To determine the best nursing home facility characteristics for aggregating antibiotic susceptibility testing results across nursing homes to produce a useful annual antibiogram that nursing homes can use in their antimicrobial stewardship programs.

Derivation cohort study.

Center for Medicare and Medicaid Services (CMS) certified skilled nursing facilities in Georgia (N = 231).

All residents of eligible facilities submitting urine culture specimens for microbiologic testing at a regional referral laboratory.

Crude and adjusted metrics of antibiotic resistance prevalence (percent of isolates testing susceptible) for 5 bacterial species commonly recovered from urine specimens were calculated using mixed linear models to determine which facility characteristics were predictive of testing antibiotic susceptibility.

In a single year, most facilities had an insufficient number of isolates tested to create facility-specific antibiograms: 49% of facilities had sufficient Escherichia coli isolates tested, but only about 1 in 10 had sufficient isolates of Klebsiella pneumoniae, Proteus mirabilis, Enterococcus faecalis, or Pseudomonas aeruginosa. After accounting for antibiotic tested and age of the patient, facility characteristics predictive of susceptibility were: E. coli, region, year, average length of stay; K. pneumoniae, region, bed size; P. mirabilis, region; and for E. faecalis or P. aerginosa no facility parameter remained in the model.

Nursing homes often have insufficient data to create facility-specific antibiograms; aggregating data across nursing homes in a region is a statistically sound approach to overcoming data shortages in nursing home stewardship programs.

To assess the feasibility of electronic data capture of postdischarge durations and evaluate total durations of antimicrobial exposure related to inpatient hospital stays.

Multicenter, retrospective cohort study.

Two community hospitals and 1 academic medical center.

Hospitalized patients who received ≥1 dose of a systemic antimicrobial agent.

We collected and reviewed electronic data on inpatient and discharge antimicrobial prescribing from April to September 2016 in 3 pilot hospitals. Inpatient antimicrobial use was obtained from electronic medication administration records. Postdischarge antimicrobial use was calculated from electronic discharge prescriptions. We completed a manual validation to evaluate the ability of electronic prescriptions to capture intended postdischarge antibiotics. Inpatient, postdischarge, and total lengths of therapy (LOT) per admission were calculated to assess durations of antimicrobial therapy attributed to hospitalization.

A total of 45,693 inpatient admissions were evaluated. Antimicrobials were given during 23,447 admissions (51%), and electronic discharge prescriptions were captured in 7,442 admissions (16%). Manual validation revealed incomplete data capture in scenarios in which prescribers avoided the electronic system. The postdischarge LOT among admissions with discharge antimicrobials was median 8 days (range, 1–360) with peaks at 5, 7, 10, and 14 days. Postdischarge days accounted for 38% of antimicrobial exposure days.

Discharge antimicrobial therapy accounted for a large portion of antimicrobial exposure related to inpatient hospital stays. Discharge prescription data can feasibly be captured through electronic prescribing records and may aid in designing stewardship interventions at transitions of care.

To measure the association between receipt of specific infection prevention interventions and procedure-related cardiac implantable electronic device (CIED) infections.

Retrospective cohort with manually reviewed infection status.

Setting: National, multicenter Veterans Health Administration (VA) cohort.

Sampling of procedures entered into the VA Clinical Assessment Reporting and Tracking-Electrophysiology (CART-EP) database from fiscal years 2008 through 2015.

A sample of procedures entered into the CART-EP database underwent manual review for occurrence of CIED infection and other clinical/procedural variables. The primary outcome was 6-month incidence of CIED infection. Measures of association were calculated using multivariable generalized estimating equations logistic regression.

