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Objective: Urine cultures collected from catheterized patients have a high likelihood of false-positive results due to colonization. We examined the impact of a clinical decision support (CDS) tool that includes catheter information on test utilization and patient-level outcomes. Methods: This before-and-after intervention study was conducted at 3 hospitals in North Carolina. In March 2021, a CDS tool was incorporated into urine-culture order entry in the electronic health record, providing education about indications for culture and suggesting catheter removal or exchange prior to specimen collection for catheters present >7 days. We used an interrupted time-series analysis with Poisson regression to evaluate the impact of CDS implementation on utilization of urinalyses and urine cultures, antibiotic use, and other outcomes during the pre- and postintervention periods. Results: The CDS tool was prompted in 38,361 instances of urine cultures ordered in all patients, including 2,133 catheterized patients during the postintervention study period. There was significant decrease in urine culture orders (1.4% decrease per month; P < .001) and antibiotic use for UTI indications (2.3% decrease per month; P = .006), but there was no significant decline in CAUTI rates in the postintervention period. Clinicians opted for urinary catheter removal in 183 (8.5%) instances. Evaluation of the safety reporting system revealed no apparent increase in safety events related to catheter removal or reinsertion. Conclusion: CDS tools can aid in optimizing urine culture collection practices and can serve as a reminder for removal or exchange of long-term indwelling urinary catheters at the time of urine-culture collection.

Background:Clinicians and laboratories routinely use urinalysis (UA) parameters to determine whether antimicrobial treatment and/or urine cultures are needed. Yet the performance of individual UA parameters and common thresholds for action are not well defined and may vary across different patient populations.Methods:In this retrospective cohort study, we included all encounters with UAs ordered 24 hours prior to a urine culture between 2015 and 2020 at 3 North Carolina hospitals. We evaluated the performance of relevant UA parameters as potential outcome predictors, including sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We also combined 18 different UA criteria and used receiver operating curves to identify the 5 best-performing models for predicting significant bacteriuria (≥100,000 colony-forming units of bacteria/mL).Results:In 221,933 encounters during the 6-year study period, no single UA parameter had both high sensitivity and high specificity in predicting bacteriuria. Absence of leukocyte esterase and pyuria had a high NPV for significant bacteriuria. Combined UA parameters did not perform better than pyuria alone with regard to NPV. The high NPV ≥0.90 of pyuria was maintained among most patient subgroups except females aged ≥65 years and patients with indwelling catheters.Conclusion:When used as a part of a diagnostic workup, UA parameters should be leveraged for their NPV instead of sensitivity. Because many laboratories and hospitals use reflex urine culture algorithms, their workflow should include clinical decision support and or education to target symptomatic patients and focus on populations where absence of pyuria has high NPV.

Policies that promote conversion of antibiotics from intravenous to oral route administration are considered “low hanging fruit” for hospital antimicrobial stewardship programs. We developed a simple metric based on digestive days of therapy divided by total days of therapy for targeted agents and a method for hospital comparisons. External comparisons may help identify opportunities for improving prospective implementation.

Objectives:The typical 5-day work week affects healthcare outcomes. Structured work hours have also been implicated in antimicrobial prescribing choice. We developed a visualization tool to aid in evaluating breadth of antibiotic use in various time (day of week and hour of day) and space (patient location) combinations.Methods:We evaluated antibiotic administration data from a tertiary-care academic medical center between July 1, 2018, and July 1, 2020. We calculated a cumulative empiric antibiotic spectrum score by adapting a previously validated antibiotic spectrum index (ASI) and applying that score to empiric antibiotic use. We visualized these data as a heat map based on various day-of-week–time combinations and then compared the distribution of scores between weekday nights, weekend days, and weekend nights to the typical workweek hours (weekday days, weekday days) using the Mann-Whitney U nonparametric test with a Bonferroni correction.Results:The analysis included 76,535 antibiotic starts across 53,900 unique patient admissions over 2 years. The mean cumulative ASI was higher in all 3 night and weekend combinations (weekday nights, 7.3; weekend days, 7.6; weekend nights, 7.5) compared to the weekday daytime hours (weekday days, 7.1) and the distribution of scores was different in all groups compared to the weekday daytime reference. The cumulative ASI was also higher in intensive care units.Conclusions:Empiric antibiotic prescribing patterns differed across space and time; broader antibiotic choices occurred in the intensive care units and on nights and weekends. Visualization of these patterns aids in antimicrobial prescribing pattern recognition and may assist in finding opportunities for additional antimicrobial stewardship interventions.

Executive summaryTesting of asymptomatic patients for severe acute respiratory coronavirus virus 2 (SARS-CoV-2) (ie, “asymptomatic screening) to attempt to reduce the risk of nosocomial transmission has been extensive and resource intensive, and such testing is of unclear benefit when added to other layers of infection prevention mitigation controls. In addition, the logistic challenges and costs related to screening program implementation, data noting the lack of substantial aerosol generation with elective controlled intubation, extubation, and other procedures, and the adverse patient and facility consequences of asymptomatic screening call into question the utility of this infection prevention intervention. Consequently, the Society for Healthcare Epidemiology of America (SHEA) recommends against routine universal use of asymptomatic screening for SARS-CoV-2 in healthcare facilities. Specifically, preprocedure asymptomatic screening is unlikely to provide incremental benefit in preventing SARS-CoV-2 transmission in the procedural and perioperative environment when other infection prevention strategies are in place, and it should not be considered a requirement for all patients. Admission screening may be beneficial during times of increased virus transmission in some settings where other layers of controls are limited (eg, behavioral health, congregate care, or shared patient rooms), but widespread routine use of admission asymptomatic screening is not recommended over strengthening other infection prevention controls. In this commentary, we outline the challenges surrounding the use of asymptomatic screening, including logistics and costs of implementing a screening program, and adverse patient and facility consequences. We review data pertaining to the lack of substantial aerosol generation during elective controlled intubation, extubation, and other procedures, and we provide guidance for when asymptomatic screening for SARS-CoV-2 may be considered in a limited scope.

Background Antibiotic Stewardship Programs (ASPs) assist front-line clinicians in synthesizing emerging data and establishing best practices. Our ASP team directly maintained and edited an internal web application, Duke CustomID®, to disseminate updated guideline, policy, and drug information during COVID-19. We aimed to describe website engagement and maintenance during the dynamic pandemic period. Methods We performed a descriptive, time-series analysis using Google Analytics software to measure engagement with Duke CustomID® during a 1-year pre-pandemic period through the Omicron surge: January 2019 to March 2022. We measured total page views (or “hits”), COVID-specific page hits, and days requiring COVID-specific page edits by week. Given fluctuations in hospitalization rates, we defined the primary outcome as the rate of hits divided by total hospitalizations. Weekly data were assessed graphically with positive COVID tests and COVID hospitalizations. We used negative binomial regression to quantify the association between COVID hospitalizations and hit rates and to trend engagement over time, adjusted for seasonality. We stratified data by COVID page and calculated a hit/edit ratio. Results Engagement with CustomID® increased during the pandemic period, especially during surges (Figure). Hits in the pre-pandemic period were median 1707 (range 1165-2354) per week, and hit rates median 1.95 per hospitalization (range 1.40-2.86). Peaks were observed in March 2020 (hit rate 4.59) and January 2022 (hit rate 3.87). On average, for every 100 COVID hospitalizations, the hit rate increased by 0.08 (0.004-0.16, p=0.04). Engagement slowly increased over the study period (relative rate week 1 versus 170: 1.15, 95% confidence interval 1.02-1.28, p=0.02). COVID page edits per week had a median of 2 (range 0-12). Adult Inpatient Guidelines and COVID Monoclonal Antibody pages had highest use (Table). Duke CustomID Hits and Maintenance Efforts over the Pandemic Top: COVID-specific CustomID hits per week (Green), Positive COVID tests per week (Blue) over time Middle: Total custom ID page hits relative to total hospitalizations per week (teal), COVID hospitalizations (Red) Bottom: Number of edits to COVID-specific CustomID pages per week, stratified by management pages and drug pages Several dates of significance are highlighted including the Emergency Use Authorizations (EUA) for remdesivir, the COVID Vaccines, and Paxlovid Duke CustomID COVID-19 Page Hits and Edits COVID specific pages on Duke CustomID with total hits, edits, and ratio over the pandemic Conclusion Our ASP’s website was a highly utilized, practical tool for disseminating practice-changing information during the pandemic. Use increased over time and especially during surges. An electronic reference customized for local practice and rapidly updated by ASPs offers critical support for front-line clinicians. Disclosures Martha B. Adams, M.D., Custom Clinical Decision Support, Inc: Board Member|Custom Clinical Decision Support, Inc: Ownership Interest Richard H. Drew, PharmD MS, American College of Clinical Pharmacists: Publication royalties|Takeda: Advisor/Consultant|UpToDate: publication royalties Rebekah W. Moehring, MD, MPH, FIDSA, FSHEA, UpToDate, Inc.: Author Royalties Michael J. Smith, M.D., M.S.C.E, Merck: Grant/Research Support|Pfizer: Grant/Research Support.

