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Introduction: Vancomycin-associated acute kidney injury (V-AKI) is a common adverse reaction; however, there is currently no method to systematically monitor its incidence. We previously developed and internally validated an electronic algorithm to identify cases of V-AKI using structured electronic health record data at the Johns Hopkins Hospital, which demonstrated excellent agreement with chart review (percent agreement 92.5%; weighted kappa coefficient 0.95), as well as excellent sensitivity (89.7%) and specificity (98.2%) in detecting at least possible V-AKI events. The objective of this study was to evaluate the generalizability of the V-AKI electronic algorithm. Methods: We identified a retrospective cohort of adult and pediatric patients who received ≥1 dose of intravenous vancomycin while admitted to University of Virginia (UVA) Medical Center from 1/2021-1/2023. An increase in creatinine (Cr) of ≥0.3 mg/dL within 48 hours or ≥50% increase in baseline Cr within 7 days, occurring after the first dose and up to 72 hours after the last dose of IV vancomycin, was considered a potential V-AKI event. The electronic algorithm was executed at UVA with only limited contextualization of hospital specific variables (e.g., procedure names). Patients were categorized as excluded/not meeting criteria, or as having an unlikely, possible or probable V-AKI event using a causality framework. A random subset of the cohort underwent chart review by a blinded reviewer for external validation. Percent agreement and a weighted kappa coefficient were calculated. The sensitivity and specificity in identifying at least possible V-AKI events was determined. Results: The electronic algorithm was validated using 200 cases and demonstrated 60.0% percent agreement with chart review (Figure). The weighted kappa coefficient was 0.75. The algorithm was 83.8% sensitive and 71.4% specific in detecting at least possible V-AKI events. Among the 80 discrepant cases, there was only a 1-category difference in 62.5% of cases. The most common reasons for discrepant assessments, which were partly due to inconsistencies in chart review, included disagreement regarding timing of AKI onset (18.6%) and whether renal function returned to baseline (16.3%). Conclusions: An electronic algorithm to identify V-AKI events was successfully implemented at another institution. Although agreement with chart review was only fair, sensitivity in detecting at least possible V-AKI events remained excellent. The electronic algorithm may be useful for systematically and reproducibly identifying V-AKI events across institutions in a scalable manner to inform stewardship interventions. However, further refinement of the algorithm and improvement in consistency of chart review assessments is needed.

Background: Feedback regarding antibiotic-associated adverse events (ABX-AEs) may assist clinicians with antibiotic decision making. We sought to understand how clinicians account for ABX-AEs when prescribing and their preferences for ABX-AE feedback. Methods: We conducted 1-hour virtual focus groups with 3–5 physicians or advance practice practitioners (APPs) per session at Johns Hopkins Hospital. Participants discussed the role of ABX-AEs in antibiotic decision making and feedback preferences. Participants evaluated prespecified categorization (mildly, moderately, or very concerning) of several ABX-AEs. Focus groups were recorded and transcribed. Transcripts were coded inductively by 2 independent reviewers; discrepancies were resolved by consensus. Codes were used to conduct thematic analysis. Results: Overall, 3 focus groups were conducted with 12 participants: 41.6% were house staff, 16.7% were attending physicians, and 41.6% were APPs. Most were female (91.6%) and were white (41.7%) or Asian (41.7%). Clinicians generally agreed with the prespecified categorizations of ABX-AEs based on degree of clinical concern (Table). We identified 5 themes: (1) The risk of ABX-AE is considered during initial prescribing but influences agent selection more than the decision to prescribe antibiotics. (2) The occurrence of an ABX-AE leads to assessment of need for continued antibiotic therapy. (3) The impact of an ABX-AE on other management decisions is as important as the direct harm of the ABX-AE when assessing severity. (4) Feedback must be curated to prevent clinicians from being overwhelmed with data. (5) Clinicians will be more receptive to feedback regarding ABX-AEs if feedback is contextualized (Fig.). Conclusions: The themes identified and assessment of ABX-AEs of greatest clinical concern may help inform the development of effective ABX-AE feedback methods to improve antibiotic safety.