Background An antibiogram is a summary of antibiotic susceptibility patterns for selected bacterial pathogens and antibiotics. The New Hampshire Department of Health and Human Services’ Division of Public Health Services (DPHS) sought to create an annual state antibiogram to monitor statewide antibiotic resistance trends, guide appropriate empiric antibiotic prescribing, and inform future statewide antibiotic stewardship. Methods Through legislative authority, DPHS required hospital laboratories to report antibiogram data annually. DPHS convened an advisory group of infectious disease and pharmacy stakeholders and experts to develop a standardized reporting form for bacteria and antibiotic susceptibility, which was disseminated to all 26 hospitals in New Hampshire. We combined the reported data into a statewide antibiogram, and we created clinical messaging to highlight findings and promote rational antibiotic prescribing among health care providers. Results All hospital laboratories in New Hampshire submitted annual antibiogram data for 2016 and 2017, including more than 30 000 and 20 000 bacterial isolates recovered from urine and nonurine cultures, respectively, each year. The advisory group created clinical messages for appropriate treatment of common infectious syndromes, including uncomplicated urinary tract infections, community-acquired pneumonia, skin and soft-tissue infections, intra-abdominal infections, and health care–associated gram-negative aerobic infections. The statewide antibiograms and clinical messaging were widely disseminated. Conclusions The small size of New Hampshire, a centralized public health structure, and close working relationships with hospitals and clinical partners allowed for efficient creation and dissemination of an annual statewide antibiogram, which has fostered public health–clinical partnerships and built a foundation for future state-coordinated antibiotic stewardship. This process serves as a model for other jurisdictions that are considering antibiogram development.

Few data exist on the incidence of central line-associated bloodstream infection present on hospital admission (CLABSI-POA), although the practice of patients maintaining central lines outside of hospitals is increasing. We describe patients presenting to an academic medical center with CLABSI-POA over 1 year. Of the 130 admissions, half presented from home infusion (47%), followed by oncology clinic (22%), hemodialysis (14%), and skilled nursing facility (8%). Efforts to reduce CLABSIs should address patients across the entire health care system.

Background Syndrome-based antibiotic stewardship can be limited by difficulty in finding cases for evaluation. We developed an electronic extraction algorithm to prospectively identify CAP patients. Methods We included non-oncology patients ≥18 years old admitted to The Johns Hopkins Hospital from 12/2018 to 3/2019 who 1) received common CAP antibiotics for ≥48 hours after admission and 2) had a bacterial urinary antigen and chest imaging ordered within 48 hours of admission that was not for assessment of endotracheal tube or central line placement. Charts of patients meeting these criteria were reviewed by 2 authors to identify true cases of CAP based on IDSA guidelines. Cases identified in 12/2018 (n=111) were used to explore potential indicators of CAP, and cases identified 1–3/2019 (n=173) were used to evaluate combinations of indicators that could identify patients treated for CAP who did have CAP (true CAP) and did not have CAP (false CAP). This cohort was divided into a training and a validation set (2/3 and 1/3, respectively). Potential indicators included vitals signs, laboratory data and free text extracted via natural language processing (NLP). Predictive performance of composite indicators for true CAP were assessed using receiver-operating characteristics (ROC) curves. The Hosmer-Lemeshow goodness fit test was used to test model fit and the Akaike Information Criteria was used to determine model selection. Results True CAP was observed in 41% (71/173) of cases and 14 potential individual indicators were identified (Table). These were combined to make 45 potential composite indicators. ROC curves for selected composite indicators are shown in the Figure. Models without use of NLP-derived variables had poor discriminative ability. The best model included fever, hypoxemia, leukocytosis, and “consolidation” on imaging with a sensitivity and positive predictive value 78.7% and specificity and negative predictive value of 85.7%. Table. Indicators evaluated to identify patients with CAP Figure. ROC curves for composite indicators Conclusion Patients with CAP can be identified using electronic data but use of NLP-derived radiographic criteria is required. These data can be linked with data on antibiotic use and duration to develop reports for clinicians regarding appropriate CAP diagnosis and treatment. Disclosures All Authors: No reported disclosures

BackgroundAntibiotic-resistant infections have been identified as an urgent national health threat. In response, the New Hampshire Division of Public Health Services (DPHS) sought to develop a system for tracking antibiotic resistance statewide through use of hospital antibiograms to (1) proactively monitor resistance trends over time and geographic region, (2) promote antimicrobial stewardship in NH healthcare facilities, and (3) provide a tool for providers to help guide appropriate antibiotic prescribing.MethodsThrough statutory legislative authority, DPHS requires hospital laboratories to report antibiogram data annually. DPHS formed an advisory group, consisting of infectious disease, medical and pharmacy subject matter experts to develop a standardized data collection tool. DPHS validated reported data to confirm accuracy, and clarify aberrant data by comparing the susceptibilities among all hospitals. Any questionable data were verified with the respective laboratory. The combined data were reviewed by the clinical advisory group and recommendations were created from the antibiogram data to highlight appropriate antibiotic prescribing and the need for coordinated stewardship. The antibiogram and clinical recommendations were disseminated widely throughout the state.ResultsAll 26 hospitals in New Hampshire submitted data. A total of 42,519 and 21,306 bacteria were cultured from urine and non-urine sources, respectively. The clinical advisory group’s recommendations included interpretations and antibiotic therapy directives for common clinical syndromes. Dissemination was accomplished through a health alert, partnership with a state working group of stakeholders, widespread email communication and online publication.ConclusionThe small size of New Hampshire, centralized public health structure, and close working relationships with all hospitals allowed for efficient collection of these data. Our process may serve as a model for other states, and will inform more accurate, comprehensive antibiotic resistance surveillance. This antibiogram is the launch for a larger statewide public health antibiotic stewardship campaign and coincides with national efforts around antibiotic stewardship and resistance surveillance.Disclosures All authors: No reported disclosures.