应用市场篮子分析法确定食物蛋白诱发小肠结肠炎综合征(FPIES)患儿食物过敏原之间的复杂共轭关系。

IF 1.5 Q3 HEALTH POLICY & SERVICES
Health Services Research and Managerial Epidemiology Pub Date : 2024-07-25 eCollection Date: 2024-01-01 DOI:10.1177/23333928241264020
Ankona Banerjee, Kenneth Nobleza, Cynthia Haddad, Joshua Eubanks, Ruchit Rana, Nicholas L Rider, Lisa Pompeii, Duc Nguyen, Sara Anvari
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引用次数: 0

摘要

背景:食物蛋白诱发的小肠结肠炎综合征(FPIES)是一种非 IgE 介导的食物过敏症,其特征是在摄入食物过敏原 1 到 4 小时后出现迟发性重复呕吐。治疗 FPIES 需要严格避免诱发食物。FPIES 的关注点在于确定由于与其他食物或食物组的潜在共生关系而引发另一种 FPIES 食物诱发反应的风险。分析 FPIES 相关数据的有效统计方法对于确定常见的共过敏原及其关联性至关重要:本研究采用数据挖掘技术 "市场篮子分析"(Market Basket Analysis)来研究德克萨斯州休斯顿市一家儿科三级中心的 FPIES 患者的过敏原之间的相关性和模式。研究人员对 2018 年 1 月至 2022 年 3 月由过敏专科医生诊断的 FPIES 患者的电子病历进行了回顾性分析。分析使用了R软件,特别是 "arules "和 "arulesViz "软件包,采用Apriori算法,设定了最小支持度和置信度阈值:研究纳入了 210 例 FPIES 病例,历时 4 年,其中 112 例患者对一种食物诱发因素产生反应,98 例患者对一种以上诱发因素产生反应。在后一组患者中,5种主要诱发因素分别是牛奶(45.9%)、大米(31.6%)、燕麦(30.6%)、大豆(22.4%)和鳄梨(19.4%)。市场篮子分析确定了食品类别之间的重要关联,尤其是大豆与乳制品、鸡蛋与乳制品、燕麦与乳制品、大米与乳制品以及牛油果与乳制品之间的关联:事实证明,市场篮子分析能有效识别 FPIES 数据中的模式和关联。这些见解对于医疗服务提供者为 FPIES 患者制定饮食建议至关重要。这种方法有可能加强对食物引入和避免的指导,从而改善 FPIES 患者的管理和生活质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying Market Basket Analysis to Determine Complex Coassociations Among Food Allergens in Children With Food Protein-Induced Enterocolitis Syndrome (FPIES).

Background: Food protein-induced enterocolitis syndrome (FPIES) is a non-IgE-mediated food allergy, characterized by delayed onset of repetitive vomiting occurring 1 to 4 h following ingestion of a food allergen. Managing FPIES requires strict avoidance of the food trigger. The concern with FPIES is determining the risk of another FPIES food trigger reaction due to potential coassociations with other foods or food groups. An effective statistical approach for analyzing FPIES-related data is essential to identify common coallergens and their associations.

Methods: This study employed Market Basket Analysis, a data-mining technique, to examine correlations and patterns among allergens in FPIES patients at a Houston, Texas, pediatric tertiary center. A retrospective analysis of electronic medical records from January 2018 to March 2022 for allergist diagnosed FPIES patients was conducted. The analysis utilized R software, specifically the "arules" and "arulesViz" packages, implementing the Apriori algorithm with set minimum support and confidence thresholds.

Results: The study included 210 FPIES cases over 4 years, with 112 patients reacting to one food trigger and 98 to more than one trigger. In the latter group, the 5 predominant triggers were cow's milk (45.9%), rice (31.6%), oats (30.6%), soy (22.4%), and avocado (19.4%). Market Basket Analysis identified significant associations between food categories, particularly between soy and dairy, egg and dairy, oat and dairy, rice and dairy, and avocado and dairy.

Conclusion: Market Basket Analysis proved effective in identifying patterns and associations in FPIES data. These insights are crucial for healthcare providers in formulating dietary recommendations for FPIES patients. This approach potentially enhances guidance on food introductions and avoidances, thereby improving management and the quality of life for those affected by FPIES.

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CiteScore
1.60
自引率
6.20%
发文量
32
审稿时长
12 weeks
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