The impact of hurricane-related flooding on infectious diseases in the US is not well understood. Using geocoded electronic health records for 62,762 veterans living in North Carolina counties impacted by Hurricane Matthew coupled with flood maps, we explore the impact of hurricane and flood exposure on infectious outcomes in outpatient settings and emergency departments as well as antimicrobial prescribing. Declines in outpatient visits and antimicrobial prescribing are observed in weeks 0-2 following the hurricane as compared with the baseline period and the year prior, while increases in antimicrobial prescribing are observed 3+ weeks following the hurricane. Taken together, hurricane and flood exposure appear to have had minor impacts on infectious outcomes in North Carolina veterans, not resulting in large increases in infections or antimicrobial prescribing

Even though antimicrobial days of therapy did not significantly decrease during a period of robust stewardship activities at our center, we detected a significant downward trend in antimicrobial spectrum, as measured by days of antibiotic spectrum coverage (DASC). The DASC metric may help more broadly monitor the effect of stewardship activities.

Fluoroquinolone resistance among Enterobacteriaceae is a notable challenge for appropriate empiric therapy in outpatient settings. We describe the spatial distribution of fluoroquinolone resistance and its chronological change between 2000 and 2017 in the nationwide Veterans’ Health Administration system. We found spatially concentrated increasing prevalence in the 2000s, followed by spatial dispersion in the 2010s.

The optimal metric for outpatient antimicrobial stewardship has not been well defined. The number of antibiotic prescriptions per clinic visit does not account for the therapeutic duration. We found only moderate association between prescription-based metrics and days-supplied–based metrics. Outpatient antibiotic consumption metrics should incorporate the duration of therapy.

To investigate factors that influence antibiotic prescribing decisions, we interviewed 49 antibiotic stewardship champions and stakeholders across 15 hospitals. We conducted thematic analysis and subcoding of decisional factors. We identified 31 factors that influence antibiotic prescribing decisions. These factors may help stewardship programs identify educational targets and design more effective interventions.

Background: Antibiotic use during end-of-life (EOL) care is an increasingly important target for antimicrobial stewardship given the high prevalence of antibiotic use in this setting with limited evidence on safety and effectiveness to guide antibiotic decision making. We estimated antibiotic use during the last 6 months of life for patients under hospice or palliative care, and we identified potential targets (ie time points) during the EOL period when antimicrobial stewardship interventions could be targeted for maximal benefit. Methods: We conducted a retrospective cohort study of nationwide Veterans’ Affairs (VA) patients, 18 years and older who died between January 1, 2014, and December 31, 2019, and who had been hospitalized within 6 months prior to death. Data from the VA’s integrated electronic medical record (EMR) were collected including demographics, comorbid conditions, and duration of inpatient antibiotics administered, along with outpatient antibiotics dispensed. A propensity-score matched-cohort analysis was conducted to compare antibiotic use between patients placed into palliative care or hospice matched to patients not receiving palliative care or hospice care. Repeated measures ANOVA and repeated measures linear regression methods were used to analyze difference in difference (D-I-D) of days of therapy (DOT) between the 2 cohorts. Results: There were 251,822 patients in the cohort, including 23,746 in hospice care, 89,768 in palliative care, and 138,308 without palliative or hospice care. The median days from last discharge to death was 9 days. The most common comorbidities were chronic obstructive pulmonary diseases (50%), malignancy (46%), and diabetes mellitus (43%). Overall, 18,296 (77%) of 23,746 hospice patients, and 71,812 (80%) of 89,768 palliative care patients received at least 1 antibiotic, whereas 95,167 (69%) of 138,308 who were not placed in hospice or did not receive palliative care received antibiotics. In the primary matched cohort analysis that compared patients placed into hospice or palliative care to propensity-score matched controls, entry into palliative care was associated with a 11% absolute increase in antibiotic prescribing, and entry into hospice was associated with a 4% absolute increase during the 7–14 days after entry versus the 7–14 days before entry (Fig. 1). The stratified cohorts had very similar balanced covariates as the overall cohort. Conclusions: In our large cohort study, we observed that patients receiving EOL care had high levels of antibiotic exposure across VA population, particularly on entry to hospice or during admissions when they received palliative care consultation. Future studies are needed to identify the optimal EOL strategies for collaboration between antimicrobial stewardship and palliative care.

