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Spearman’s rank-order correlation to compare hospitals on these metrics and their Conclusion. Our findings suggest that hospitals using fewer days of antibiotic associated rankings. therapy did not necessarily use narrower-spectrum antibiotics. ASI/1,000 DP, as a Results. At the hospital-level, the median ASI/DOT, ASI/1,000 DP and combined measure of antibiotic consumption quantity and average spectrum, pro- DOT/1,000 DP were 5.4 (interquartile range: 5.2-5.8), 2,332.7 (1,941.8-2,796.2) and vided a different view of hospital performance than DOT/1,000 DP alone. Future work 443.5 (362.5-512.2), respectively. There was a strong correlation between the ASI/1,000 is needed to define how this new metric relates to the quality of antibiotic use. DP and DOT/1,000 DP metrics [Spearman’s correlation test: r=0.97 (p< 0.01)] but only Disclosures. All Authors: No reported disclosures a weak and insignificant correlation between ASI/DOT and DOT/1,000 DP [r=0.17 (p=0.06), Figure 1]. Twenty (16.1%) hospitals showed a difference of 10% or more in 103. Expansion of an Antimicrobial Stewardship Program Through their ranking for ASI/1,000 DP compared to their ranking for DOT/1,000 DP. The Implementation of a Discharge Verification Queue range of ranking difference was from -17.7% to 21.0% (Figure 2a and b). 1 1 Ellen C. Rubin, PharmD, BCPS ; Alison L. Blackman, PharmD, BCIDP ; 1 1 1 Eleanor K. Broadbent, PharmD ; David Wang, PharmD ; Ilda Plasari, PharmD ; Figure 1. Distribution of the Antibiotic Spectrum Index / Day of Therapy by Days of 1 1 1 Pawlose Ketema, PharmD ; Karrine Brade, PharmD ; Tamar F. Barlam, MD, MSc ; era Th py / 1000 Days Present for 124 Acute-Care VHA Hospitals during 2018. Black Boston Medical Center, South Boston, Massachusetts line: Median values of DOT/1,000 DP and ASI/DOT, respectively. Session: P-07. Antimicrobial Stewardship: Program Development and Implementation Background. Antimicrobial stewardship programs (ASPs) have traditionally focused interventions on inpatient care to improve antibiotic prescribing. Support of effective interventions for ASPs targeting antibiotic prescriptions at hospital discharge is emerging. Our objective was to expand stewardship services into the outpatient set- ting through implementation of a process by the antimicrobial stewardship team (AST) to verify antimicrobials prescribed at discharge. Methods. This quality improvement initiative incorporated a discharge order veri - fication queue managed by AST pharmacists to review electronically prescribed antimi - crobials Monday through Friday, from 8:00 am to 4:00 pm. The queue was piloted Sep 2020 and expanded hospital-wide Feb 2021. Patients < 18 years old and those with ob- servation or emergency department status were excluded. The AST pharmacist reviewed discharge prescriptions for appropriateness, intervened directly with prescribers, and ei- ther rejected or verified prescriptions prior to transmission to outpatient pharmacies. Complicated cases were reviewed with the AST physician to evaluate intervention appro- priateness. Interventions were categorized as either dose adjustment, duration, escalation or de-escalation, discontinuation, or safety monitoring. Results. A total of 602 prescriptions were reviewed between Sep 2020 and Apr 2021. An AST pharmacist intervened on 28% (171/602) of prescriptions. The most common intervention types were duration (41%, 70/171), discontinuation (18%, 31/171), and dose adjustment (17%, 30/171). The most common indications in which the duration was shortened was community acquired pneumonia (26%, 18/70), skin and soft tissue infection (21%, 15/70), and urinary tract infection (17%, 12/70). The most common antibiotics recommended for discontinuation were cephalexin (32%, 10/31) and trimeth- oprim-sulfamethoxazole (10%, 3/31). The overall intervention acceptance rate was 78%. Conclusion. An AST pharmacist review of antimicrobial prescriptions at dis- charge improved appropriate prescribing. The discharge queue serves as an effective Figure 2. (a) Distribution of the rankings in DOT/1,000 DP and ASI/1,000 DP. Blue stewardship strategy for inpatient ASPs to expand into the outpatient setting. line: the position of same ranking between ASI/1,000 DP and DOT/1,000 DP. (b) Disclosures. All Authors: No reported disclosures Distribution of the differences in each hospital’s ranking for DOT/1,000 DP and ASI/1,000 DP 104. Improving Efficiency of Antimicrobial Stewardship Reviews Using Artificial Intelligence Modelling 1 1 Si Lin Sarah Tang, BSc Pharm (Hons), MSc ; Winnie Lee, MSc ; 2 2 Yiling Chong, MTech ; Akshay Saigal, B.Eng (Electronics) ; Peijun Yvonne Zhou, BSc Pharm (Hons), MSc ; Kai Chee Hung, BSc 1 1 1 (Pharmacy) ; Lun Yi Tan, BSc ; Shimin Jasmine Chung, M.B.B.S, BSc, MRCP ; 1 1 Lay Hoon Andrea Kwa, PharmD ; Singapore General Hospital, Singapore, Not Applicable, Singapore; DXC Technology, Singapore, Not Applicable, Singapore Session: P-07. Antimicrobial Stewardship: Program Development and Implementation Background. Antimicrobial stewardship programs (ASP) in hospitals improve antibiotic prescribing, slow antimicrobial resistance, reduce hospitalisation duration, mortality and readmission rates, and save costs. However, the strategy of prospective audit and feedback is laborious. In Singapore General Hospital (SGH), 10 reviews are required to identify 2 inappropriate cases. Limited manpower constraints ASP audits to only about 30% of antibiotics prescribed. This proof-of-concept study explored the feasibility of developing a predictive model to prioritise inappropriate antibiotic pre- scriptions for ASP review. Methods. ASP-audited adult pneumonia patients from January 2016 to December 2018 in SGH were included. Patient data e.g., demographics, allergies, past medical history, and relevant laboratory investigations at each antibiotic use episode were extracted from electronic medical records and re-assembled through linking for analysis. Ground truth for model training was based on ASP-defined ap - propriateness for each encounter. The dataset was split into 80% and 20% for training and testing respectively. Three modelling techniques, XGBoost, decision tree and logistic regression, were assessed for their relative performance in terms of precision, sensitivity and specificity. Results. er Th e were 12471 unique patient encounters. Training was done on 10459 encounters and 39 data elements were included. When tested on 2012 encounters, the logistic regression model performed the best (86.7% sensitivity, 71.4% specificity). The model correctly classified 1377 out of 1388 (99.2%) encounters as “appropriate” (do not require ASP intervention). 624 antibiotic use encounters were classified as “inappro - priate”, of which only 72 were truly inappropriate (positive predictive value for ASP intervention, PPV 11.5%). The low PPV was likely due to inadequate representation of “inappropriate” cases in the training dataset (4.1%). Applying this model would priori- tise the number of immediate ASP reviews needed to identify cases for intervention by two-thirds, from 2012 to 624 (Figure 1). S166 • OFID 2021:8 (Suppl 1) • Abstracts
Open Forum Infectious Diseases – Oxford University Press
Published: Dec 4, 2021
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