONS:

Hospital descriptors, aggregate patient characteristics, community bacteremia and/or fungemia burden, and clinical blood-culture testing practices influence rates of HOB. Benchmarking an HOB metric is feasible and should endeavor to include both facility and clinical variables.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Fungemia / Bacteremia Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Infect Control Hosp Epidemiol Journal subject: Communicable Diseases / Nursing / Epidemiology / Hospitals Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Fungemia / Bacteremia Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Limits: Humans Language: English Journal: Infect Control Hosp Epidemiol Journal subject: Communicable Diseases / Nursing / Epidemiology / Hospitals Year: 2022 Document Type: Article