Background COVID-19 shifted antibiotic stewardship program resources and changed antibiotic use (AU). Shifts in patient populations with COVID surges, including pauses to surgical procedures, and dynamic practice changes makes temporal associations difficult to interpret. Our analysis aimed to address the impact of COVID on AU after adjusting for other practice shifts. Methods We performed a longitudinal analysis of AU data from 30 Southeast US hospitals. Three pandemic phases (1: 3/20–6/20; 2: 7/20–10/20; 3: 11/20–2/21) were compared to baseline (1/2018–1/2020). AU (days of therapy (DOT)/1000 patient days (PD)) was collected for all antimicrobial agents and specific subgroups: broad spectrum (NHSN group for hospital-onset infections), CAP (ceftriaxone, azithromycin, levofloxacin, moxifloxacin, and doxycycline), and antifungal. Monthly COVID burden was defined as all PD attributed to a COVID admission. We fit negative binomial GEE models to AU including phase and interaction terms between COVID burden and phase to test the hypothesis that AU changes during the phases were related to COVID burden. Models included adjustment for Charlson comorbidity, surgical volume, time since 12/2017 and seasonality. Results Observed AU rates by subgroup varied over time; peaks were observed for different subgroups during distinct pandemic phases (Figure). Compared to baseline, we observed a significant increase in overall, broad spectrum, and CAP groups during phase 1 (Table). In phase 2, overall and CAP AU was significantly higher than baseline, but in phase 3, AU was similar to baseline. These phase changes were separate from effects of COVID burden, except in phase 1 where we observed significant effects on antifungal (increased) and CAP (decreased) AU (Table). Conclusion Changes in hospital AU observed during early phases of the COVID pandemic appeared unrelated to COVID burden and may have been due to indirect pandemic effects (e.g., case mix, healthcare resource shifts). By pandemic phase 3, these disruptive effects were not as apparent, potentially related to shifts in non-COVID patient populations or ASP resources, availability of COVID treatments, or increased learning, diagnostic certainty, and provider comfort with avoiding antibacterials in patients with suspected COVID over time. Disclosures Melissa D. Johnson, PharmD, Biomeme: Licensed Transcriptional Signature for Candidemia|Charles River Laboratories: Grant/Research Support|Entasis Therapeutics: Advisor/Consultant|Merck & Co. Inc: Advisor/Consultant|Merck & Co. Inc: Grant/Research Support|Pfizer, Inc.: Advisor/Consultant|Scynexis Inc.: Grant/Research Support|Theratechnologies: Advisor/Consultant Angelina Davis, PharmD, M.S., Merck & Co.: Honoraria Sonali D. Advani, MBBS, MPH, FIDSA, Locus Biosciences: Advisor/Consultant|Locus Biosciences: Honoraria|Sysmex America: Advisor/Consultant Ibukun Kalu, MD, Pfizer, Inc.: Institutional support for clinical trial Rebekah W. Moehring, MD, MPH, FIDSA, FSHEA, UpToDate, Inc.: Author Royalties.

Background Broad-spectrum antibiotic use is a known risk factor for candidemia, but the duration and type of antibiotic exposure associated with greatest risk are not well characterized. Measurements of antibiotic days may be useful to assess cumulative burden of selective pressure on the microbiome and risk for candidemia. Methods This retrospective cohort study aimed to quantify the effect of antibiotic exposure on risk for candidemia. The primary outcome was hazard for developing candidemia across an array of antibiotic agents, classes and spectra. We measured antibiotic days of therapy (DOT) for adults admitted to Duke University hospitals 1/1/2016–12/31/2021. We excluded patients with community-onset candidemia, defined as growth of Candida spp. in blood culture collected ≤ 48 hours of admission, because antibiotic exposure prior to arrival was not reliably accessible. Time-to-event analyses were performed using the Nelson-Aalen estimator for modeling cumulative hazard functions to compare the proportion of candidemia observed based on exposure to each antibiotic. Logrank tests were used to evaluate for differences between hazard functions with a pre-specified alpha level of 0.05, and exponential cumulative proportional hazards models were implemented to generalize hazard functions. Results During 164,185 encounters in 105,330 unique patients, we identified candidemia in 237 patients. Prior to developing candidemia, cases received a total of 9,604 antibacterial DOT distributed across 46 unique antibiotic agents (Fig. 1) Carbapenems were associated with increased hazard for candidemia compared to beta-lactams (p< 0.005) (Figs. 2–3). There were 57 encounters for candidemia where meropenem was administered with a median of 10 DOT prior to onset of candidemia. Days of Antibiotic Therapy by Agent and Outcome. Conclusion This work represents a novel approach to quantifying antibiotic exposure as a risk factor for candidemia. In an unadjusted model, we identified carbapenems as a high-risk class; additional analysis with adjusted regression models will help contextualize exposure risk with respect to comorbidity and illness severity. This work may serve as a reference for antibiotic stewards as they promote appropriate antibiotic use, including reducing overuse of broad-spectrum antibiotics. Disclosures Rebekah W. Moehring, MD, MPH, FIDSA, FSHEA, UpToDate, Inc.: Author Royalties Barbara D. Alexander, MD, Astellas: Advisor/Consultant|HealthtrackRx: Advisor/Consultant|HealthtrackRx: Grant/Research Support|Scynexis: Grant/Research Support|UpToDate: Advisor/Consultant Melissa D. Johnson, PharmD, Biomeme: Licensed Transcriptional Signature for Candidemia|Charles River Laboratories: Grant/Research Support|Entasis Therapeutics: Advisor/Consultant|Merck & Co. Inc: Advisor/Consultant|Merck & Co. Inc: Grant/Research Support|Pfizer, Inc.: Advisor/Consultant|Scynexis Inc.: Grant/Research Support|Theratechnologies: Advisor/Consultant.

Background Benchmarking AU is important to identify opportunities and allocate resources within a health system. Patient level factors such as infection diagnosis codes further refine risk adjustments but have not been more widely adopted because of the burden of accurately collecting and submitting granular data. The goal of this study was to evaluate a novel metric to estimate facility-level infection burden as a potential factor to use in adjustment of AU. Methods We conducted a retrospective analysis of hospital administrative data (for calendar year 2020) from 8 hospitals in a single health system using a common electronic health record and coding department. We identified inpatient encounters with an infection-related primary ICD-10 code (I-PDX), based on the health system’s coding department determination and extracted the length of stay (LOS) for each encounter. For any encounter with an I-PDX, the entire LOS was classified as infection-related patient days (IPD). Overall AU in days of therapy (DOT) was adjusted using two novel infection diagnoses denominators. The first was based on proportion of total patient days (PD) attributable to I-PDX encounters (% I-PDX x PD). Since LOS tends to be longer in I-PDX, we also calculated DOT with adjustment for actual extracted IPDs. We then rank ordered study hospitals based on standard DOT / 1,000 PD, NHSN SAAR metrics, and our novel DOT / (% I-PDX x PD) and DOT / 1,000 IPD metrics. Results The proportion of I-PDX was highly variable among hospitals, with a system-wide median of 37.27% (range 23.48 - 43.32) (Figure 1). Using DOT / 1,000 patient days for 1 year, Hospital A was the lowest in the system and hospital H was the highest (Figure 2). However, after adjusting for the proportion of patients with I-PDX encounters and IPDs, hospital rank changed considerably, i.e. Hospital H and C respectively ranked lowest and Hospital A was highest. Conclusion These novel infection diagnoses PD denominators more closely associated facility level infection burden with AU, for a more refined rank order within the health system. These metrics provide an example of a parsimonious adjustment using patient level data that is already collected at any facility. Next steps might include indirect standardization using PDX categories and other patient level factors readily collected. Disclosures Melissa D. Johnson, PharmD, Charles River Laboratories: Grant/Research Support|Entasis: Honoraria|Merck: Grant/Research Support|Pfizer: Grant/Research Support|Scynexis: Grant/Research Support|Theratechnologies: Grant/Research Support|UpToDate: Honoraria Rebekah W. Moehring, MD, MPH, FIDSA, FSHEA, UpToDate, Inc.: Author Royalties Angelina Davis, PharmD, M.S., Merck & Co.: Honoraria.

