Yu, Kalvin C.;
Ye, Gang;
Edwards, Jonathan R.;
Dantes, Raymund;
Gupta, Vikas;
Ai, ChinEn;
Betz, Kristina;
Benin, Andrea L.
Treated, hospital-onset Clostridiodes difficile infection: An evaluation of predictors and feasibility of benchmarking comparing 2 risk-adjusted models among 265 hospitals
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Medientyp:
E-Artikel
Titel:
Treated, hospital-onset Clostridiodes difficile infection: An evaluation of predictors and feasibility of benchmarking comparing 2 risk-adjusted models among 265 hospitals
Beteiligte:
Yu, Kalvin C.;
Ye, Gang;
Edwards, Jonathan R.;
Dantes, Raymund;
Gupta, Vikas;
Ai, ChinEn;
Betz, Kristina;
Benin, Andrea L.
Erschienen:
Cambridge University Press (CUP), 2024
Erschienen in:Infection Control & Hospital Epidemiology
Beschreibung:
<jats:title>Abstract</jats:title><jats:sec id="S0899823X23001241_as1"><jats:title>Objectives:</jats:title><jats:p>To evaluate the incidence of a candidate definition of healthcare facility-onset, treated <jats:italic>Clostridioides difficile</jats:italic> (CD) infection (cHT-CDI) and to identify variables and best model fit of a risk-adjusted cHT-CDI metric using extractable electronic heath data.</jats:p></jats:sec><jats:sec id="S0899823X23001241_as2"><jats:title>Methods:</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec id="S0899823X23001241_as3"><jats:title>Results:</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec id="S0899823X23001241_as4"><jats:title>Conclusions:</jats:title><jats:p>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.</jats:p></jats:sec>