rational policy planning. We developed a mathematical model to describe antimicrobial use and demonstrate how it could be used in a model-driven decision support system.
Methods.  We developed a discrete-time Markov chain model to describe antimicrobial use as a function of the following parameters: Choice decisions to start antibiotics on admission or after, Change decisions to stop antibiotics, and Completion decisions to discharge patients whether they were on or off antimicrobials. Partial derivatives were used to predict the extent to which antimicrobial use would respond to changes in each parameter. We used Veterans Affairs Bar Code Medication Administration data from 2010 to estimate parameters, as well as antimicrobial use using National Healthcare Safety Network (NHSN) definitions. Categories of anti-methicillin-resistant Staphylococcus aureus (MRSA), broad community, broad hospital, and surgical site infection prophylaxis (SSIP) from NHSN were also used. Because of certain assumptions made when estimating parameters, we used non-linear regression to adjust them using data from year 2010. We then applied our model to predict antimicrobial use from 2013 parameters and compared with actual use with Pearson’s correlation coefficient.
Results.  Correlation of predicted and actual antimicrobial use was 0.97, 0.99, 0.95, and 0.92 (using NHSN category order above; Figure 1). As a conservative estimate, the correlation of yearly changes between predicted and actual antimicrobial use for all categories was 0.75. For > 99% of all combinations of medical center, antimicrobial category, and year, decreasing the probability of starting antimicrobials had the most impact on measured antimicrobial use.
Conclusion.  Our mathematical model is highly predictive of antimicrobial use and can be used to anticipate how much changes in decision points might lead to changes in antimicrobial use. Given the parameter space that most VA medical centers occupy, not starting antimicrobials appears to have greatest impact on use.

Background.  A national assessment of antibiotic appropriateness in intensive care units (ICUs) with benchmarking was performed to assist antibiotic stewardship programs (ASPs) identify improvement opportunities.
Methods.  A Centers for Disease Control and Prevention tool was adapted by an expert panel from the Partnership for Quality Care (PQC), a coalition dedicated to high quality care in US hospitals, to validate appropriate antibiotic use measurement via a point prevalence survey on a single day. Data were collected by ASP personnel at each hospital, de-identified and submitted in aggregate to PQC for benchmarking. Hospitals identified reasons for inappropriate antibiotic use by category and antibiotics misused.
Results.  Forty-seven ICUs from 12 PQC hospitals participated: California (2), Florida (2), Massachusetts (3), Minnesota (1), and New York (4). Most hospitals identified as teaching (83%) with 252-1550 bed size (median: 563)  and 20–270 licensed ICU beds (median: 70). All hospitals reported a formal ASP. On March 1, 2017, 362 (54%) of 667 patients in participating ICUs were on antibiotics (range: 8-81 patients); 1 patient was not assessed. Of the remaining 361 antibiotic regimens, 112 (31%) were identified as inappropriate from among all 12 hospitals (range: 9-82%) (figure). The table displays inappropriate antibiotic use by ICU type. Reasons for inappropriate use included unnecessarily broad spectrum of activity (29%), duration longer than necessary (21%), and treatment of a non-infectious syndrome (19%). The antibiotic most commonly misused was vancomycin in 7 (58%) hospitals.
Conclusion.  Up to 80% of antibiotic use in some ICUs is inappropriate, underscoring the need for ASP interventions, standardized assessment tools and benchmarking. Strategies should focus on de-escalation of broad-spectrum antibiotics and reducing duration of therapy.

Disclosures.  All authors: No reported disclosures.

685. Working Together to Define Antibiotic Appropriateness: Point Prevalence Survey in 47 Intensive Care Units from 12 US Hospitals, Partnership for Quality Care, March 2017 Kavita K. Trivedi, MD1; Belinda Ostrowsky, MD, MPH, FIDSA, FSHEA2; Lilian M. Abbo, MD3; Arjun Srinivasan, MD, FSHEA4; Rachel Bartash, MD5; Fred Cassera, RPh6; Jorge Fleisher, MD7; David W. Kubiak, PharmD, BCPS {AQ ID}8; Alyssa R. Letourneau, MD, MPH9; Priya Nori, MD10; Stephen Parodi, MD, FIDSA11; Laura Aragon, PharmD, BCPS-AQ ID12; Eliza Dollard, PharmD12; Christina Gagliardo, MD13; Monica Ghitan, MD, FIDSA14; Amber Giles, PharmD12; Suri Mayer, PharmD6; Jennifer Quevedo, PharmD15; Gunter Rieg, MD16; Galina Shteyman, PharmD, BCPS17; Jaclyn Vargas, MD18; Shannon Kelley, MPA19 and Phyllis Silver, MEd19; 1Trivedi Consults, LLC, Berkeley, California, 2Department of Medicine, Division of Infectious Diseases, Montefiore Medical Center, Bronx, New York, 3Infectious Disease, University of Miami-Jackson Memorial Hospital, Miami, Florida, 4Centers for Disease Control and Prevention, Atlanta, Georgia, 5Infectious Diseases, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, New York, 6Maimonides Medical Center, Brooklyn, New York, 7Infectious Diseases, Saint Elizabeth’s Medical Center/ Tufts University, Brighton, Massachusetts , 8Division of Infectious Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts , 9Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts , 10Infectious Diseases, Montefiore Medical Center, Bronx, New York, 11The Permanente Medical Group, Vallejo, California, 12Pharmacy, Jackson Memorial Hospital, Miami, Florida, 13Pediatrics, Maimonides Infants & Children’s Hospital of Brooklyn, Brooklyn, New York, 14Division of Infectious Diseases, Maimonides Medical Center, Brooklyn, New York, 15University of Miami Hospital, Miami, Florida, 16Kaiser Permanente, Harbor City, California, 17Park Nicollet Methodist Hospital, Health Partners, St. Louis Park, Minnesota, 18Los Angeles County + University of Southern California Medical Center, Los Angeles, California, 19Partnership for Quality Care, New York City, New York
Session: 74. Stewardship: Data and Program Planning Thursday, October 5, 2017: 12:30 PM

Disclosures.  D.  W. Kubiak, Shionogi: Consultant, Consulting fee. Astellas Pharma: Consultant, Consulting fee
686. Broad-Spectrum Antibiotic Use at Choice, Change, and Completion Throughout VA: Patterns of Initiation and De-escalation Matthew Goetz, MD1,2,3; Christopher J. Graber, MD, MPH, FIDSA4,5; Makoto Jones, MD, MS6; Karl Madaras-Kelly, PharmD, M.P.H.7; Matthew Samore, MD, FSHEA8; Peter Glassman, MBBS, MSc2,9 and The Veterans Affairs Antimicrobial Use Learning Collaborative; 1Department of Medicine, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, 2David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, 3Infectious Diseases, VA Greater Los Angeles Healthcare System, Los Angeles, California, 4Infectious Diseases Section, VA Greater Los Angeles Healthcare System, Los Angeles, California, 5David Geffen School of Medicine at the University of California, Los Angeles, Los Angeles, California, 6Internal Medicine, VA Salt Lake City Health Care System, Salt

S250 • OFID 2017:4 (Suppl 1) • Poster Abstracts