Rapid identification technologies and phenotypic methods, new therapeutic strategies, and novel treatment paradigms have evolved in an attempt to improve treatment outcomes for VAP; however, clinical data supporting alternative treatment strategies and adjunctive therapies remain sparse. Importantly, new classes of antimicrobials, novel virulence factor inhibitors, and beta-lactam/beta-lactamase inhibitor combinations are currently in development. Conscientious stewardship of new and emerging therapeutic agents will be needed to ensure they remain effective well into the future.

This study sought to characterize the impact of 3 types of variation on the Standardized Antimicrobial Administration Ratio (SAAR) utilizing local National Healthcare Safety Network (NHSN) data. SAAR and antimicrobial days per 1,000 days present (AD/1000DP) were compiled monthly for Northwestern Memorial Hospital from 2014 to 2016. Antimicrobial consumption was aggregated into agent categories (via NHSN criteria). Month-to-month changes in SAAR and AD/1000DP were evaluated. Azithromycin and oseltamivir AD/1000DP from 2012 through 2017 were explored for seasonal variation. A sensitivity analysis was performed to explore the effect of seasonality and altered consumption at other hypothetical hospitals on the SAAR. Across agent categories for both the intensive care unit (n = 4) and general wards (n = 4), the average matched-month percent change in AD/1000DP was correlated with the corresponding change in SAAR (coefficient of determination of 0.99). The monthly mean ± standard deviation (SD) AD/1000DP was 235 (range, 47.2 to 661.5), and the mean ± SD SAAR was 1.09 ± 0.26 (range, 0.79 to 1.09) across the NHSN agent categories. Five seasons exhibited seasonal variation in AD/1000DP for azithromycin with a mean percent change of 26.76% (range, 22.27 to 30.69). Eight seasons exhibited seasonal variation in AD/1000DP for oseltamivir with a mean percent change of 129.1% (range, 32.01 to 352.74). The sensitivity analyses confirm that antimicrobial usage at comparator hospitals does not impact the local SAAR, and seasonal variation of antibiotics has the potential to impact SAAR. Month-to-month changes in the SAAR mirror monthly changes in an institution’s AD/1000DP. Seasonal variation is an important variable for future SAAR consideration, and the variable antibiotic use at peer hospitals is not currently captured by the SAAR methodology.

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BackgroundAn antibiotic timeout (ATO) provides a potential opportunity to improve antibiotic utilization and decrease inappropriate antibiotic prescribing. The CDC and Joint Commission suggest ATO as an antimicrobial stewardship program (ASP) action to support optimal antibiotic use. Unfortunately, little is known about the design and implementation of an ATO. Our primary objective was to describe different ATO models established by hospitals across the United States.MethodsData describing ATO strategies and ASP efforts were collected via a Qualtrics survey as a part of a multicenter study conducted by Vizient™ member hospitals to research the impact of an ATO on various ASP reporting metrics.ResultsSeventy-one hospitals responded to the survey. Twenty (28%) had a formalized ATO. Most institutions utilizing an ATO were community hospitals (60%) and had formalized ASPs (95%). Hospitals with an ATO program trended toward a higher average combined number of ASP physician and pharmacist FTEs than those without a formalized ATO (1.72 vs. 1.2, P = 0.28). Prescribers were responsible for the ATO in 40% of programs (N = 8), 30% were pharmacist-led, and the remainder were multidisciplinary. ATOs were most commonly performed daily (75%) as opposed to on select days of the week and targeted patients receiving antibiotics for 72 hours. Electronic medical record (EMR)-based ATOs (where the EMR prompted the responsible personnel to respond) existed at 14 programs, whereas 4 programs performed an ATO manually through chart review. Forty percent of hospitals conducted ATO on all antibiotics and antifungals; 20% included only antibiotics in their ATO. For the remaining 40% of institutions, only select drugs were included in the ATO.ConclusionMultiple ATO strategies are used in the United States. Most ATOs are electronic-based, performed at 72 hours of antibiotic therapy, inclusive of all antibiotics, and supported by established ASPs. To our knowledge, this is the largest descriptive study on ATO implementation in the United States.Figure 1.Distribution of hospital type and duration of ASPs by the presence of ATOFigure 2.Personnel responsible for conducting ATOsDisclosures All authors: No reported disclosures.