Funding: None

Disclosures: None

Background: Antimicrobial stewardship programs (ASPs) are advised to audit antimicrobial consumption as a metric to feedback to clinicians. However, many ASPs lack the tools necessary for appropriate risk adjustment and standardized data collection, which are critical for peer-program benchmarking. We evaluated the impact of the dashboard deployment that displays these metrics and its acceptance among ASP members and antimicrobial prescribers. Materials/methods: We conducted semistructured interviews of ASP stewards and antimicrobial prescribers before and after implementation of a web-based ASP information dashboard (Fig. 1) implemented in the VA Midwest Health Care Network (VISN23). The dashboard provides risk-adjusted benchmarking, longitudinal trends, and analysis of antimicrobial usage patterns at each facility. Risk-adjusted benchmarking was based on an observed-to-expected comparison of antimicrobial days of therapy at each facility, after adjusting for differences in patient case mix and facility-level variables. Respondents were asked to evaluate several aspects of the dashboard, including its ease of use, applicability to ongoing ASP activities, perceived validity and reliability, and advantages compared to other ASP monitoring systems. All interviews were digitally recorded and transcribed verbatim. The analysis was conducted using MaxQDA 2020.4 and the Consolidated Framework for Implementation Research (CFIR) constructs. Results: We completed 4 preimplementation interviews and 11 postimplementation interviews with ASP champions and antimicrobial prescribers from 6 medical centers. We derived 4 key themes from the data that map onto CFIR constructs. These themes were interconnected so that implementation of the dashboard (ie, adapting and adopting) was influenced by respondents’ perception of a facility’s size, patient population, and priority placed on stewardship (ie, structural and cultural context), the availability of dedicated stewardship staff and training needed to implement the dashboard (ie, resources needed), and how the dashboard compared to established stewardship activities (ie, relative advantage). ASP champions and antimicrobial prescribers indicated that dashboard metrics were useful for identifying antimicrobial usage and for comparing metrics among similar facilities. Respondents also specified barriers to acceptance of the risk-adjusted metric, such as disagreement regarding how antimicrobials were grouped by the current NHSN protocol, uncertainty of factors involved in risk adjustments, and difficulty developing a clear interpretation of hospital rankings. Conclusions: Given the limited resources for antimicrobial stewardship personnel, automated, risk-adjusted, antimicrobial-use dashboards provided by ASPs are an attractive method to both facilitate compliance and improve efficiency. To increase the uptake of surveillance systems in antimicrobial stewardship, our study highlights the need for clear descriptions of methods and metrics.

Funding: None

Disclosures: None

Background: Avoiding unnecessary antipseudomonal coverage is 1 of the most common targets for antibiotic stewardship programs (ASPs), but little is known about the magnitude of facility-level variation in antipseudomonal agent utilization. We aimed to describe the variability in the use of antipseudomonal agents across inpatient settings within a nationwide integrated healthcare system. Method: We analyzed the data from a retrospective cohort of patients who were admitted to acute-care hospitals within the VHA system in 2019. We defined antipseudomonal agents as systemic antibiotics with activity against wild-type Pseudomonas aeruginosa, and we evaluated overall and antipseudomonal antibiotic use among 129 hospitals, according to the agents described in the NHSN Antimicrobial Usage and Resistance Module. We calculated each hospital’s overall and antipseudomonal days of therapy (DOT) per 1,000 days present and the proportion of antipseudomonal agent usage among all antibiotics based on DOT at each hospital. Hospital-level variation was assessed by comparing the proportion of total antibiotic consumption accounted for by antipseudomonal agents. Associations between antipseudomonal proportions and overall antibiotic consumption were also assessed. Results: Among 129 VHA hospitals, the median DOT per 1,000 days present for all antibiotics was 434.4 (IQR, 371.9–487.1), and the median antipseudomonal DOT per 1,000 days present was 127.7 (IQR, 99.8–159.6). The median proportion of total antibiotic consumption accounted for by antipseudomonal agents was 30.0% (range, 14.9%–40.7%; IQR, 26.4%–34.4%) (Fig. 1). We detected only a weak correlation between overall antibiotic consumption and antipseudomonal proportion (Pearson correlation coefficient, 0.396), which suggests that hospitals with higher total antibiotic consumption were not necessarily using more antipseudomonal agents. In a stratified analysis, there was more prominent hospital-level variability in surgical specialties than medical specialties (Fig. 2). Conclusions: We detected high hospital-level variability in the consumption and proportion of antipseudomonal antibiotics among an integrated healthcare system. Although it is plausible that these variabilities originated from case-mix differences among hospitals, including differing rates of P. aeruginosa infections, it may also highlight opportunities for reducing antipseudomonal antibiotic utilization, especially among surgical specialties. Further studies are needed to evaluate the contribution of modifiable patient- and facility-level factors to this variability.

