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To evaluate the hospital-reported cost of care, clinical burden, and incidence of hospital-onset bacteremia and fungemia (HOB) for hospital admissions with surgical site infections (SSI).
Methods:
A cross-sectional study of 38 acute-care hospital admissions with a procedure under the National Healthcare Safety Network (NHSN) surveillance for SSI was conducted. SSI admissions were identified through NHSN reporting by the hospital. Clinical outcomes were estimated for SSI compared to no SSI controls using propensity matching and multivariable adjusted models that controlled for patient and hospital demographics; these endpoints were also compared for SSI admissions with and without HOB co-occurrence.
Results:
The rate of hospital-reported SSI was 0.15 per 100 admissions with a procedure under surveillance for SSI. Admissions with SSI compared to no SSI had significantly higher incremental hospital-reported cost of $30,689 and length of stay (LOS) was 11.6 days higher. The incidence of HOB was 6-fold higher in admissions with SSI compared to no SSI. For SSI admissions with HOB vs. no HOB, HOB added $28,049 to cost of care and 6.5 days to the LOS.
Conclusions:
Hospital-reported SSIs were associated with higher clinical and economic burden. Patients with SSI and HOB had even more deleterious outcomes. These data may inform programs to augment infection prevention bundles targeting SSIs and downstream complications or comorbidities like HOB.
This study quantified the burden of hospital-onset bacteremia and fungemia (HOB) among cancer and transplant patients compared to other patients.
Methods:
A retrospective cross-sectional study used data from 41 hospitals between October 2015 and June 2019. Hospitalizations were segmented into categories using diagnosis-related groups (DRG): myeloproliferative (MP) cancer, solid tumor cancer, transplant, and non-cancer/non-transplant (“reference group”). To quantify the association between DRG and HOB, multivariable adjusted Poisson regression models were fit. Analyses were stratified by length of stay (LOS).
Results:
Of 645,315 patients, 59% were female and the majority 41 years of age or older (76%). Hospitalizations with MP cancer and transplant demonstrated higher HOB burden compared to the reference group, regardless of LOS category. For all hospitalizations, the >30 days LOS category had a higher burden of HOB. The median time to reportable HOB was within 30 days regardless of duration of hospitalization (reference, 8 days; solid tumor cancer, 8 days; transplant, 12 days; MP cancer, 13 days).
Conclusion:
MP cancer and transplant patients had a higher burden of HOB compared to other hospitalized patients regardless of LOS. Whether these infections are preventable should be further evaluated to inform quality metrics involving reportable bacteremia and fungemia.
We investigated trends in Staphylococcus aureus (staph) bacteremia incidence stratified by methicillin susceptibility (methicillin-susceptible S. aureus [MSSA] vs. methicillin-resistant S. aureus [MRSA]) and onset designation (community-onset [CO] vs. hospital-onset [HO]).
Methods:
We evaluated the microbiological data among adult patients who were admitted to 267 acute-care hospitals during October 1, 2015, to February 28, 2020. Using a subset of data from 41 acute-care hospitals, we conducted a retrospective cohort study to assess patient demographics, characteristics, mortality, length of stay, and costs. We also conducted a case-control study between those with and without staph bacteremia.
Results:
The incidence of MSSA bacteremia significantly increased from 2.43 per 1,000 admissions to 2.87 per 1,000 admissions (estimate=0.0047, P-value=.0006). The incidence of MRSA significantly increased from 2.11 per 1,000 admissions to 2.42 per 1,000 admissions (estimate=0.0126, P-value <.0001). While the incidence of CO MSSA and CO MRSA demonstrated a significant increase (p=0.0023, and p < 0.0001), the incidence of HO MSSA and HO MRSA did not significantly change (p=0.2795 and p < 0.4464). Compared to those without staph bacteremia, mortality, length of stay, and total cost were significantly higher in those with staph bacteremia, regardless of methicillin susceptibility or onset designation.
Conclusion:
The increasing incidence of CO MSSA and MRSA bacteremia might suggest the necessity for dedicated infection control measures and interventions for community members colonized with or at risk of acquiring Staphylococcus aureus.
To describe the relative burden of catheter-associated urinary tract infections (CAUTIs) and non-CAUTI hospital-onset urinary tract infections (HOUTIs).
Methods:
A retrospective observational study of patients from 43 acute-care hospitals was conducted. CAUTI cases were defined as those reported to the National Healthcare Safety Network. Non-CAUTI HOUTI was defined as a positive, non-contaminated, non-commensal culture collected on day 3 or later. All HOUTIs were required to have a new antimicrobial prescribed within 2 days of the first positive urine culture. Outcomes included secondary hospital-onset bacteremia and fungemia (HOB), total hospital costs, length of stay (LOS), readmission risk, and mortality.
Results:
Of 549,433 admissions, 434 CAUTIs and 3,177 non-CAUTI HOUTIs were observed. The overall rate of HOB likely secondary to HOUTI was 3.7%. Total numbers of secondary HOB were higher in non-CAUTI HOUTIs compared to CAUTI (101 vs 34). HOB secondary to non-CAUTI HOUTI was more likely to originate outside the ICU compared to CAUTI (69.3% vs 44.1%). CAUTI was associated with adjusted incremental total hospital cost and LOS of $9,807 (P < .0001) and 3.01 days (P < .0001) while non-CAUTI HOUTI was associated with adjusted incremental total hospital cost and LOS of $6,874 (P < .0001) and 2.97 days (P < .0001).
Conclusion:
CAUTI and non-CAUTI HOUTI were associated with deleterious outcomes. Non-CAUTI HOUTI occurred more often and was associated with a higher facility aggregate volume of HOB than CAUTI. Patients at risk for UTIs in the hospital represent a vulnerable population who may benefit from surveillance and prevention efforts, particularly in the non-ICU setting.
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.
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.
Methods:
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.
Results:
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.
Conclusions:
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.
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.
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