Background The impact of the US Centers for Medicare & Medicaid Services (CMS) Severe Sepsis and Septic Shock: Management Bundle (SEP-1) core measure on overall antibacterial utilization is unknown. Methods We performed a retrospective multicenter longitudinal cohort study with interrupted time–series analysis to determine the impact of SEP-1 implementation on antibacterial utilization and patient outcomes. All adult patients admitted to 26 hospitals between 1 October 2014 and 30 September 2015 (SEP-1 preparation period) and between 1 November 2015 and 31 October 2016 (SEP-1 implementation period) were evaluated for inclusion. The primary outcome was total antibacterial utilization, measured as days of therapy (DOT) per 1000 patient-days. Results The study cohort included 701 055 eligible patient admissions and 4.2 million patient-days. Overall antibacterial utilization increased 2% each month during SEP-1 preparation (relative rate [RR], 1.02 per month [95% confidence interval {CI}, 1.00–1.04]; P = .02). Cumulatively, the mean monthly DOT per 1000 patient-days increased 24.4% (95% CI, 18.0%–38.8%) over the entire study period (October 2014–October 2016). The rate of sepsis diagnosis/1000 patients increased 2% each month during SEP-1 preparation (RR, 1.02 per month [95% CI, 1.00–1.04]; P = .04). The rate of all-cause mortality rate per 1000 patients decreased during the study period (RR for SEP-1 preparation, 0.95 [95% CI, .92–.98; P = .001]; RR for SEP-1 implementation, .98 [.97–1.00; P = .01]). Cumulatively, the monthly mean all-cause mortality rate/1000 patients declined 38.5% (95% CI, 25.9%–48.0%) over the study period. Conclusions Announcement and implementation of the CMS SEP-1 process measure was associated with increased diagnosis of sepsis and antibacterial utilization and decreased mortality rate among hospitalized patients.

We performed surveillance for hospital-acquired COVID-19 (HA-COVID-19) and compared time-based, electronic definitions to real-time adjudication of the most likely source of acquisition. Without real-time adjudication, nearly 50% of HA-COVID-19 cases identified using electronic definitions were misclassified. Both electronic and traditional contact tracing methods likely underestimated the incidence of HA-COVID-19.

Objective:To determine the impact of a documented penicillin or cephalosporin allergy on the development of surgical site infections (SSIs).Background:Appropriate preoperative antibiotic prophylaxis reduces SSI risk, but documented antibiotic allergies influence the choice of prophylactic agents. Few studies have examined the relationship between a reported antibiotic allergy and risk of SSI and to what extent this relationship is modified by the antibiotic class given for prophylaxis.Methods:We conducted a retrospective cohort study of adult patients undergoing coronary artery bypass, craniotomy, spinal fusion, laminectomy, hip arthroplasty and knee arthroplasty at 3 hospitals from July 1, 2013, to December 31, 2017. We built a multivariable logistic regression model to calculate the adjusted odds ratio (aOR) of developing an SSI among patients with and without patient-reported penicillin or cephalosporin allergies. We also examined effect measure modification (EMM) to determine whether surgical prophylaxis affected the association between reported allergy and SSI.Results:We analyzed 39,972 procedures; 1,689 (4.2%) with a documented patient penicillin or cephalosporin allergy, and 374 (0.9%) resulted in an SSI. Patients with a reported penicillin or cephalosporin allergy were more likely to develop an SSI compared to patients who did not report an allergy to penicillin or cephalosporins (adjusted odds ratio, 3.26; 95% confidence interval, 2.71–3.93). Surgical prophylaxis did not have significant EMM on this association.Conclusions:Patients who reported a penicillin or cephalosporin allergy had higher odds of developing an SSI than nonallergic patients. However, the increase in odds is not completely mediated by the type of surgical prophylaxis. Instead, a reported allergy may be a surrogate marker for a more complicated patient population.

Person-to-person contact is important for the transmission of health care-associated pathogens. Quantifying these contact patterns is crucial for modeling disease transmission and understanding routes of potential transmission.To generate and analyze the mixing matrices of hospital patients based on their contacts within hospital units.In this quality improvement study, mixing matrices were created using a weighted contact network of connected hospital patients, in which contact was defined as occupying the same hospital unit for 1 day. Participants included hospitalized patients at 299 hospital units in 24 hospitals in the Southeastern United States that were part of the Duke Antimicrobial Stewardship Outreach Network between January 2015 and December 2017. Analysis was conducted between October 2021 and February 2022.The mixing matrices of patients for each hospital unit were assessed using age, Elixhauser Score, and a measure of antibiotic exposure.Among 1 549 413 hospitalized patients (median [IQR] age, 44 [26-63] years; 883 580 [56.3%] women) in 299 hospital units, some units had highly similar patterns across multiple hospitals, although the number of patients varied to a great extent. For most of the adult inpatient units, frequent mixing was observed for older adult groups, while outpatient units (eg, emergency departments and behavioral health units) showed mixing between different age groups. Most units mixing patterns followed the marginal distribution of age; however, patients aged 90 years or older with longer lengths of stay created a secondary peak in some medical wards. From the mixing matrices by Elixhauser Score, mixing between patients with relatively higher comorbidity index was observed in intensive care units. Mixing matrices by antibiotic spectrum, a 4-point scale based on priority for antibiotic stewardship programs, resulted in 6 major distinct patterns owing to the variation of the type of antibiotics used in different units, namely those dominated by a single antibiotic spectrum (narrow, broad, or extended), 1 pattern spanning all antibiotic spectrum types and 2 forms of narrow- and extended-spectrum dominant exposure patterns (an emergency room where patients were exposed to one type of antibiotic or the other and a pediatric ward where patients were exposed to both types).This quality improvement study found that the mixing patterns of patients both within and between hospitals followed broadly expected patterns, although with a considerable amount of heterogeneity. These patterns could be used to inform mathematical models of health care-associated infections, assess the appropriateness of both models and policies for smaller community hospitals, and provide baseline information for the design of interventions that rely on altering patient contact patterns, such as practices for transferring patients within hospitals.

We analyzed the impact of a hospital tap water avoidance protocol on respiratory isolation of nontuberculous mycobacteria (NTM). After protocol implementation, hospital-onset episodes of respiratory NTM isolation on high-risk units decreased from 41.0 to 9.9 episodes per 10 000 patient-days (incidence rate ratio, 0.24; 95% confidence interval, .17–.34; P

ImportancePerson-to-person contact is important for the transmission of healthcare-associated pathogens. Quantifying these contact patterns is crucial for modeling disease transmission and understanding routes of potential transmission.ObjectiveGenerate and analyze the mixing matrices of hospital patients based on their contacts within hospital units.Design, Setting, and ParticipantsThe study was conducted in 24 hospitals in the Southeastern United States that were part of the Duke Antimicrobial Stewardship Outreach Network (DASON) between January 2015 and December 2017. There were a total of 1,569,413 patients and 299 hospital units.Main Outcome and MeasuresThe mixing matrices of patients for each hospital unit using age, Elixhauser Score, and a measure of antibiotic exposure.ResultsMixing matrices were calculated from a database of 24 hospitals, which included 2.9 million admission records for nearly 1.6 million patients. Some units had highly similar patterns across multiple hospitals although the number of patients might vary to a great extent. Within a period of 26 months (October 2015 and December 2017), the highest daily average is 765 patients in the ED of Hospital-12 and lowest daily average is only 2 patients in some of the smaller hospital units. For most of the adult inpatient units, frequent mixing was observed for older adult groups while outpatient units e.g. ED and Behavioral Health etc. units showed mixing between different age groups. From the mixing matrices by Elixhauser Score, we observed mixing between patients with relatively higher comorbidity index on the ICUs. Mixing matrices by Antibiotic Rank, a 4-point scale based on priority for antibiotic stewardship programs, resulted in six major distinct patterns due to the variation of the type of antibiotics used in different units.Conclusions and RelevanceThe mixing patterns of patients both within and between hospitals followed broadly expected patterns, though with a considerable amount of heterogeneity. These patterns can be used to evaluate the appropriateness of policies and guidelines for smaller community hospitals, as well as improve the design of interventions that rely on altering patient contact patterns.Key PointsQuestionWhat are the mixing patterns among hospitalized patients who could be susceptible to infection?FindingIn this study of 299 hospital units from 24 hospitals, we analyzed the mixing patterns between patients based on a number of variables namely Age, Antibiotic Ranks (4-point scale based on priority for antibiotic stewardships programs) and Elixhauser Comorbidity Score. While some units showed highly similar patterns across hospitals, variation has also been observed in concentration of mixing on different age groups and antibiotic usage among the patients who are coming into contact.MeaningHow patients mix, can impact their risk of acquiring an infection. While patterns followed what was expected heuristically, there is considerable between-hospital heterogeneity, which can help in risk assessments and modeling approaches.

