Impact of Education and Data Feedback on Antibiotic Prescribing for Urinary Tract Infections in the Emergency Department: An Interrupted Time-Series Analysis.

BACKGROUND: Urinary tract infections (UTIs) are often misdiagnosed or treated with exceedingly broad-spectrum antibiotics, leading to negative downstream effects. We aimed to implement antimicrobial stewardship (AS) strategies targeting UTI prescribing in the emergency department (ED). METHODS: We conducted a quasi-experimental prospective AS intervention outlining appropriate UTI diagnosis and management across 3 EDs, within an academic and 2 community hospitals, in North Carolina, United States. The study was divided into 3 phases: a baseline period and 2 intervention phases. Phase 1 included introduction of an ED-specific urine antibiogram and UTI guideline, education, and department-specific feedback on UTI diagnosis and antibiotic prescribing. Phase 2 included re-education and provider-specific feedback. Eligible patients included adults with an antibiotic prescription for UTI diagnosed in the ED from 13 November 2018 to 1 March 2021. Admitted patients were excluded. The primary outcome was guideline-concordant antibiotic use, assessed using an interrupted time-series regression analysis with 2-week intervals. RESULTS: Overall, 8742 distinct patients with 10 426 patient encounters were included. Ninety-two percent of all encounters (n = 9583) were diagnosed with cystitis and 8.1% with pyelonephritis (n = 843). There was an initial 15% increase in guideline-concordant antibiotic prescribing in phase 1 compared with the preintervention period (incidence rate ratio [IRR], 1.15; 95% confidence interval [CI], 1.03-1.29). A significant increase in guideline-concordant prescriptions was seen with every 2-week interval during phase 2 (IRR, 1.03; 95% CI, 1.01-1.04). CONCLUSIONS: This multifaceted AS intervention involving a guideline, education, and provider-specific feedback increased guideline-concordant antibiotic choices for treat-and-release patients in the ED.

Electronic health record-based readmission risk model performance for patients undergoing outpatient parenteral antibiotic therapy (OPAT).

BACKGROUND: Outpatient Parenteral Antibiotic Therapy (OPAT) provides coordinated services to deliver parenteral antibiotics outside of the acute care setting. However, the reduction in monitoring and supervision may impact the risks of readmission to the hospital. While identifying those at greatest risk of hospital readmission through use of computer decision support systems could aid in its prevention, validation of such tools in this patient population is lacking. OBJECTIVE: The primary aim of this study is to determine the ability of the electronic health record-embedded EPIC Unplanned Readmission Model 1 to predict all-cause 30-day hospital unplanned readmissions in discharged patients receiving OPAT through the Duke University Heath System (DUHS) OPAT program. We then explored the impact of OPAT-specific variables on model performance. METHODS: This retrospective cohort study included patients ≥ 18 years of age discharged to home or skilled nursing facility between July 1, 2019 -February 1, 2020 with OPAT care initiated inpatient and coordinated by the DUHS OPAT program and with at least one Epic readmission score during the index hospitalization. Those with a planned duration of OPAT < 7 days, receiving OPAT administered in a long-term acute care facility (LTAC), or ongoing renal replacement therapy were excluded. The relationship between the primary outcome (unplanned readmission during 30-day post-index discharge) and Epic readmission scores during the index admission (discharge and maximum) was examined using multivariable logistic regression models adjusted for additional predictors. The performance of the models was assessed with the scaled Brier score for overall model performance, the area under the receiver operating characteristics curve (C-index) for discrimination ability, calibration plot for calibration, and Hosmer-Lemeshow goodness-of-fit test for model fit. RESULTS: The models incorporating maximum or discharge Epic readmission scores showed poor discrimination ability (C-index 0.51, 95% CI 0.45 to 0.58 for both models) in predicting 30-day unplanned readmission in the Duke OPAT cohort. Incorporating additional OPAT-specific variables did not improve the discrimination ability (C-index 0.55, 95% CI 0.49 to 0.62 for the max score; 0.56, 95% CI 0.49 to 0.62 for the discharge score). Although models for predicting 30-day unplanned OPAT-related readmission performed slightly better, discrimination ability was still poor (C-index 0.54, 95% CI 0.45 to 0.62 for both models). CONCLUSION: EPIC Unplanned Readmission Model 1 scores were not useful in predicting either all-cause or OPAT-related 30-day unplanned readmission in the DUHS OPAT cohort. Further research is required to assess other predictors that can distinguish patients with higher risks of 30-day unplanned readmission in the DUHS OPAT patients.

