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.

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.

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.

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.

To compare characteristics and outcomes associated with central-line–associated bloodstream infections (CLABSIs) and electronic health record–determined hospital-onset bacteremia and fungemia (HOB) cases in hospitalized US adults.

We conducted a retrospective observational study of patients in 41 acute-care hospitals. CLABSI cases were defined as those reported to the National Healthcare Safety Network (NHSN). HOB was defined as a positive blood culture with an eligible bloodstream organism collected during the hospital-onset period (ie, on or after day 4). We evaluated patient characteristics, other positive cultures (urine, respiratory, or skin and soft-tissue), and microorganisms in a cross-sectional analysis cohort. We explored adjusted patient outcomes [length of stay (LOS), hospital cost, and mortality] in a 1:5 case-matched cohort.

The cross-sectional analysis included 403 patients with NHSN-reportable CLABSIs and 1,574 with non-CLABSI HOB. A positive non-bloodstream culture with the same microorganism as in the bloodstream was reported in 9.2% of CLABSI patients and 32.0% of non-CLABSI HOB patients, most commonly urine or respiratory cultures. Coagulase-negative staphylococci and Enterobacteriaceae were the most common microorganisms in CLABSI and non-CLABSI HOB cases, respectively. In case-matched analyses, CLABSIs and non-CLABSI HOB, separately or combined, were associated with significantly longer LOS [difference, 12.1–17.4 days depending on intensive care unit (ICU) status], higher costs (by $25,207–$55,001 per admission), and a >3.5-fold increased risk of mortality in patients with an ICU encounter.

CLABSI and non-CLABSI HOB cases are associated with significant increases in morbidity, mortality, and cost. Our data may help inform prevention and management of bloodstream infections.

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

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.

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.

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.

Antibiotics are widely used by all specialties in the hospital setting. We evaluated previously defined high-risk antibiotic use in relation to Clostridioides difficile infections (CDIs).

We analyzed 2016–2017 data from 171 hospitals. High-risk antibiotics included second-, third-, and fourth-generation cephalosporins, fluoroquinolones, carbapenems, and lincosamides. A CDI case was a positive stool C. difficile toxin or molecular assay result from a patient without a positive result in the previous 8 weeks. Hospital-associated (HA) CDI cases included specimens collected >3 calendar days after admission or ≤3 calendar days from a patient with a prior same-hospital discharge within 28 days. We used the multivariable Poisson regression model to estimate the relative risk (RR) of high-risk antibiotic use on HA CDI, controlling for confounders.

The median days of therapy for high-risk antibiotic use was 241.2 (interquartile range [IQR], 192.6–295.2) per 1,000 days present; the overall HA CDI rate was 33 (IQR, 24–43) per 10,000 admissions. The overall correlation of high-risk antibiotic use and HA CDI was 0.22 (P = .003), and higher correlation was observed in teaching hospitals (0.38; P = .002). For every 100-day (per 1,000 days present) increase in high-risk antibiotic therapy, there was a 12% increase in HA CDI (RR, 1.12; 95% CI, 1.04–1.21; P = .002) after adjusting for confounders.

High-risk antibiotic use is an independent predictor of HA CDI. This assessment of poststewardship implementation in the United States highlights the importance of tracking trends of antimicrobial use over time as it relates to CDI.

Limitations in sample size, overly inclusive antibiotic classes, lack of adjustment of key risk variables, and inadequate assessment of cases contribute to widely ranging estimates of risk factors for Clostridium difficile infection (CDI).

To incorporate all key CDI risk factors in addition to 27 antibiotic classes into a single comprehensive model.

Retrospective cohort study.

Kaiser Permanente Southern California.

Members of Kaiser Permanente Southern California at least 18 years old admitted to any of its 14 hospitals from January 1, 2011, through December 31, 2012.

Hospital-acquired CDI cases were identified by polymerase chain reaction assay. Exposure to major outpatient antibiotics (10 classes) and those administered during inpatient stays (27 classes) was assessed. Age, sex, self-identified race/ethnicity, Charlson Comorbidity Score, previous hospitalization, transfer from a skilled nursing facility, number of different antibiotic classes, statin use, and proton pump inhibitor use were also assessed. Poisson regression estimated adjusted risk of CDI.

A total of 401,234 patients with 2,638 cases of incident CDI (0.7%) were detected. The final model demonstrated highest CDI risk associated with increasing age, exposure to multiple antibiotic classes, and skilled nursing facility transfer. Factors conferring the most reduced CDI risk were inpatient exposure to tetracyclines and first-generation cephalosporins, and outpatient macrolides.

Although type and aggregate antibiotic exposure are important, the factors that increase the likelihood of environmental spore acquisition should not be underestimated. Operationally, our findings have implications for antibiotic stewardship efforts and can inform empirical and culture-driven treatment approaches.

Infect. Control Hosp. Epidemiol. 2015;36(12):1409–1416