We report the first case of Curvularia alcornii aortic pseudoaneurysm following bioprosthetic aortic valve replacement in an immunocompetent host. Infection was complicated by septic emboli to multiple organs. Despite aggressive surgical intervention and antifungal therapy, infection progressed. We review the literature on invasive Curvularia infection to inform diagnosis and management.

Background Urinary tract infections (UTIs) are often misdiagnosed or treated with exceedingly broad-spectrum antibiotics, leading to negative downstream effects. We aimed to implement antimicrobial stewardship (AS) strategies targeting UTI prescribing in the emergency department (ED). Methods We conducted a quasi-experimental prospective AS intervention outlining appropriate UTI diagnosis and management across 3 EDs, within an academic and 2 community hospitals, in North Carolina, United States. The study was divided into 3 phases: a baseline period and 2 intervention phases. Phase 1 included introduction of an ED-specific urine antibiogram and UTI guideline, education, and department-specific feedback on UTI diagnosis and antibiotic prescribing. Phase 2 included re-education and provider-specific feedback. Eligible patients included adults with an antibiotic prescription for UTI diagnosed in the ED from 13 November 2018 to 1 March 2021. Admitted patients were excluded. The primary outcome was guideline-concordant antibiotic use, assessed using an interrupted time-series regression analysis with 2-week intervals. Results Overall, 8742 distinct patients with 10 426 patient encounters were included. Ninety-two percent of all encounters (n = 9583) were diagnosed with cystitis and 8.1% with pyelonephritis (n = 843). There was an initial 15% increase in guideline-concordant antibiotic prescribing in phase 1 compared with the preintervention period (incidence rate ratio [IRR], 1.15; 95% confidence interval [CI], 1.03–1.29). A significant increase in guideline-concordant prescriptions was seen with every 2-week interval during phase 2 (IRR, 1.03; 95% CI, 1.01–1.04). Conclusions This multifaceted AS intervention involving a guideline, education, and provider-specific feedback increased guideline-concordant antibiotic choices for treat-and-release patients in the ED.

We performed a descriptive study of reflex urine culture (RUC) practices across 51 community hospitals in southeastern United States. We found that 26 unique reflexing criteria were used in 28 hospitals. Only 14% hospitals of hospitals that offered RUC restricted it to specific populations (eg, emergency room). Our data suggest that the current RUC approach in community hospitals warrants further validation of urinalysis criteria and identification of specific populations in which RUC performs best.

Background Few groups have formally studied the effect of dedicated antibiotic stewardship rounds (ASRs) on antibiotic use (AU) in intensive care units (ICUs). Methods We implemented weekly ASRs using a 2-arm, cluster-randomized, crossover study in 5 ICUs at Duke University Hospital from November 2017 to June 2018. We excluded patients without an active antibiotic order, or if they had a marker of high complexity including an existing infectious disease consult, transplantation, ventricular assist device, or extracorporeal membrane oxygenation. AU during and following ICU stay for patients with ASRs was compared to the controls. We recorded the number of reviews, recommendations delivered, and responses. We evaluated change in ICU-specific AU during and after the study. Results Our analysis included 4683 patients: 2330 intervention and 2353 controls. Teams performed 761 reviews during ASRs, which excluded 1569 patients: 60% of patients off antibiotics, and 8% complex patients. Exclusions affected 88% of cardiothoracic ICU (CTICU) patients. The AU rate ratio (RR) was 0.97 (95% confidence interval [CI], .91–1.04). When CTICU was removed, the RR was 0.93 (95% CI, .89–.98). AU in the poststudy period decreased by 16% (95% CI, 11%–24%) compared to AU in the baseline period. Change in AU was differential among units: largest in the neurology ICU (–28%) and smallest in the CTICU (–2%). Conclusions Weekly multidisciplinary ASRs was a high-resource intervention associated with a small AU reduction. The noticeable ICU AU decline over time is possibly due to indirect effects of ASRs. Effects differed among specialty ICUs, emphasizing the importance of customizing ASRs to match unit-specific population, workflow, and culture.

Background: Quantitative evaluation of antibiotic spectrum is an important, underutilized metric in measuring antibiotic use (AU) and may assist antimicrobial stewards in identifying targets and strategy for intervention. We evaluated the spectrum of initial antibiotic choices by hospital location, day of the week, and time of day to determine whether these factors may be associated with broad-spectrum antibiotic choices. Methods: We identified all admissions with antibiotic exposure in medical and surgical wards and critical care units in a tertiary academic medical center between July 1, 2014, and July 1, 2019. The antibiotic spectrum index (ASI), proposed by Gerber et al, is a numeric score based on the number of pathogens covered by a particular agent. We defined ASI for initial antibiotic choice as follows: ASI for each unique antibiotic administered within 24 hours of the first antibiotic administration was summed and assigned to the administration time of the first dose. We categorized time into 4 distinct categories: weekday days (Monday–Friday, 7 a.m.–7 p.m.), weekday nights, weekend days, and weekend nights. Weekend time began 7 p.m. Friday and ended 7 a.m. Monday. We constructed heatmaps stratified by hospital location. Mann-Whitney U tests were applied to evaluate differences in the distributions of ASI using weekday days as a reference. Results: Data included 90,455 unique antibiotic admissions with initial antibiotic starts in medical and surgical wards and critical care units. Patterns of ASI for initial antibiotic choice varied between unit locations and time (Figs. 1 and 2). Mean and median ASIs for initial antibiotic choices were higher for medical ward and medical ICUs than for surgical wards and surgical ICUs. Initial antibiotic choices had higher ASIs during overnight hours for all units except the surgical ICU. Notable differences in ASIs were identified between weekday and weekend prescribing for surgical units, whereas medical units demonstrated less extreme differences. Conclusion: We observed a “weekend effect” across hospital units; the most extreme occurred in surgical wards. This observation may be due to differences in patient volume and rounding patterns. For example, hospitalist and critical care units have 7-day schedules, whereas surgical wards are highly influenced by operating room schedules. Antimicrobial stewardship teams may use these data to identify strategies targeting the most opportune time and place to intervene on the spectrum of initial antibiotic choice.Funding: NoneDisclosures: None

We analyzed antibiotic use data from 29 southeastern US hospitals over a 5-year period to determine changes in antibiotic use after the fluoroquinolone US Food and Drug Administration (FDA) advisory update in 2016. Fluoroquinolone use declined both before and after the FDA announcement, and the use of select, alternative antibiotics increased after the announcement.Fluoroquinolones are among the 4 most commonly prescribed antibiotic classes.1,2 Postmarketing reports of serious adverse events linked to fluoroquinolones include tendonitis, neuropathy, hypoglycemia, psychiatric side effects, and possible aortic vessel rupture, leading to safety label changes in July 2008 and August 2013.3 In July 2016, the US Food and Drug Administration (FDA) strengthened the “black box” warning following an initial safety announcement in May 2016, recommending avoidance of fluoroquinolones for uncomplicated infections such as acute exacerbation of chronic bronchitis, uncomplicated urinary tract infections, and acute bacterial sinusitis.4 Concerns over safety and the association with Clostridiodes difficile infection have led inpatient antimicrobial stewardship programs (ASPs) to develop initiatives to promote avoidance of quinolones. The objective of this study was to quantify the effect of the 2016 FDA “black box” update on inpatient antibiotic use among a cohort of southeastern US hospitals.

This review illustrates relevant and current antibiotic use (AU) metrics and demonstrates their differences. The authors also highlight novel, increasingly complex AU metrics proposed for use in antibiotic stewardship research. Many common antibiotic use metrics (e.g., days of therapy, length of therapy) are well defined, widely generalizable, and commonly used by antimicrobial stewardship programs. Multiple measures are required to comprehensively capture real-world AU, though utilization metrics often lack important contextual information needed to inform antibiotic stewardship strategy. Combining standard AU metrics with meaningful characterization of antibiotic prescriptions, including appropriateness of therapy or antibiotic spectrum, provides important information for developing strategy. Current antibiotic use metrics are limited by an inability to convey meaningful context. Future research should aim to define generalizable measures that illustrate improved prescribing in addition to volume of use.