Utility of a risk assessment model in predicting 30†day unplanned hospital readmission in adult patients receiving outpatient parenteral antimicrobial therapy.

Objectives: Outpatient parenteral antimicrobial therapy (OPAT) is associated with high hospital readmission rates. A 30 day unplanned readmission risk prediction model for OPAT patients has been developed in the UK. Given significant differences in patient mix and methods of OPAT delivery, we explored the model for its utility in Duke University Health System (DUHS) patients receiving OPAT. Methods: We analysed OPAT episodes of adult patients from two hospitals between 1 July 2019 and 1 February 2020. The discriminative ability of the model to predict 30 day unplanned all-cause and OPAT-related admission was examined. An updated model was created by logistic regression with the UK risk factors and additional risk factors, OPAT delivery in a skilled nursing facility, vancomycin use and IV drug abuse. Results: Compared with patients of the UK cohort, our study patients were of higher acuity, treated for more invasive infections, and received OPAT through different modes. The 30 day unplanned readmission rate in our cohort was 20% (94/470), with 59.5% (56/94) of those being OPAT-related. The original model was unable to discriminate for all-cause readmission with a C-statistic of 0.52 (95% CI 0.46-0.59) and for OPAT-related readmission with a C-statistic of 0.55 (95% CI 0.47-0.64). The updated model with additional risk factors did not have improved performance, with a C-statistic of 0.55 (95% CI 0.49-0.62). Conclusions: The UK 30 day unplanned hospital readmission model performed poorly in predicting readmission for the OPAT population at a US academic medical centre.

Impact of Education and Data Feedback on Guideline-Concordant Prescribing for Urinary Tract Infections in the Outpatient Setting.

BACKGROUND: Urinary tract infections (UTIs) are the most common outpatient indication for antibiotics and an important target for antimicrobial stewardship (AS) activities. With The Joint Commission standards now requiring outpatient AS, data supporting effective strategies are needed. METHODS: We conducted a 2-phase, prospective, quasi-experimental study to estimate the effect of an outpatient AS intervention on guideline-concordant antibiotic prescribing in a primary care (PC) clinic and an urgent care (UC) clinic between August 2017 and July 2019. Phase 1 of the intervention included the development of clinic-specific antibiograms and UTI diagnosis and treatment guidelines, presented during educational sessions with clinic providers. Phase 2, consisting of routine clinic- and provider-specific feedback, began ~12 months after the initial education. The primary outcome was percentage of encounters with first- or second-line antibiotics prescribed according to clinic-specific guidelines and was assessed using an interrupted time series approach. RESULTS: Data were collected on 4724 distinct patients seen during 6318 UTI encounters. The percentage of guideline-concordant prescribing increased by 22% (95% CI, 12% to 32%) after Phase 1 education, but decreased by 0.5% every 2 weeks afterwards (95% CI, -0.9% to 0%). Following routine data feedback in Phase 2, guideline concordance stabilized, and significant further decline was not seen (-0.6%; 95% CI, -1.6% to 0.4%). This shift in prescribing patterns resulted in a 52% decrease in fluoroquinolone use. CONCLUSIONS: Clinicians increased guideline-concordant prescribing, reduced UTI diagnoses, and limited use of high-collateral damage agents following this outpatient AS intervention. Routine data feedback was effective to maintain the response to the initial education.