We identified 101 procedure-related CIED infections among 2,098 procedures (4.8% of reviewed sample). Factors associated with increased odds of infections included (1) wound complications (adjusted odds ratio [aOR], 8.74; 95% confidence interval [CI], 3.16–24.20), (2) revisions including generator changes (aOR, 2.4; 95% CI, 1.59–3.63), (3) an elevated international normalized ratio (INR) >1.5 (aOR, 1.56; 95% CI, 1.12–2.18), and (4) methicillin-resistant Staphylococcus colonization (aOR, 9.56; 95% CI, 1.55–27.77). Clinically effective prevention interventions included preprocedural skin cleaning with chlorhexidine versus other topical agents (aOR, 0.41; 95% CI, 0.22–0.76) and receipt of β-lactam antimicrobial prophylaxis versus vancomycin (aOR, 0.60; 95% CI, 0.37–0.96). The use of mesh pockets and continuation of antimicrobial prophylaxis after skin closure were not associated with reduced infection risk.

These findings regarding the real-world clinical effectiveness of different prevention strategies can be applied to the development of evidence-based protocols and infection prevention guidelines specific to the electrophysiology laboratory.

Current surveillance for healthcare-associated (HA) urinary tract infection (UTI) is focused on catheter-associated infection with hospital onset (HO-CAUTI), yet this surveillance does not represent the full burden of HA-UTI to patients. Our objective was to measure the incidence of potentially HA, community-onset (CO) UTI in a retrospective cohort of hospitalized patients.

Retrospective cohort study.

Academic, quaternary care, referral center.

Hospitalized adults at risk for HA-UTI from May 2009 to December 2011 were included.

Patients who did not experience a UTI during the index hospitalization were followed for 30 days post discharge to identify cases of potentially HA-CO UTI.

We identified 3,273 patients at risk for potentially HA-CO UTI. The incidence of HA-CO UTI in the 30 days post discharge was 29.8 per 1,000 patients. Independent risk factors of HA-CO UTI included paraplegia or quadriplegia (adjusted odds ratio [aOR], 4.6; 95% confidence interval [CI], 1.2–18.0), indwelling catheter during index hospitalization (aOR, 1.5; 95% CI, 1.0–2.3), prior piperacillin-tazobactam prescription (aOR, 2.3; 95% CI, 1.1–4.5), prior penicillin class prescription (aOR, 1.7; 95% CI, 1.0–2.8), and private insurance (aOR, 0.6; 95% CI, 0.4–0.9).

HA-CO UTI may be common within 30 days following hospital discharge. These data suggest that surveillance efforts may need to be expanded to capture the full burden to patients and better inform antibiotic prescribing decisions for patients with a history of hospitalization.

To evaluate the Orange County Clostridium difficile infection (CDI) prevention collaborative’s effect on rates of CDI in acute-care hospitals (ACHs) in Orange County, California.

Controlled interrupted time series.

We convened a CDI prevention collaborative with healthcare facilities in Orange County to reduce CDI incidence in the region. Collaborative participants received onsite infection control and antimicrobial stewardship assessments, interactive learning and discussion sessions, and an interfacility transfer communication improvement initiative during June 2015–June 2016. We used segmented regression to evaluate changes in monthly hospital-onset (HO) and community-onset (CO) CDI rates for ACHs. The baseline period comprised 17 months (January 2014–June 2015) and the follow-up period comprised 28 months (September 2015–December 2017). All 25 Orange County ACHs were included in the CO-CDI model to account for direct and indirect effects of the collaborative. For comparison, we assessed HO-CDI and CO-CDI rates among 27 ACHs in 3 San Francisco Bay Area counties.

HO-CDI rates in the 15 participating Orange County ACHs decreased 4% per month (incidence rate ratio [IRR], 0.96; 95% CI, 0.95–0.97; P < .0001) during the follow-up period compared with the baseline period and 3% (IRR, 0.97; 95% CI, 0.95–0.99; P = .002) per month compared to the San Francisco Bay Area nonparticipant ACHs. Orange County CO-CDI rates declined 2% per month (IRR, 0.98; 95% CI, 0.96–1.00; P = .03) between the baseline and follow-up periods. This decline was not statistically different from the San Francisco Bay Area ACHs (IRR, 0.97; 95% CI, 0.95–1.00; P = .09).

Our analysis of ACHs in Orange County provides evidence that coordinated, regional multifacility initiatives can reduce CDI incidence.

Clostridioides difficile (C. difficile) poses a major challenge to the healthcare system. We assessed factors that should be considered when designing subprocesses of a C. difficile infection (CDI) prevention bundle.