Objective:To assess the feasibility of electronic data capture of postdischarge durations and evaluate total durations of antimicrobial exposure related to inpatient hospital stays.Design:Multicenter, retrospective cohort study.Setting:Two community hospitals and 1 academic medical center.Patients:Hospitalized patients who received ≥1 dose of a systemic antimicrobial agent.Methods: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.Results: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.Conclusion: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.

Background Data on occupational acquisition of COVID-19 in healthcare settings are limited. Contact tracing efforts are high resource investments. Methods Duke Health developed robust COVID-19 contact tracing methods as part of a comprehensive prevention program. We prospectively collected data on HCW exposures and monitored for development of symptomatic (SYX) and asymptomatic (ASYX) COVID-19 infection after documented high-, medium, and low-risk exposures. HCWs were required to self-report exposures or were identified through contact tracing as potentially exposed to COVID-19 positive HCWs, patients or visitors. Contact tracers interviewed exposed HCWs and assessed the risk of exposure as high-, medium-, or low-risk based on CDC guidance (Table 1). Testing was recommended at 6 days after high- or medium-risk exposures and was provided upon HCW request following low-risk exposures. Our vaccination campaign began in 12/2020. Results 12,916 HCWs registered in the contact tracing database. From March 2020-May 2021, we identified 6,606 occupational exposures (0.51 exposures/HCW). The highest incidence of workplace exposures per number of HCWs in each job category was among respiratory therapists (RT) (0.95 exposures/RT), nursing assistants (NA) (0.79 exposures/NA), and physicians (0.64 exposures/physician). The most common exposure risk level was medium (51.4%), followed by low (35.5%), and then high (13.1%). A total of 260 (2%) HCW had positive tests/conversions; 28 (10.8%) were ASYX at the time of testing. High-risk exposures had a significantly greater number of post-exposure infections compared to medium- and low-risk exposures (12.5% vs. 4.2%, vs. 0.4%; p < 0.001). The rate of SYX infection following exposure to a fellow HCW (179/3,198; 5.6%) was higher than that following exposure to a patient (81/3,408; 2.4%; p< 0.001). Conclusion Conversion following exposure to COVID-19 in the healthcare setting with appropriate protective equipment was low. Incomplete testing of all exposed individuals was a limitation and our data may under-estimate the true conversion rate. Our findings support our local practice of not quarantining HCWs following non-household exposures. Limiting contact tracing to only high or medium risk exposures may best utilize limited personnel resources. Disclosures Rebekah W. Moehring, MD, MPH, UpToDate, Inc. (Other Financial or Material Support, Author Royalties)

Background We aimed to describe SARS-CoV-2 (COVID-19) infections among employees in a large, academic institution. Table 1. COVID-19 Attribution Definitions Table 2. Description of 3,140 COVID 19 Infections in Employees from 3/2020 to 4/2021 Methods We prospectively tracked and traced COVID-19 infections among employees across our health system and university. Each employee with a confirmed positive test and 3 presumed positive cases were interviewed with a standard contact tracing template that included descriptive variables such as high-risk behaviors and contacts, dates worked while infectious, and initial symptoms. Using this information, the most likely location of infection acquisition was adjudicated (Table 1). We compared behavior frequency between community and unknown, likely community and community and unknown cases using descriptive statistics. Table 3. Risk Factors for Community, Likely Community, and Unknown Cases Number of SARS-CoV-2 cases among employees between 3/2020 and 4/2021 by month and stratified according to clinical employee working in the healthcare system, non-clinical employee employed by the healthcare system, and university employee Results From 3/2020 to 4/2021 we identified 3,140 COVID-19 infections in 3,119 employees out of a total of 34,562 employees (9.0%) (Figure 1). Of those 3,119 employees 1,685 (54.0%) were clinical employees working in the health system, 916 (29.4%) were non-clinical employees working in the health system, and 518 (16.6%) were university employees. Descriptive characteristics for the COVID-19 infections and adjudications are outlined in Table 2. Severe disease among employees was significantly less frequent compared to patients in the health system (15.3% vs 2.2%, p< 0.01). The frequency of travel within 14 days, masked gatherings and unmasked gatherings/activities was not significantly different between the community and unknown, likely community groups or the community and unknown groups (Table 3). Conclusion The majority of COVID-19 infections were linked to acquisition in the community, and few were attributed to workplace exposures. Employees with unknown sources of COVID-19 participated in higher-risk activities at approximately the same frequency as employees with community sources of COVID-19. The most frequently reported initial symptoms were mild and non-specific and rarely included fever. Despite a comprehensive testing and benefit program, a large proportion of COVID-positive employees worked with symptoms, highlighting ongoing challenges with presenteeism in healthcare. Disclosures Rebekah W. Moehring, MD, MPH, UpToDate, Inc. (Other Financial or Material Support, Author Royalties)

Background The COVID-19 pandemic placed a strain on inpatient clinical and hospital programs due to increased patient volume and rapidly evolving data on best COVID-19 management strategies. However, the impact of the pandemic on ASPs has not been well described. Methods We performed a cross-sectional electronic survey of stewardship pharmacy and physician leaders in 37 hospitals within the Duke Antimicrobial Stewardship Outreach Network (DASON) (community) and Duke/UNC Health systems (academic) in April-May 2021. The survey included 60 questions related to staffing changes, use of COVID-targeted therapies, related restrictions, and medication shortages. Results Twenty-seven facilities responded (response rate of 73%). Pharmacy personnel was reduced in 17 (63%) facilities by an average of 16%. Impacted pharmacy personnel included the stewardship lead in 15/17 (88.2%) hospitals. Converting to remote work was rare and only reported in academic institutions (n=2, 7.4%). ASP personnel were reassigned to non-stewardship duties in 12 (44%) hospitals with only half returning to routine ASP work as of May 2021. Respondents estimated that 62% of routine ASP activities were diverted during the time of the pandemic. Non-traditional, pandemic-related ASP activities included managing multiple drug shortages, of which ventilator support medications (91%) were most common affecting patient care at 52% of facilities. Steroid and hydroxychloroquine shortages were less frequent (44% and 22%, respectively). Despite staff reductions, pharmacists often served as primary contact for remdesivir approvals either using a criteria-based checklist at dispensing or as part of a dedicated phone approval team (Figure). Most (77%) hospitals used a criteria-based pharmacist review strategy after remdesivir FDA approval. Restriction processes for other COVID-19 therapies such as tocilizumab, hydroxychloroquine, and ivermectin were reported in 64% of hospitals. Remdesivir Allocation Strategy Proportion of facilities implementing specific remdesivir allocation strategies from the time of the first US Food and Drug Administration (FDA) Emergency Use Authorization (EUA) through FDA approval Conclusion Pandemic response diverted routine ASP work and has not yet returned to baseline. Despite the reduction in pharmacy personnel due to the pandemic, the ASP pharmacy lead took on a novel and critical stewardship role throughout the pandemic exemplified by their involvement in novel treatment allocation for COVID patients. Disclosures Melissa D. Johnson, PharmD, MHS, Astellas (Consultant, Grant/Research Support)Charles River Laboratories (Grant/Research Support)Cidara (Consultant)Merck & Co (Consultant, Research Grant or Support)Paratek (Consultant)Pfizer (Consultant)Scynexis (Scientific Research Study Investigator)Theratechnologies (Consultant)UpToDate (Other Financial or Material Support, Author Royalties) Sonali D. Advani, MBBS, MPH, Nothing to disclose Rebekah W. Moehring, MD, MPH, UpToDate, Inc. (Other Financial or Material Support, Author Royalties)

