Background Despite a strong evidence base and clinical guidelines specifically recommending against prolonged post-procedural antimicrobial use, studies indicate that the practice is common following cardiac device procedures. Formative evaluations conducted by the study team suggest that inappropriate antimicrobial use may be driven by information silos that drive provider belief that antimicrobials are not harmful, in part due to lack of complete feedback about all types of clinical outcomes. De-implementation is recognized as an important area of research that can lead to reductions in unnecessary, wasteful, or harmful practices, such as excess antimicrobial use following cardiac device procedures; however, investigations into strategies that lead to successful de-implementation are limited. The overarching hypothesis to be tested in this trial is that a bundle of implementation strategies that includes audit and feedback about direct patient harms caused by inappropriate prescribing can lead to successful de-implementation of guideline-discordant care. Methods We propose a hybrid type III effectiveness-implementation stepped-wedge intervention trial at three high-volume, high-complexity VA medical centers. The main study intervention (an informatics-based, real-time audit-and-feedback tool) was developed based on learning/unlearning theory and formative evaluations and guided by the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) Framework. Elements of the bundled and multifaceted implementation strategy to promote appropriate prescribing will include audit-and-feedback reports that include information about antibiotic harms, stakeholder engagement, patient and provider education, identification of local champions, and blended facilitation. The primary study outcome is adoption of evidence-based practice (de-implementation of inappropriate antimicrobial use). Clinical outcomes (cardiac device infections, acute kidney injuries and Clostridioides difficile infections) are secondary. Qualitative interviews will assess relevant implementation outcomes (acceptability, adoption, fidelity, feasibility). Discussion De-implementation theory suggests that factors that may have a particularly strong influence on de-implementation include strength of the underlying evidence, the complexity of the intervention, and patient and provider anxiety and fear about changing an established practice. This study will assess whether a multifaceted intervention mapped to identified de-implementation barriers leads to measurable improvements in provision of guideline-concordant antimicrobial use. Findings will improve understanding about factors that impact successful or unsuccessful de-implementation of harmful or wasteful healthcare practices. Trial registration ClinicalTrials.govNCT05020418

ImportanceStandardized processes for identifying when allergic-type reactions occur and linking reactions to drug exposures are limited.ObjectiveTo develop an informatics tool to improve detection of antibiotic allergic-type events.Design, Setting, and ParticipantsThis retrospective cohort study was conducted from October 1, 2015, to September 30, 2019, with data analyzed between July 1, 2021, and January 31, 2022. The study was conducted across Veteran Affairs hospitals among patients who underwent cardiovascular implantable electronic device (CIED) procedures and received periprocedural antibiotic prophylaxis. The cohort was split into training and test cohorts, and cases were manually reviewed to determine presence of allergic-type reaction and its severity. Variables potentially indicative of allergic-type reactions were selected a priori and included allergies entered in the Veteran Affair’s Allergy Reaction Tracking (ART) system (either historical [reported] or observed), allergy diagnosis codes, medications administered to treat allergic reactions, and text searches of clinical notes for keywords and phrases indicative of a potential allergic-type reaction. A model to detect allergic-type reaction events was iteratively developed on the training cohort and then applied to the test cohort. Algorithm test characteristics were assessed.ExposurePreprocedural and postprocedural prophylactic antibiotic administration.Main Outcomes and MeasuresAntibiotic allergic-type reactions.ResultsThe cohort of 36 344 patients included 34 703 CIED procedures with antibiotic exposures (mean [SD] age, 72 [10] years; 34 008 [98%] male patients); median duration of postprocedural prophylaxis was 4 days (IQR, 2-7 days; maximum, 45 days). The final algorithm included 7 variables: entries in the Veteran Affair’s hospitals ART, either historic (odds ratio [OR], 42.37; 95% CI, 11.33-158.43) or observed (OR, 175.10; 95% CI, 44.84-683.76); PheCodes for “symptoms affecting skin” (OR, 8.49; 95% CI, 1.90-37.82), “urticaria” (OR, 7.01; 95% CI, 1.76-27.89), and “allergy or adverse event to an antibiotic” (OR, 11.84, 95% CI, 2.88-48.69); keyword detection in clinical notes (OR, 3.21; 95% CI, 1.27-8.08); and antihistamine administration alone or in combination (OR, 6.51; 95% CI, 1.90-22.30). In the final model, antibiotic allergic-type reactions were identified with an estimated probability of 30% or more; positive predictive value was 61% (95% CI, 45%-76%); and sensitivity was 87% (95% CI, 70%-96%).Conclusions and RelevanceIn this retrospective cohort study of patients receiving periprocedural antibiotic prophylaxis, an algorithm with a high sensitivity to detect incident antibiotic allergic-type reactions that can be used to provide clinician feedback about antibiotic harms from unnecessarily prolonged antibiotic exposures was developed.