Funding: None

Disclosures: None

Background: The COVID-19 pandemic heavily affected healthcare delivery systems in the United States. However, little is known about its impact on overall antimicrobial consumption, especially in outpatient settings. We assessed the impact of the COVID-19 pandemic on antimicrobial consumption in both outpatient and inpatient (acute-care, long-term care, and mental health) settings in the Veterans’ Health Administration (VHA) during the 2 years before and after the start of the pandemic. Methods: We conducted a retrospective study for all patients who received care within the VHA from January 2018 to December 2021. We used antibiotic days as the primary outcome measure (days of therapy for inpatient settings and dispensed days supply for outpatient settings), and we obtained data for antimicrobial consumption from the VHA Corporate Data Warehouse. Antibiotics were categorized into classes by the NHSN protocol and included only systemic agents (oral and parenteral). We defined 2018–2019 as the prepandemic period and 2020–2021 as the pandemic period. We compared the relative and absolute difference in antibiotic consumption between the 2 periods. Results: Across all periods, 8.3 million patients received care in the VHA, and an average of 28,709,680 antibiotic days were prescribed per year. Overall, 92.9% of all antibiotic days were outpatient and 7.1% were inpatient. Total antibiotic days during the pandemic period decreased by 12.4% compared to the prepandemic period (pandemic period: 53,613,840 and prepandemic period: 61,224,878). This reduction was primarily driven by reductions in outpatient settings (relative reduction: 12.7% and absolute reduction: 7,254,880 antibiotic days over 2 years), but antibiotic days in inpatient settings decreased more modestly (relative reduction: 8.4% and absolute reduction: 356,158 antibiotic days over 2 years) (Fig. 1). When frequently prescribed antimicrobials were categorized by classes, fluoroquinolones and lincosamides showed the largest decreases (fluoroquinolones: 29.2% reduction and lincosamides: 27.2% reduction). Tetracyclines and sulfamethoxazole–trimethoprim had the smallest reductions (5.2% and 11.2%, respectively). Conclusions: Compared to the prepandemic period, the pandemic was associated with a substantial reduction in overall antibiotic consumption, especially in outpatient settings, which accounted for 95% of the overall reduction despite being outside the domain of most traditional antibiotic stewardship programs. The impact of the pandemic was most modest in the use of tetracyclines and trimethoprim–sulfamethoxazole and was most prominent in the use of fluoroquinolones and lincosamides. Further studies are required to improve the causal inference between the COVID-19 pandemic and this reduction in antibiotic consumption, as well as its impact on patient outcomes.

Funding: None

Disclosures: None

Group Name: VHA Center for Antimicrobial Stewardship and Prevention of Antimicrobial Resistance (CASPAR) Background: Antimicrobial stewardship programs (ASPs) are advised to measure antimicrobial consumption as a metric for audit and feedback. However, most ASPs lack the tools necessary for appropriate risk adjustment and standardized data collection, which are critical for peer-program benchmarking. We created a system that automatically extracts antimicrobial use data and patient-level factors for risk-adjustment and a dashboard to present risk-adjusted benchmarking metrics for ASP within the Veterans’ Health Administration (VHA). Methods: We built a system to extract patient-level data for antimicrobial use, procedures, demographics, and comorbidities for acute inpatient and long-term care units at all VHA hospitals utilizing the VHA’s Corporate Data Warehouse (CDW). We built baseline negative binomial regression models to perform risk-adjustments based on patient- and unit-level factors using records dated between October 2016 and September 2018. These models were then leveraged both retrospectively and prospectively to calculate observed-to-expected ratios of antimicrobial use for each hospital and for specific units within each hospital. Data transformation and applications of risk-adjustment models were automatically performed within the CDW database server, followed by monthly scheduled data transfer from the CDW to the Microsoft Power BI server for interactive data visualization. Frontline antimicrobial stewards at 10 VHA hospitals participated in the project as pilot users. Results: Separate baseline risk-adjustment models to predict days of therapy (DOT) for all antibacterial agents were created for acute-care and long-term care units based on 15,941,972 patient days and 3,011,788 DOT between October 2016 and September 2018 at 134 VHA hospitals. Risk adjustment models include month, unit types (eg, intensive care unit [ICU] vs non-ICU for acute care), specialty, age, gender, comorbidities (50 and 30 factors for acute care and long-term care, respectively), and preceding procedures (45 and 24 procedures for acute care and long-term care, respectively). We created additional models for each antimicrobial category based on National Healthcare Safety Network definitions. For each hospital, risk-adjusted benchmarking metrics and a monthly ranking within the VHA system were visualized and presented to end users through the dashboard (an example screenshot in Figure 1). Conclusions: Developing an automated surveillance system for antimicrobial consumption and risk-adjustment benchmarking using an electronic medical record data warehouse is feasible and can potentially provide valuable tools for ASPs, especially at hospitals with no or limited local informatics expertise. Future efforts will evaluate the effectiveness of dashboards in these settings.