BackgroundAntimicrobial stewardship programs (ASPs) reduce the burden of multidrug-resistant organisms and improve antibiotic prescribing. Concerns about drug-resistant pathogens (DRPs) in community-acquired pneumonia (CAP) lead to over-prescribing of broad-spectrum antibiotics, and ASP interventions to improve CAP prescribing are not well defined. In 2017, our hospital implemented a CAP guideline for patients at low risk for DRPs along with ASP support. The purpose of this study was to evaluate the impact of the guideline with ASP support on CAP-specific antibiotic prescribing.MethodsThis was a pragmatic two-phase quasi-experimental analysis of CAP-specific antibiotic consumption before and after implementation of a CAP guideline evaluated according to each phase of implementation. The guideline provided Gram-positive and Gram-negative risk factors and guidance on oral fluoroquinolone (FQs) alternatives. ASP interventions were implemented in two phases: (A) prospective audit and feedback in July 2016 and (B) publication of guideline with education in March 2017. Impact of each intervention was evaluated by interrupted time series segmented-regression analysis. Univariate statistics were calculated using EpiInfo 7. Least-squares segmented regressions were completed in Microsoft Excel.ResultsCAP-specific antibiotic administrations were 782 over the entire study period, with 764, 771, and 928 administrations observed before phase A, after A, and after B, respectively. Macrolide consumption increased after the guideline (P = 0.029). We observed a significant step change decrease in FQ consumption was observed after phase A) (P = 0.039) and a positive upward trend in oral alternatives agents after phase B (P = 0.090), as shown in the figure. Consumption of broad Gram-negative agents and vancomycin/linezolid were not significantly different after the guideline.ConclusionImplementation of a CAP guideline with patient-specific and DRP risk factors was associated with significant changes in CAP-specific prescribing. Changes in prescribing were temporally associated with ASP interventions. Additional studies into the impact of this guideline on correct classification of Gram-negative resistance and clinical outcomes are needed. Disclosures D. Martin, GlaxoSmithKline: Independent Contractor, SalarySyneos Health: Employee, Salary. R. G. Wunderink, Achaogen: Consultant, Consulting fee. Arsanis: Consultant, Consulting fee. Bayer: Consultant, Consulting fee. GlaxoSmithKline: Consultant, Consulting fee. KBP Biosciences: Consultant, Consulting fee. Meiji-Seiko: Consultant, Consulting fee. Merck: Consultant, Consulting fee. Nabriva: Consultant, Consulting fee. Polyphor: Consultant, Consulting fee. Roche/Genetech: Consultant, Consulting fee. Shionogi: Consultant, Consulting fee. The Medicines Company: Consultant, Consulting fee. Accelerate Diagnostics: Consultant, Consulting fee. Curetis: Consultant, Consulting fee. bioMerieux: Consultant, Consulting fee. M. H. Scheetz, Merck & Co., Inc.: Grant Investigator, Grant recipie...