Background: Despite a strong evidence base and clinical guidelines specifically recommending against prolonged post-procedural antimicrobial use, studies indicate that the practice is common following cardiac device procedures. Formative evaluations conducted by the study team suggest that inappropriate antimicrobial use may be driven by information silos that drive provider belief that antimicrobials are not harmful, in part due to lack of complete feedback about all types of clinical outcomes. De-implementation is recognized as an important area of research that can lead to reductions in unnecessary, wasteful, or harmful practices, such as excess antimicrobial use following cardiac device procedures; however, investigations into strategies that lead to successful de-implementation are limited. The overarching hypothesis to be tested in this trial is that a bundle of implementation strategies that includes audit and feedback about direct patient harms caused by inappropriate prescribing can lead to successful de-implementation of guideline-discordant care.Methods: We propose a Hybrid Type III effectiveness-implementation stepped-wedge intervention trial at three high-volume, high-complexity VA medical centers. The main study intervention (an informatics-based, real-time audit-and-feedback tool) was developed based on learning/unlearning theory and formative evaluations and guided by the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) Framework. Elements of the bundled and multifaceted implementation strategy to promote appropriate prescribing will include audit-and-feedback reports that include information about antibiotic harms, stakeholder engagement, patient and provider education, identification of local champions, and blended facilitation. The primary study outcome is adoption of evidence-based practice (de-implementation of inappropriate antimicrobial use). Clinical outcomes (cardiac device infections, acute kidney injuries and Clostridioides difficile infections) are secondary. Qualitative interviews will assess relevant implementation outcomes (acceptability, adoption, fidelity, feasibility).Discussion: De-implementation theory suggests that factors that may have a particularly strong influence on de-implementation include strength of the underlying evidence, the complexity of the intervention, and patient and provider anxiety and fear about changing an established practice. This study will assess whether a multifaceted intervention mapped to identified de-implementation barriers leads to measurable improvements in provision of guideline-concordant antimicrobial use. Findings will improve understanding about factors that impact successful or unsuccessful de-implementation of harmful or wasteful healthcare practices.Trial Registration: Clinicaltrials.gov (NCT05020418)

Background Antibiotics are one of the leading causes of emergency room visits for adverse drug events, yet surveillance for antimicrobial allergy adverse events is limited and identifying true cases is challenging. As part of a larger study to improve antimicrobial use, we sought to develop and validate a tool for near real-time measurement of antimicrobial allergy adverse events. Methods An existing cohort of patients undergoing cardiac device procedures with known antimicrobial exposure was split into a development and validation set. Candidate triggers for identifying allergic reactionswere identified a priori, using disease phenotype codes “phecodes”, allergy documentation on allergy module of the electronic medical record (EMR), and keyword searches applied to clinical notes (e.g., “anaphylaxis,” “rash”), medication administration (e.g, corticosteroids alone or with antihistamines) and administrative codes (ICD-10 codes and phecodes). Cases were reviewed for presence of a true event, and the tool was iteratively updated based on chart review findings. The tool was then applied to the validation cohort and a sample of trigger-flagged and unflagged cases underwent manual review. Data were analyzed in SAS and model triggers were selected using a LASSO technique. Results Among 34,703 patients, N=431 cases underwent manual review (350 development; 120 validation), and 104 true allergy adverse events were identified. Among chart reviewed cases, the most frequently detected flags were keywords in unstructured clinical notes (35%), phecodes (26%), corticosteroid administration (15%), observed allergy documentation in EMR (14%) and reported allergy documentation in EMR (13%). The final model contained 7 triggers and had an AUC of 0.95, and a positive predictive value of 67% (Figure). The strongest predictors of true adverse events were the allergy health factors (aOR 358, 95% CI 76.3-999) and specific Phecodes (Table1). Conclusion We developed an antibiotic allergy measurement tool using structured and unstructured data that can be applied to detect antimicrobial adverse events in near-real time. This model may be applied to provide near real-time feedback to clinicians about antimicrobial allergy adverse events and may be useful for antimicrobial stewardship programs. Disclosures Westyn Branch-Elliman, MD, MMSc, DLA Piper,LLC/Medtronic: Advisor/Consultant|Gilead Pharmaceuticals: Grant/Research Support.