Funding: No

Disclosures: None

Figure 1.

Figure 2.

Figure 3.

Coronavirus disease 2019 (COVID-19) remains a serious threat for long-term care facilities, and frequent screening of employees and residents places a substantial burden on those facilities. We report our successful multimodal prevention measures without frequent testing, which resulted in no cases within 20 nursing home units over the first 6 months of the pandemic.

Background: Daptomycin is considered an effective alternative to vancomycin in patients with methicillin-resistant Staphylococcus aureus bloodstream infection (MRSA BSI). Objective: We investigated the real-world effectiveness of recommended daptomycin doses compared with vancomycin. Methods: This nationwide retrospective cohort study included patients from 124 Veterans’ Affairs hospitals who had a MRSA BSI and were initially treated with vancomycin during 2007–2014. Patients were categorized into 3 groups by daptomycin dose calculated using adjusted body weight: low (>6 mg/kg/day), standard (6–8 mg/kg/day), and high (≥8 mg/kg/day). International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes were used to identify other prior or concurrent infections and comorbidities. Multivariate cox regression was used to compare 30-day all-cause mortality as the primary outcome comparing patients on either low-dose, standard-dose, or high-dose daptomycin with vancomycin. Hazard ratio (HR) and 95% confidence intervals (CIs) were reported. Results: Of the 7,518 patients in the cohort, 683 (9.1%) were switched to daptomycin after initial treatment with vancomycin for their MRSA BSI episode. A low dose of daptyomycin was administered to 181 patients (26.5%), a standard dose was given to 377 patients (55.2%), and a high dose was administered to 125 patients (18.3%). Dose groups differed significantly in body mass index (BMI), presence of an osteomyelitis diagnosis, and diagnosis of diabetes. Thirty-day mortality was significantly lower in daptomycin patients than in those given vancomycin (11.3% vs 17.6%; P < .0001). Treatment with daptomycin was associated with improved 30-day survival compared with vancomycin (HR, 0.66; 95% CI, 0.53–0.84), after adjusting for age, BMI, diagnosis of endovascular infection, skin and soft-tissue infection and osteomyelitis, hospitalization in the prior year, immunosuppression, diagnosis of diabetes, and vancomycin minimum inhibitory concentration (MIC). Treatment with a standard dose of daptomycin was associated with lower mortality compared with vancomycin (HR, 0.63; 95% CI, 0.46–0.86). High and low daptomycin dose groups had a trend toward improved 30-day survival compared with vancomycin (Fig. 1). In 2 separate sensitivity analyses excluding vancomycin patients, there was no difference in 30-day mortality between a standard dose and a high dose (HR, 1.01; 95% CI, 0.51–1.97). However, we detected a trend toward poor survival with a low dose compared with a standard dose (HR, 1.21; 95% CI, 0.73–2.02). Conclusions: A standard dose of daptomycin was significantly associated with lower 30-day mortality compared with continued vancomycin treatment. Accurate dosage of daptomycin and avoidance of low-dose daptomycin should be a part of good antibiotic stewardship practice.

