Hospital-onset bacteremia and fungemia: An evaluation of predictors and feasibility of benchmarking comparing two risk-adjusted models among 267 hospitals

• Authors: Kalvin C Yu, Becton, Dickinson and Company, Franklin Lakes, New Jersey; Gang Ye, Becton, Dickinson and Company, Franklin Lakes, New Jersey; Jonathan R Edwards, Centers for Disease Control and Prevention, Atlanta; Vikas Gupta, Emory University; Andrea Benin, Emory University; ChinEn Ai, Becton, Dickinson and Company, Franklin Lakes, New Jersey; Raymund Dantes, Emory University
• Journal Title: INFECTION CONTROL AND HOSPITAL EPIDEMIOLOGY
• Volume: Volume 43, Number 10
• Publisher: CAMBRIDGE UNIV PRESS | 2022-09-09, Pages 1317-1325
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1017/ice.2022.211
• Keywords: Science & Technology; Life Sciences & Biomedicine; Public, Environmental & Occupational Health; Infectious Diseases; BLOOD-STREAM INFECTIONS

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.