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.

Cardiovascular implantable electronic device (CIED) infections have a high mortality and morbidity. CIED infections secondary to gram-negative pathogens are rare, and there is minimal data regarding their treatment. We report a case of a 60-year-old male who developed recurrent Salmonella enteritidis bacteremia leading to CIED infection and non-susceptibility to ciprofloxacin.

Background Outpatient parenteral antimicrobial therapy (OPAT) is used for patients that require prolonged durations of intravenous (IV) antimicrobials and who are healthy enough to receive the medications in the outpatient setting. While OPAT is both efficacious and cost-effective, hospital readmission rates are high. Durojaiye and colleagues in the UK developed a 30-day unplanned readmission risk prediction model for OPAT patients. Given differences in patient mix and methods of OPAT delivery, we validated the established risk assessment model for Duke University Health System (DUHS) patients receiving OPAT. Methods A retrospective review of 606 OPAT episodes of adult patients who were enrolled in the DUHS OPAT program between July 1, 2019 and February 1, 2020 was conducted. The review captured the 6 risk predictors of the established model: age, Charlson Comorbidity Score, number of admissions in the preceding 12 months, concurrent receipt of more than one IV antimicrobial agent, type of infection, and mode of OPAT delivery. Additional risk predictors were captured: aminoglycoside use, vancomycin use, OPAT delivery in a skilled nursing facility, and history of IV drug abuse. The discriminative ability of the model to predict 30-day unplanned readmission as well as 30-day OPAT-related unplanned readmission was validated with the collected data using scaled Brier score, Hosmer-Lemeshow goodness-of-fit, and area under the receiver operating curve. A logistic regression model fitted with the additional risk factors was conducted to determine their impact on the model. Results When comparing DUHS OPAT patients with those of the UK model, DUHS patients were sicker (mean Charlson Comorbidity Score 3 vs 1), were treated for deeper seated infections, and received OPAT through different modes. Overall the 30-day unplanned readmission rate was 20.0% (94/470), with 59.5% of those being OPAT-related. The UK model was unable to discriminate between patients with readmission and those without, both overall and OPAT-related. The additional risk factors were also non-significant between the groups and the updated model could not predict 30-day readmission risk. Conclusion The UK 30-day unplanned hospital readmission model did not predict patient risk of readmission for the Duke OPAT population. Disclosures Richard H. Drew, PharmD MS, American College of Clinical Pharmacists: Publication royalties|Takeda: Advisor/Consultant|UpToDate: publication royalties.

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.