Centre for Musculoskeletal Research, Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, United Kingdom; Department of Immunology, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Salford, United Kingdom; Greater Manchester Immunology Service, Manchester University NHS Foundation Trust, Manchester, United Kingdom.
School of Computing and Digital Technology, Digital Innovation Hub, STEAM House, Birmingham City University, Birmingham, United Kingdom.
J Allergy Clin Immunol Pract. 2021 Dec;9(12):4410-4418.e4. doi: 10.1016/j.jaip.2021.07.057. Epub 2021 Sep 8.
Penicillin allergy overdiagnosis has been associated with inappropriate antibiotic prescribing, increased antimicrobial resistance, worse clinical outcomes, and increased health care costs.
To develop and validate a questionnaire-based algorithm built in a mobile application to support clinicians in collecting accurate history of previous reactions and diagnosing drug allergy appropriately.
A survey was completed by 164 medical and nonmedical prescribers to understand barriers to best practice. Based on the survey recommendations, we created a 10-item questionnaire-based algorithm to allow classification of drug allergy history in line with the National Institute for Health and Care Excellence guidelines on drug allergy. The algorithm was incorporated into a mobile application and retrospectively validated using anonymized clinical databases at regional immunology and dermatology centers in Manchester, United Kingdom.
A total of 55.2% of prescribers (95% confidence interval, 47% to 63.4%) thought it impossible to draw a firm conclusion based on history alone and 59.4% (95% CI, 51.4% to 67.5%) believed that regardless of the details of the penicillin allergy history, they would avoid all β-lactams. A drug allergy mobile application was developed and retrospectively validated, which revealed a low risk for misclassification of outcomes compared with reference standard drug allergy investigations in the allergy and dermatology clinics.
Perceived lack of time and preparedness to collect an accurate drug allergy history appear to be important barriers to appropriate antimicrobial prescribing. The Drug Allergy App may represent a useful clinical decision support tool to diagnose drug allergy correctly and support appropriate antibiotic prescribing.
青霉素过敏过度诊断与不适当的抗生素处方、抗菌药物耐药性增加、临床结局恶化以及医疗保健费用增加有关。
开发并验证一种基于移动应用程序的问卷算法，以支持临床医生准确收集既往反应史并进行适当的药物过敏诊断。
对 164 名医学和非医学处方者进行了一项调查，以了解最佳实践的障碍。根据调查建议，我们创建了一个 10 项基于问卷的算法，以根据国家卫生与保健卓越研究所（NICE）关于药物过敏的指南对药物过敏史进行分类。该算法被纳入移动应用程序，并使用英国曼彻斯特地区免疫和皮肤科中心的匿名临床数据库进行回顾性验证。
共有 55.2%的处方者（95%置信区间，47%至 63.4%）认为仅凭病史不可能得出明确结论，59.4%（95%置信区间，51.4%至 67.5%）的人认为，无论青霉素过敏史的详细情况如何，他们都会避免使用所有β-内酰胺类抗生素。开发并回顾性验证了一款药物过敏移动应用程序，与过敏和皮肤科诊所的药物过敏参考标准调查相比，该应用程序显示出对结果分类错误的风险较低。
临床医生认为缺乏时间和准备来收集准确的药物过敏史是适当使用抗菌药物的重要障碍。药物过敏应用程序（Drug Allergy App）可能是一种有用的临床决策支持工具，可以正确诊断药物过敏并支持适当的抗生素处方。