Background The treatment of asymptomatic bacteriuria (ASB) does not improve clinical outcomes in most patients and may be associated with an increased risk of adverse events such as Clostridioides difficile infection. A best practice alert (BPA) was created to identify patients with possible ASB for antimicrobial stewardship (AS) review. We aimed to determine whether automated identification of ASB improved the timing of stewardship intervention. Methods An electronic health record BPA message to inpatient AS pharmacists was activated on 01/19/2021. The BPA identified inpatients with a new antibiotic order with an associated genitourinary indication and a preceding urinalysis with 0 to 5 WBC/hpf. BPAs were reviewed by an AS pharmacist during weekdays and normal business hours. We retrospectively evaluated the impact of the BPA on time from order to stewardship intervention between a cohort of pre-BPA (01/2020 to 12/2020) and post-BPA (01/20/2021 to 04/10/2021) patients. Included patients met the BPA criteria and had an AS intervention within 7 days of the antibiotic order. We specified interventions that were UTI-related. The median time from antibiotic order entry to any AS intervention was compared pre- to post-BPA using the Mann Whitney U test. Rates of UTI-related interventions were compared with Fisher’s Exact test. Results 327 antibiotic orders met BPA criteria and were analyzed: 245 and 82 in the pre- and post-BPA group, respectively. Groups had similar baseline characteristics (Table 1). A total of 33 (27 UTI-related) pre-BPA group and 24 (17 UTI-related) post-BPA group interventions were documented by the AS team. The median time to any intervention was 28 hours (IQR 18-64.5) in the pre-BPA group compared to 13.5 hours (IQR 3.5-28.75) in the post-BPA group (p = 0.03, Figure). The pre-BPA group had a lower rate of UTI-related interventions compared to the post-BPA group (11.0% vs 20.7%, p = .04). Conclusion Automated identification of antibiotics targeting UTI with urinalysis showing absence of pyuria reduced the time to stewardship intervention and increased rate of UTI-specific interventions. The use of clinical decision support may aid in efficiency of AS review and syndrome-targeted AS impact. Disclosures Rebekah W. Moehring, MD, MPH, UpToDate, Inc. (Other Financial or Material Support, Author Royalties)

Importance Penicillin allergies are frequently mislabeled, which may contribute to use of less-preferred alternative antibiotics. Objective To evaluate a pharmacist-led allergy assessment program’s association with antimicrobial use and clinical outcomes. Design, Setting, and Participants A pharmacist-led allergy assessment program was launched in 2 phases (June 1, 2015, and November 2, 2016) at a single-center tertiary referral hospital. The longitudinal cross-sectional study included all study period adult admissions; hospitalwide outcomes were assessed by segmented regression. Individual outcomes were assessed within an embedded propensity score–matched case-control study of inpatients undergoing comprehensive allergy assessment following self-report of penicillin allergy. Analysis occurred from March 1, 2020, to February 29, 2020. Exposures The longitudinal study analyzed hospital-level outcomes over 3 periods: preintervention (15 months), phase 1 (structured allergy history alone, 16 months), and phase 2 (comprehensive assessment including penicillin skin testing, 52 months). The case-control study defined cases as individuals undergoing comprehensive allergy assessment. Main Outcomes and Measures Hospital-level outcomes included antibiotic days of therapy per 1000 patient-days and hospital-acquiredClostridioides difficileinfection (CDI) incidence per 10 000 patient-days. Individual outcomes included antibiotic selection, overall survival, and CDI-free survival. Results Longitudinal analysis spanned 2014-2020 (median admissions, 46 416 per year; interquartile range [IQR], 46 001-50 091 per year). Hospitalwide, allergy histories were temporally associated with decreased use of nonpenicillin alternative antibiotics (rate ratio, 0.87; 95% CI, 0.79-0.97) and high-CDI-risk antibiotics (rate ratio, 0.91; 95% CI, 0.85-0.98). Penicillin skin testing was temporally associated with lower hospital-acquired CDI rates (rate ratio, 0.61; 95% CI, 0.43-0.86). The embedded case-control study included 272 cases and 819 controls. Median age was 63 years (interquartile range, 51-73 years), 553 (50.7%) patients were women, and 229 (21.0%) patients were Black. Allergy-assessed patients were less likely to receive high-CDI-risk antibiotics at discharge (odds ratio, 0.66; 95% CI, 0.44-0.98). Estimated reductions in mortality (hazard ratio, 0.77; 95% CI, 0.55-1.07) and hospital-acquired CDI risk (hazard ratio, 0.53; 95% CI, 0.18-1.55) were not statistically significant. Conclusions and Relevance Pharmacist-led allergy assessments may be associated with reduced high-CDI-risk antibiotic use at both hospitalwide and individual levels. Although individual reductions in mortality and CDI risk did not achieve significance, divergence of survival curves suggest longer-term benefits of allergy delabeling warrant future study.

Background Urinary tract infections (UTIs) are the most common outpatient indication for antibiotics and an important target for antimicrobial stewardship (AS) activities. With The Joint Commission standards now requiring outpatient AS, data supporting effective strategies are needed. Methods We conducted a 2-phase, prospective, quasi-experimental study to estimate the effect of an outpatient AS intervention on guideline-concordant antibiotic prescribing in a primary care (PC) clinic and an urgent care (UC) clinic between August 2017 and July 2019. Phase 1 of the intervention included the development of clinic-specific antibiograms and UTI diagnosis and treatment guidelines, presented during educational sessions with clinic providers. Phase 2, consisting of routine clinic- and provider-specific feedback, began ~12 months after the initial education. The primary outcome was percentage of encounters with first- or second-line antibiotics prescribed according to clinic-specific guidelines and was assessed using an interrupted time series approach. Results Data were collected on 4724 distinct patients seen during 6318 UTI encounters. The percentage of guideline-concordant prescribing increased by 22% (95% CI, 12% to 32%) after Phase 1 education, but decreased by 0.5% every 2 weeks afterwards (95% CI, –0.9% to 0%). Following routine data feedback in Phase 2, guideline concordance stabilized, and significant further decline was not seen (–0.6%; 95% CI, –1.6% to 0.4%). This shift in prescribing patterns resulted in a 52% decrease in fluoroquinolone use. Conclusions Clinicians increased guideline-concordant prescribing, reduced UTI diagnoses, and limited use of high-collateral damage agents following this outpatient AS intervention. Routine data feedback was effective to maintain the response to the initial education.

Key Points Question Do variable sets of varying complexity derived from the electronic health record accurately identify inpatient antimicrobial exposure? Findings Machine learning models developed in this cohort study identified encounter-level antimicrobial exposures with high fidelity, with a mean area under the curve of 0.85. Meaning Encounter-level information from the electronic health record accurately identified antibiotic exposures and may be useful in future risk-adjusted comparisons for hospital antimicrobial stewardship assessments., Importance Comparisons of antimicrobial use among hospitals are difficult to interpret owing to variations in patient case mix. Risk-adjustment strategies incorporating larger numbers of variables haves been proposed as a method to improve comparisons for antimicrobial stewardship assessments. Objective To evaluate whether variables of varying complexity and feasibility of measurement, derived retrospectively from the electronic health records, accurately identify inpatient antimicrobial use. Design, Setting, and Participants Retrospective cohort study, using a 2-stage random forests machine learning modeling analysis of electronic health record data. Data were split into training and testing sets to measure model performance using area under the curve and absolute error. All adult and pediatric inpatient encounters from October 1, 2015, to September 30, 2017, at 2 community hospitals and 1 academic medical center in the Duke University Health System were analyzed. A total of 204 candidate variables were categorized into 4 tiers based on feasibility of measurement from the electronic health records. Main Outcomes and Measures Antimicrobial exposure was measured at the encounter level in 2 ways: binary (ever or never) and number of days of therapy. Analyses were stratified by age (pediatric or adult), unit type, and antibiotic group. Results The data set included 170 294 encounters and 204 candidate variables from 3 hospitals during the 3-year study period. Antimicrobial exposure occurred in 80 190 encounters (47%); 64 998 (38%) received 1 to 6 days of therapy, and 15 192 (9%) received 7 or more days of therapy. Two-stage models identified antimicrobial use with high fidelity (mean area under the curve, 0.85; mean absolute error, 1.0 days of therapy). Addition of more complex variables increased accuracy, with largest improvements occurring with inclusion of diagnosis information. Accuracy varied based on location and antibiotic group. Models underestimated the number of days of therapy of encounters with long lengths of stay. Conclusions and Relevance Models using variables derived from electronic health records identified antimicrobial exposure accurately. Future risk-adjustment strategies incorporating encounter-level information may make comparisons of antimicrobial use more meaningful for hospital antimicrobial stewardship assessments., This cohort study evaluates whether variables of varying complexity and feasibility of measurement, derived retrospectively from the electronic health records, accurately identify inpatient antimicrobial exposure

Background: Early administration of antibiotics in sepsis is associated with improved patient outcomes, but safe and generalizable approaches to de-escalate or discontinue antibiotics after suspected sepsis events are unknown. Methods: We used a modified Delphi approach to identify safety criteria for an opt-out protocol to guide de-escalation or discontinuation of antibiotic therapy after 72 hours in non-ICU patients with suspected sepsis. An expert panel with expertise in antimicrobial stewardship and hospital epidemiology rated 48 unique criteria across 3 electronic survey rating tools. Criteria were rated primarily based on their impact on patient safety and feasibility for extraction from electronic health record review. The 48 unique criteria were rated by anonymous electronic survey tools, and the results were fed back to the expert panel participants. Consensus was achieved to either retain or remove each criterion. Results: After 3 rounds, 22 unique criteria remained as part of the opt-out safety checklist. These criteria included high-risk comorbidities, signs of severe illness, lack of cultures during sepsis work-up or antibiotic use prior to blood cultures, or ongoing signs and symptoms of infection. Conclusions: The modified Delphi approach is a useful method to achieve expert-level consensus in the absence of evidence suifficient to provide validated guidance. The Delphi approach allowed for flexibility in development of an opt-out trial protocol for sepsis antibiotic de-escalation. The utility of this protocol should be evaluated in a randomized controlled trial.