Funding: None

Disclosures: None

Background: Studies of interventions to decrease rates of surgical site infections (SSIs) must include thousands of patients to be statistically powered to demonstrate a significant reduction. Therefore, it is important to develop methodology to extract data available in the electronic medical record (EMR) to accurately measure SSI rates. Prior studies have created tools that optimize sensitivity to prioritize chart review for infection control purposes. However, for research studies, positive predictive value (PPV) with reasonable sensitivity is preferred to limit the impact of false-positive results on the assessment of intervention effectiveness. Using information from the prior tools, we aimed to determine whether an algorithm using data available in the Veterans Affairs (VA) EMR could accurately and efficiently identify deep incisional or organ-space SSIs found in the VA Surgical Quality Improvement Program (VASQIP) data set for cardiac and orthopedic surgery patients. Methods: We conducted a retrospective cohort study of patients who underwent cardiac surgery or total joint arthroplasty (TJA) at 11 VA hospitals between January 1, 2007, and April 30, 2017. We used EMR data that were recorded in the 30 days after surgery on inflammatory markers; microbiology; antibiotics prescribed after surgery; International Classification of Diseases (ICD) and current procedural terminology (CPT) codes for reoperation for an infection related purpose; and ICD codes for mediastinitis, prosthetic joint infection, and other SSIs. These metrics were used in an algorithm to determine whether a patient had a deep or organ-space SSI. Sensitivity, specificity, PPV and negative predictive values (NPV) were calculated for accuracy of the algorithm through comparison with 30-day SSI outcomes collected by nurse chart review in the VASQIP data set. Results: Among the 11 VA hospitals, there were 18,224 cardiac surgeries and 16,592 TJA during the study period. Of these, 20,043 were evaluated by VASQIP nurses and were included in our final cohort. Of the 8,803 cardiac surgeries included, manual review identified 44 (0.50%) mediastinitis cases. Of the 11,240 TJAs, manual review identified 71 (0.63%) deep or organ-space SSIs. Our algorithm identified 32 of the mediastinitis cases (73%) and 58 of the deep or organ-space SSI cases (82%). Sensitivity, specificity, PPV, and NPV are shown in Table 1. Of the patients that our algorithm identified as having a deep or organ-space SSI, only 21% (PPV) actually had an SSI after cardiac surgery or TJA. Conclusions: Use of the algorithm can identify most complex SSIs (73%–82%), but other data are necessary to separate false-positive from true-positive cases and to improve the efficiency of case detection to support research questions.

Funding: None

Disclosures: None

Background: When control mechanisms such as water temperature and biocide level are insufficient, Legionella, the causative bacteria of Legionnaires’ disease, can proliferate in water distribution systems in buildings. Guidance and oversight bodies are increasingly prioritizing water safety programs in healthcare facilities to limit Legionella growth. However, ensuring optimal implementation in large buildings is challenging. Much is unknown, and sometimes assumed, about whether building and campus characteristics influence Legionella growth. We used an extensive real-world environmental Legionella data set in the Veterans Health Administration (VHA) healthcare system to examine infrastructure characteristics and Legionella positivity. Methods: VHA medical facilities across the country perform quarterly potable water sampling of healthcare buildings for Legionella detection as part of a comprehensive water safety program. Results are reported to a standardized national database. We did an exploratory univariate analysis of facility-reported Legionella data from routine potable water samples taken in 2015 to 2018, in conjunction with infrastructure characteristics available in a separate national data set. This review examined the following characteristics: building height (number of floors), building age (reported construction year), and campus acreage. Results: The final data set included 201,936 water samples from 819 buildings. Buildings with 1–5 floors (n = 634) had a Legionella positivity rate of 5.3%, 6–10 floors (n = 104) had a rate of 6.4%, 11–15 floors (n = 36) had a rate of 8.1%, and 16–22 floors (n = 9) had a rate of 8.8%. All rates were significantly different from each other except 11–15 floors and 16–22 floors (P < .05, χ2). The oldest buildings (1800s) had significantly less (P < .05, χ2) Legionella positivity than those built between 1900 and 1939 and between 1940 and 1979, but they were no different than the newest buildings (Fig. 1). In newer buildings (1980–2019), all decades had buildings with Legionella positivity (Fig. 1 inset). Campus acreage varied from ~3 acres to almost 500 acres. Although significant differences were found in Legionella positivity for different campus sizes, there was no clear trend and campus acreage may not be a suitable proxy for the extent or complexity of water systems feeding buildings. Conclusions: The analysis of this large, real-world data set supports an assumption that taller buildings are more likely to be associated with Legionella detection, perhaps a result of more extensive piping. In contrast, the assumption that newer buildings are less associated with Legionella was not fully supported. These results demonstrate the variability in Legionella positivity in buildings, and they also provide evidence that can inform implementation of water safety programs.

Funding: None

Disclosures: Chetan Jinadatha, principal Investigator/Co-I: Research: NIH/NINR, AHRQ, NSF principal investigator: Research: Xenex Healthcare Services. Funds provided to institution. Inventor: Methods for organizing the disinfection of one or more items contaminated with biological agents. Owner: Department of Veterans Affairs. Licensed to Xenex Disinfection System, San Antonio, TX.