โก Quick Summary
A new clinical decision tool, the Betalactam-Predictor, has been developed to assist in identifying low-risk patients for betalactam allergy delabeling. Initial validation shows a remarkable 93% specificity, significantly improving upon previous decision tools.
๐ Key Details
- ๐ Dataset: 6,468 patients across multiple centers
- ๐งฉ Features used: Eight-item questionnaire based on patient history
- โ๏ธ Technology: Artificial intelligence-assisted variable selection
- ๐ Performance: 93% specificity in multicenter external validation
๐ Key Takeaways
- ๐ก New tool simplifies the process of delabeling low-risk betalactam allergy patients.
- ๐ Significant improvement in specificity compared to previous tools (25% increase).
- ๐งช Developed using data from 2,207 patients at Mรกlaga University Hospital.
- ๐ Multicentric validation included patients from Spain, the USA, Italy, France, and Denmark.
- ๐ Eight-item questionnaire assesses risk factors for allergic reactions.
- ๐ฐ Cost-effective approach to reducing unnecessary antibiotic allergy labels.
- ๐ฉบ Potential for use in non-specialty settings, enhancing patient care.
๐ Background
Approximately 10% of the population is labeled as having a betalactam allergy, which can lead to the use of less effective second-line antibiotics. This mislabeling can have serious implications for patient safety and treatment efficacy. The development of a reliable tool to identify low-risk patients is crucial for improving antibiotic stewardship and patient outcomes.
๐๏ธ Study
The study was conducted at Mรกlaga University Hospital, where researchers aimed to create a Betalactam-Predictor score. This involved a comprehensive analysis of a retrospective cohort of 2,207 patients who underwent penicillin allergy testing. The score was developed through a meticulous two-step variable selection process, utilizing both univariate analysis and logistic regression.
๐ Results
The final questionnaire comprised eight items, with risk points assigned based on the logistic regression model. The internal validation yielded a specificity of 86% and a negative predictive value of 83%. The multicenter external validation demonstrated an impressive 93% specificity, indicating the tool’s effectiveness in accurately identifying low-risk patients.
๐ Impact and Implications
The introduction of the Betalactam-Predictor could significantly streamline the diagnostic process for low-risk patients, facilitating quicker delabeling and reducing the burden of unnecessary antibiotic allergy labels. This tool has the potential to enhance patient safety, improve treatment outcomes, and lower healthcare costs, making it a valuable addition to clinical practice.
๐ฎ Conclusion
The development of the Betalactam-Predictor represents a significant advancement in the management of betalactam allergies. With its high specificity and potential for use in various healthcare settings, this tool could transform how we approach antibiotic allergy assessments. Continued research and validation will be essential to fully realize its benefits in clinical practice.
๐ฌ Your comments
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Development of Betalactam-Predictor: A Clinical Decision Tool for Delabeling Low-Risk Betalactam Allergy Patients. Initial Validation in Penicillin Allergy.
Abstract
BACKGROUND: A label of betalactam (BL) allergy is estimated in around 10% of the population in their medical records. Second-line choices carry significant negative consequences, including reduced efficacy, effectiveness, and safety. This study aimed to develop a new highly specific score constructed by selecting variables assisted by artificial intelligence to identify low-risk BL-allergic patients.
METHODS: In this study, derivation and validation of the BL-predictor score were performed on a retrospective cohort of 2207 patients who underwent penicillin allergy testing at Mรกlaga University Hospital (Spain). The development of the BL-predictor encompassed expert drafting and a two-step variable selection process consisting of univariate analysis and variable filtering, followed by stepwise logistic regression with resampling. To assess the efficiency, a multicentric retrospective external validation was performed in 4261 patients from six populations: Salamanca and Madrid, Spain; Nashville, United States of America; Verona, Italy; Paris, France; and Copenhagen, Denmark.
RESULTS: The definitive questionnaire consisted of eight items and risk points were computed from the logistic regression model as follows: +1 for reactions after first dose or in less than 1โh (ITEM-1), +2 for anaphylaxis (ITEM-2); +1 for previous reaction with the culprit (ITEM-3); -1 for resolution in >โ24โh (ITEM-4); +2 for spontaneous resolution (ITEM-5); -2 for unknown symptoms (ITEM-6); -2 for reaction occurred >โ5โyears (ITEM-7), and -1 for another reported drug allergy (ITEM-8). After establishing a threshold of โคโ0 points to classify individuals with low risk, internal validation showed a specificity of 86% and a negative predictive value (NPV) of 83%. Overall multicenter external validation showed a specificity of 93%, which implies a 25% increase in specificity compared to the previously published BL decision tool.
CONCLUSION: This score would simplify diagnostic procedures in low-risk patients, enabling rapid delabeling, potentially in non-specialty settings, and reducing diagnostic costs and the negative consequences associated with incorrect antibiotic allergy labels.
Author: [‘Labella M’, ‘Nuรฑez R’, ‘Doรฑa I’, ‘de Guzmรกn JR’, ‘Moreno E’, ‘Garvey LH’, ‘Laguna JJ’, ‘Barbaud A’, ‘Bonnadona P’, ‘Boel JB’, ‘Mosbech H’, ‘Sfriso G’, ‘Castells M’, ‘Phillips E’, ‘Torres MJ’]
Journal: Allergy
Citation: Labella M, et al. Development of Betalactam-Predictor: A Clinical Decision Tool for Delabeling Low-Risk Betalactam Allergy Patients. Initial Validation in Penicillin Allergy. Development of Betalactam-Predictor: A Clinical Decision Tool for Delabeling Low-Risk Betalactam Allergy Patients. Initial Validation in Penicillin Allergy. 2026; (unknown volume):(unknown pages). doi: 10.1111/all.70222