Group Name: Duke Center for Antimicrobial Stewardship and Infection PreventionBackground: Blood cultures are an essential diagnostic test, but over- and underutilization may cause harm. Methods: We analyzed blood culture utilization at 6 hospitals in the southeastern United States including 1 academic hospital (A) and 5 community hospitals (B–F) from May 2019 to April 2020. We measured blood culture utilization rate (BCUR) per 1,000 patient days and blood cultures per encounter. We counted blood cultures by laboratory accession number and measured utilization per 1,000 patient days and encounter. A likely contaminant was defined as 1 of 2 blood cultures collected in the same calendar day positive for a common skin commensal (CSC), as defined by the NHSN, and not identified from subsequent cultures. A likely pathogen was defined as a culture with a pathogen not on the CSC list or a CSC not meeting the contaminant definition. Hospital-level BCUR included samples for culture collected in the emergency department (ED) and inpatient areas divided by inpatient days. Results: The analysis included 117,897 blood cultures and 662,723 patient days with a median BCUR of 209.7 per hospital and median blood culture per encounter of 2 (Table 1). One community hospital (C) demonstrated a substantially higher BCUR than others. Cultures were frequently collected in the ED (54%; range, 36%–78%); most encounters with cultures in the ED were subsequently admitted to an inpatient unit (84%; range, 73%–89%). Higher BCURs were observed in intensive care and oncology units. The proportion of first blood cultures drawn after initiation of antibiotics was 6% (range, 3%–9%. Mondays had higher BCURs than other days of the week (Figure 1). The average BCUR by month was 176.1 (range, 164.3–181.4) with no seasonal patterns observed. Overall, 7.7% (range, 4.5%–9.1%) of blood cultures identified a likely pathogen and 2.1% (range, 1.3%–3.2%) identified a likely contaminant. The 3 hospitals with BCURs >200 also had contaminant rates >2% and >60% ED cultures. Conclusions: Blood culture utilization varied by hospital, unit, and day of the week. We observed higher rates of likely contaminants among hospitals with higher BCURs and ED culture rates. Comparisons may assist in identifying opportunities to optimize practice around blood-culture ordering and collection.Funding: NoDisclosures: None

Respiratory tract infections (RTIs) drive many outpatient encounters and, despite being predominantly viral, are associated with high rates of antibiotic prescriptions. With rising antibacterial resistance, optimization of prescribing of antibiotics in outpatients with RTIs is a critical need. Fortunately, this challenge arises at a time of increasing availability of novel RTI diagnostics to help discern which patients have bacterial infections warranting treatment. Effective implementation of antibiotic stewardship is needed, but optimal approaches for ambulatory settings are unknown. Future research needs are reviewed in this summary of a research summit convened by the Infectious Diseases Society of America in the fall of 2019.

Background Antimicrobial stewardship programs (ASPs) promote the principle of de-escalation: moving from broad- to narrow-spectrum agents and stopping antibiotics when no longer indicated. A standard, objective definition of de-escalation applied to electronic data could be useful for ASP assessments. Methods We derived an electronic definition of antibiotic de-escalation and performed a retrospective study among 5 hospitals. Antibiotics were ranked into 4 categories: narrow-spectrum, broad-spectrum, extended-spectrum, and agents targeted for protection. Eligible adult patients were cared for on inpatient units, had antibiotic therapy for at least 2 days, and were hospitalized for at least 3 days after starting antibiotics. Number of antibiotics and rank were assessed at 2 time points: day of antibiotic initiation and either day of discharge or day 5. De-escalation was defined as reduction in either the number of antibiotics or rank. Escalation was an increase in either number or rank. Unchanged was either no change or discordant directions of change. We summarized outcomes among hospitals, units, and diagnoses. Results Among 39 226 eligible admissions, de-escalation occurred in 14 138 (36%), escalation in 5129 (13%), and antibiotics were unchanged in 19 959 (51%). De-escalation varied among hospitals (median, 37%; range, 31–39%, P Conclusions Our electronic de-escalation metric demonstrated variation among hospitals, units, and diagnoses. This metric may be useful for assessing stewardship opportunities and impact.

Summary Background The hospital environment is a source of pathogen transmission. The effect of enhanced disinfection strategies on the hospital-wide incidence of infection has not been investigated in a multicentre, randomised controlled trial. We aimed to assess the effectiveness of four disinfection strategies on hospital-wide incidence of multidrug-resistant organisms and Clostridium difficile in the Benefits of Enhanced Terminal Room (BETR) Disinfection study. Methods We did a prespecified secondary analysis of the results from the BETR Disinfection study, a pragmatic, multicentre, crossover cluster-randomised trial that assessed four different strategies for terminal room disinfection in nine hospitals in the southeastern USA. Rooms from which a patient with a specific infection or colonisation (due to the target organisms C difficile , meticillin-resistant Staphylococcus aureus , vancomycin-resistant enterococci (VRE), or multidrug-resistant Acinetobacter spp ) was discharged were terminally disinfected with one of four strategies: standard disinfection (quaternary ammonium disinfectant, except for C difficile , for which 10% hypochlorite [bleach] was used; reference); standard disinfection and disinfecting ultraviolet light (UV-C), except for C difficile , for which bleach and UV-C was used (UV strategy); 10% hypochlorite (bleach strategy); and bleach and UV-C (bleach and UV strategy). We randomly assigned the sequence of strategies for each hospital (1:1:1:1), and each strategy was used for 7 months, including a 1-month wash-in period and 6 months of data collection. The prespecified secondary outcomes were hospital-wide, hospital-acquired incidence of all target organisms (calculated as number of patients with hospital-acquired infection with a target organism per 10 000 patient days), and hospital-wide, hospital-acquired incidence of each target organism separately. BETR Disinfection is registered with ClinicalTrials.gov, number NCT01579370. Findings Between April, 2012, and July, 2014, there were 271 740 unique patients with 375 918 admissions. 314 610 admissions met all inclusion criteria (n=73 071 in the reference study period, n=81 621 in the UV study period, n=78 760 in the bleach study period, and n=81 158 in the bleach and UV study period). 2681 incidenct cases of hospital-acquired infection or colonisation occurred during the study. There was no significant difference in the hospital-wide risk of target organism acquisition between standard disinfection and the three enhanced terminal disinfection strategies for all target multidrug-resistant organisms (UV study period relative risk [RR] 0·89, 95% CI 0·79–1·00; p=0·052; bleach study period 0·92, 0·79–1·08; p=0·32; bleach and UV study period 0·99, 0·89–1·11; p=0·89). The decrease in risk in the UV study period was driven by decreases in risk of acquisition of C difficile (RR 0·89, 95% CI 0·80–0·99; p=0·031) and VRE (0·56, 0·31–0·996; p=0·048). Interpretation Enhanced terminal room disinfection with UV in a targeted subset of high-risk rooms led to a decrease in hospital-wide incidence of C difficile and VRE. Enhanced disinfection overcomes limitations of standard disinfection strategies and is a potential strategy to reduce the risk of acquisition of multidrug-resistant organisms and C difficile . Funding US Centers for Disease Control and Prevention.