Phenomenological qualitative study.

We conducted 3 focus groups of environmental services (EVS) staff, physicians, and nurses to assess their perspectives on a CDI prevention bundle. We used the Systems Engineering Initiative for Patient Safety (SEIPS) model to examine 5 subprocesses of the CDI bundle: diagnostic testing, empiric isolation, contact isolation, hand hygiene, and environmental disinfection. We coded transcripts to the 5 SEIPS elements and ensured scientific rigor. We sought to determine common, unique, and conflicting factors across stakeholder groups and subprocesses of the CDI bundle.

Each focus group lasted 1.5 hours on average. Common work-system barriers included inconsistencies in knowledge and practice of CDI management procedures; increased workload; poor setup of aspects of the physical environment (eg, inconvenient location of sinks); and inconsistencies in CDI documentation. Unique barriers and facilitators were related to specific activities performed by the stakeholder group. For instance, algorithmic approaches used by physicians facilitated timely diagnosis of CDI. Conflicting barriers or facilitators were related to opposing objectives; for example, clinicians needed rapid placement of a patient in a room while EVS staff needed time to disinfect the room.

A systems engineering approach can help to holistically identify factors that influence successful implementation of subprocesses of infection prevention bundles.

Healthcare workers (HCWs) are at risk of acquiring and transmitting respiratory viruses while working in healthcare settings.

To investigate the incidence of and factors associated with HCWs working during an acute respiratory illness (ARI).

HCWs from 9 Canadian hospitals were prospectively enrolled in active surveillance for ARI during the 2010–2011 to 2013–2014 influenza seasons. Daily illness diaries during ARI episodes collected information on symptoms and work attendance.

At least 1 ARI episode was reported by 50.4% of participants each study season. Overall, 94.6% of ill individuals reported working at least 1 day while symptomatic, resulting in an estimated 1.9 days of working while symptomatic and 0.5 days of absence during an ARI per participant season. In multivariable analysis, the adjusted relative risk of working while symptomatic was higher for physicians and lower for nurses relative to other HCWs. Participants were more likely to work if symptoms were less severe and on the illness onset date compared to subsequent days. The most cited reason for working while symptomatic was that symptoms were mild and the HCW felt well enough to work (67%). Participants were more likely to state that they could not afford to stay home if they did not have paid sick leave and were younger.

HCWs worked during most episodes of ARI, most often because their symptoms were mild. Further data are needed to understand how best to balance the costs and risks of absenteeism versus those associated with working while ill.

To assess the impact of incorporating early rapid influenza diagnosis on antimicrobial usage, nosocomial influenza transmission, length of stay, and occupancy rates among hospitalized patients.

A 1,100 bed tertiary-care hospital in southern Israel.

We implemented early rapid detection of influenza with immediate communication of results. Using Orion methods, we compared the 2017–2018 influenza season to the prior season in our hospital and to the 2017–2018 occupancy rates at other Israeli hospitals.

During the intervention season, 5,006 patients were admitted; 1,824 were tested for influenza, of whom 437 (23.9%) were positive. In the previous season, 4,825 patients were admitted; 1,225 were tested and 288 (23.5%) were positive. Time from admission to test report decreased from 35.5 to 18.4 hours (P < .001). Early discharge rates significantly increased, from 21.5% to 41.6% at 36 hours, from 37.2% to 54.5% at 48 hours, and from 66% to 73.2% at 72 hours. No increase in repeat ER visits, readmission, or mortality rates was observed. Hospital occupancy decreased by 10% compared to the previous year and was 26% lower than the national rate. Hospital-acquired influenza cases were reduced from 37 (11.4%) to 12 (2.7%) (P < .001). Antibiotic usage was reduced both before and after notification of test results by 16% and 12%, respectively.

Implementing this intervention led to earlier discharge of patients, lower occupancy in medical wards, reduced antibiotic administration, and fewer hospital-acquired influenza events. This strategy is useful for optimizing hospital resources, and its implementation should be considered for upcoming influenza seasons.