Background Pseudomonas aeruginosa (PsA) is an infrequent pathogen associated with poor outcomes in community-acquired pneumonia (CAP). Identifying patients at high and low-risk for PsA in CAP is necessary to reduce inappropriate and overly broad-spectrum antibiotic use. We evaluated the distribution of risk-factors in hospitalized CAP patients with and without PsA infection.MethodsDesign: retrospective, single-center, case–control study. Inclusion: hospitalized CAP patients admitted to the general medicine wards between January 1, 2014 and May 29, 2018. Exclusion: cystic fibrosis, ≥ 3 admissions within 30 days, CAP requiring ICU admission, and death within 48 hours of admission. Case patients had PsA in respiratory or blood cultures during the index CAP admission. Controls were randomly selected targeting a 3:1 ratio. Comorbidities, pneumonia severity index, and m-APACHE II were assessed. Gram-negative risk factors defined by Shindo et al. 2013 (PMID: 23855620) and validated by Kobayashi et al. (2018; PMID: 30349327) were scored for each patient. Stepwise logistic regression was used to identify covariates that distinguished cases from controls at a P < 0.2; these were then used to generate propensity weights (i.e., inverse-probability conditioned on covariates). Unadjusted and adjusted odds ratios for case status were estimated using logistic regression according to: the total number of risk factors present and threshold values, respectively. All analyses were conducted using IC Stata (v.14.2).Results54 cases and 152 controls were included. The distribution of the patient-specific sum of risk factors for PsA is shown in Figure 1. The univariate OR for case status was 4.29 (95% CI:1.55–11.9) at n = 3 risk factors, which was similar after propensity weight adjustment [aOR = 4.64 (95% CI: 1.32–16.3)]. The univariate OR of case status was 2.98 among patients with ≥ 3 risk factors (95% CI: 1.34–6.62), which was similar after propensity weight adjustment [aOR = 2.8 (95% CI: 1.02–7.72)], and correct classification was 73.8%.ConclusionAt a threshold of ≥ 3 PsA risk factors, cases and controls were well classified, even after adjusting for propensity weights. The impact of patient-specific PsA risk-stratification on CAP outcomes and appropriate antibiotic use should be evaluated. Disclosures All authors: No reported disclosures.

BackgroundThe standardized antimicrobial administration ratio (SAAR) compares each hospital’s observed to predicted days of antimicrobial therapy. However, confusion exists about how hospital-level, seasonal, and hospital-peer-based variations in antibiotic use might impact an institution’s SAAR. We characterized the impact of each of these three types of variation on predicted SAARs utilizing local NHSN data.MethodsAnalysis of antibiotic consumption data from an academic medical center in Chicago, IL was conducted. SAAR and antimicrobial days per 1,000 days present (AD/1,000DP) were compiled in monthly increments from 2014 to 2016.Antimicrobial consumption was aggregated and classified into agent categories according to NHSN criteria. Month-to-month changes in both the SAAR and AD/1,000DP were evaluated. Azithromycin AD/1,000DP from 2012 through 2017 were explored for seasonal variation as defined as >20% increase in AD/1,000DP from each quarter to the overall mean AD/1,000DP for all months. A simulation was performed to explore the potential effect of seasonality on the SAAR. Demographic covariates within the SAAR model were altered while holding constant observed antibiotic use; thus we were able to observe the potential impact of demographics. Finally, a simulation explored the effect of altered consumption at other hospitals on a local institution’s SAAR.ResultsAcross all antibiotic agent categories for both ICU (n = 4) and general wards (n = 4), the average matched-month percent change in AD/1,000DP was highly predicted and correlated with the corresponding change in SAAR (Figure 1, Pearson’s r = 0.99). The monthly mean ± SD AD/1,000DP was 235.0 (range 47.2–661.5), and the mean ± SD SAAR was 1.09 ± 0.26 (range 0.79–1.09) across the NHSN antibiotic agent categories. Five quarters were found to have seasonal variation in AD/1000DP for azithromycin (Figure 2). Simulations demonstrated that changing antimicrobial usage at comparator hospitals does not impact the local SAAR, and seasonal variation may cause fluctuating SAARs.ConclusionMonth-to-month changes in the SAAR mirror monthly changes in an institution’s AD/1,000DP. Seasonal variation can impact the SAAR, and the effect changing peer hospital antibiotic consumption is not currently captured by the SAAR methodology. Disclosures J. Liu, Merck: Grant fund from Merck, Research grant. D. Martin, Syneos Health: Employee, Salary. GlaxoSmithKline: Independent Contractor, Salary. M. H. Scheetz, Merck & Co., Inc.: Grant Investigator, Grant recipient. Bayer: Consultant, Consulting fee.