OBJECTIVETo determine the feasibility and value of developing a regional antibiogram for community hospitals.DESIGNMulticenter retrospective analysis of antibiograms.SETTING AND PARTICIPANTSA total of 20 community hospitals in central and eastern North Carolina and south central Virginia participated in this study.METHODSWe combined antibiogram data from participating hospitals for 13 clinically relevant gram-negative pathogen–antibiotic combinations. From this combined antibiogram, we developed a regional antibiogram based on the mean susceptibilities of the combined data.RESULTSWe combined a total of 69,778 bacterial isolates across 13 clinically relevant gram-negative pathogen–antibiotic combinations (median for each combination, 1100; range, 174–27,428). Across all pathogen–antibiotic combinations, 69% of local susceptibility rates fell within 1 SD of the regional mean susceptibility rate, and 97% of local susceptibilities fell within 2 SD of the regional mean susceptibility rate. No individual hospital had >1 pathogen–antibiotic combination with a local susceptibility rate >2 SD of the regional mean susceptibility rate. All hospitals’ local susceptibility rates were within 2 SD of the regional mean susceptibility rate for low-prevalence pathogens (CONCLUSIONSSmall community hospitals frequently cannot develop an accurate antibiogram due to a paucity of local data. A regional antibiogram is likely to provide clinically useful information to community hospitals for low-prevalence pathogens.Infect Control Hosp Epidemiol 2018;39:718–722

Patient days and days present were compared to directly measured person time to quantify how choice of different denominator metrics may affect antimicrobial use rates. Overall, days present were approximately one-third higher than patient days. This difference varied among hospitals and units and was influenced by short length of stay.Infect Control Hosp Epidemiol 2018;39:612–615

Background Antibiotic stewardship programs improve clinical outcomes and patient safety and help combat antibiotic resistance. Specific guidance on resources needed to structure stewardship programs is lacking. This manuscript describes results of a survey of US stewardship programs and resultant recommendations regarding potential staffing structures in the acute care setting. Methods A cross-sectional survey of members of 3 infectious diseases subspecialty societies actively involved in antibiotic stewardship was conducted. Survey responses were analyzed with descriptive statistics. Logistic regression models were used to investigate the relationship between stewardship program staffing levels and self-reported effectiveness and to determine which strategies mediate effectiveness. Results Two-hundred forty-four respondents from a variety of acute care settings completed the survey. Prior authorization for select antibiotics, antibiotic reviews with prospective audit and feedback, and guideline development were common strategies. Eighty-five percent of surveyed programs demonstrated effectiveness in at least 1 outcome in the prior 2 years. Each 0.50 increase in pharmacist and physician full-time equivalent (FTE) support predicted a 1.48-fold increase in the odds of demonstrating effectiveness. The effect was mediated by the ability to perform prospective audit and feedback. Most programs noted significant barriers to success. Conclusions Based on our survey's results, we propose an FTE-to-bed ratio that can be used as a starting point to guide discussions regarding necessary resources for antibiotic stewardship programs with executive leadership. Prospective audit and feedback should be the cornerstone of stewardship programs, and both physician leadership and pharmacists with expertise in stewardship are crucial for success.

OBJECTIVETo summarize and discuss logistic and administrative challenges we encountered during the Benefits of Enhanced Terminal Room (BETR) Disinfection Study and lessons learned that are pertinent to future utilization of ultraviolet (UV) disinfection devices in other hospitalsDESIGNMulticenter cluster randomized trialSETTING AND PARTICIPANTSNine hospitals in the southeastern United StatesMETHODSAll participating hospitals developed systems to implement 4 different strategies for terminal room disinfection. We measured compliance with disinfection strategy, barriers to implementation, and perceptions from nurse managers and environmental services (EVS) supervisors throughout the 28-month trial.RESULTSImplementation of enhanced terminal disinfection with UV disinfection devices provides unique challenges, including time pressures from bed control personnel, efficient room identification, negative perceptions from nurse managers, and discharge volume. In the course of the BETR Disinfection Study, we utilized several strategies to overcome these barriers: (1) establishing safety as the priority; (2) improving communication between EVS, bed control, and hospital administration; (3) ensuring availability of necessary resources; and (4) tracking and providing feedback on compliance. Using these strategies, we deployed ultraviolet (UV) disinfection devices in 16,220 (88%) of 18,411 eligible rooms during our trial (median per hospital, 89%; IQR, 86%–92%).CONCLUSIONSImplementation of enhanced terminal room disinfection strategies using UV devices requires recognition and mitigation of 2 key barriers: (1) timely and accurate identification of rooms that would benefit from enhanced terminal disinfection and (2) overcoming time constraints to allow EVS cleaning staff sufficient time to properly employ enhanced terminal disinfection methods.TRIAL REGISTRATIONClinical trials identifier: NCT01579370Infect Control Hosp Epidemiol 2018;39:157–163

Background Broad spectrum antibiotics are often prescribed to patients presenting to the emergency department (ED) for evaluation of urinary tract infection and pyelonephritis (UTI). We evaluated the effect of a target-specific antibiogram, education, and feedback on UTI diagnosis and antibiotic prescribing in this setting. Methods We created a urine-specific antibiogram from patients seen and treated without admission at three ED locations (one academic and two community hospitals). We then provided a treatment algorithm and supplemental educational content to ED providers in November 2019. Educational content highlighted appropriate diagnosis, antibiotic selection, and treatment duration for UTI. Adult encounters with appropriate ICD-9/10 codes within twelve months prior to content delivery comprised the preintervention cohort. The postintervention cohort consisted of adult visits following educational intervention until April 17, 2020. During the postintervention phase (November 2019 to April 2020), summary data regarding UTI diagnoses and guideline-concordant prescriptions were fed back routinely to ED providers through email. Guideline-concordant prescriptions were defined as those adhering to first or second-line therapy in the treatment algorithm. The proportion of prescriptions meeting this definition fulfilled the primary outcome. An interrupted time series analysis measured changes in guideline concordance. Results Data from 6,713 distinct encounters were analyzed across the three sites. While guideline concordant prescribing increased following intervention at all locations (30.9% to 38.8%, 48.1% to 49.1%, and 48.2% to 59.6%), these increases were not statistically significant (Figures 1, 2, and 3). The proportion of all ED encounters with a UTI diagnosis did not differ following the intervention. Interestingly, guideline concordance was greater in the academic ED, compared to the community hospitals. Conclusion Although guideline concordant prescribing for UTI increased in all three ED settings with education and email correspondence feedback, these results were not statistically significant. A variety of methods may be required to realize improved antibiotic prescribing across a diverse group of clinicians. Disclosures Rebekah W. Moehring, MD, MPH, Agency for Healthcare Quality and Research (Grant/Research Support)Centers for Disease Control and Prevention (Grant/Research Support)

Background: Evidence-based hospital antimicrobial stewardship interventions, such as postprescription review with feedback, prior authorization, and handshake stewardship, involve communication between stewards and frontline prescribers. Hierarchy, asymmetric responsibility, prescribing etiquette, and autonomy can obstruct high-quality communication in stewardship. Little is known about the strategies that stewards use to overcome these barriers. The objective of this study was to identify how stewards navigate communication challenges when interacting with prescribers. Methods: We conducted semistructured interviews with antimicrobial stewards recruited from hospitals across the United States. Interviews were audio recorded, transcribed, and analyzed using a flexible coding approach and the framework method. Social identity theory and role theory were used to interpret framework matrices. Results: Interviews were conducted with 58 antimicrobial stewards (25 physicians and 33 pharmacists) from 10 hospitals (4 academic medical centers, 4 community hospitals, and 2 children’s hospitals). Respondents who felt empowered in their interactions with prescribers explicitly adopted a social identity that conceptualized stewards and prescribers as being on the “same team” with shared goals (in-group orientation). Drawing on the meaning conferred via this social role identity, respondents engaged in communication strategies to build and maintain common bonds with prescribers. These strategies included moderating language to minimize defensive recommendations when delivering stewardship recommendations, aligning the goals of stewardship with the goals of the clinical team, communicating with prescribers about things other than stewardship, compromising for the sake of future interactions, and engaging in strategic face-to-face interaction. Respondents who felt less empowered in their interactions thought of themselves as outsiders to the clinical team and experienced a heightened sense of “us versus them” mentality with the perception that stewards primarily serve a gate-keeping function (ie, outgroup orientation). These respondents expressed deference to hierarchy, a reluctance to engage in face-to-face interaction, a feeling of cynicism about the impact of stewardship, and a sense of low professional accomplishment within the role. Respondents who exhibited an in-group orientation were more likely than those who did not to describe the positive impact of stewardship mentors or colleagues on their social role identity. Conclusions: The way antimicrobial stewards perceive their role and identity within the social context of their healthcare organization influences how they approach communication with prescribers. Social role identity in stewardship is shaped by the influence of mentors and colleagues, indicating the importance of supportive relationships for the development of steward skill and confidence.Funding: NoDisclosures: None