{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:47:12Z","timestamp":1753883232565,"version":"3.41.2"},"reference-count":25,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Services Research and Managerial Epidemiology"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p> Food protein-induced enterocolitis syndrome (FPIES) is a non-IgE-mediated food allergy, characterized by delayed onset of repetitive vomiting occurring 1 to 4\u2005h 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. <\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p> 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 \u201carules\u201d and \u201carulesViz\u201d packages, implementing the Apriori algorithm with set minimum support and confidence thresholds. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p> 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. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p> 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. <\/jats:p><\/jats:sec>","DOI":"10.1177\/23333928241264020","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T22:09:05Z","timestamp":1721945345000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Applying Market Basket Analysis to  Determine Complex Coassociations  Among Food Allergens in Children With  Food Protein-Induced Enterocolitis  Syndrome (FPIES)"],"prefix":"10.1177","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4433-5446","authenticated-orcid":false,"given":"Ankona","family":"Banerjee","sequence":"first","affiliation":[{"name":"Division of Epidemiology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth","family":"Nobleza","sequence":"additional","affiliation":[{"name":"Division of Epidemiology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cynthia","family":"Haddad","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua","family":"Eubanks","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA"},{"name":"Division of Immunology, Allergy and Retrovirology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruchit","family":"Rana","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA"},{"name":"Division of Immunology, Allergy and Retrovirology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas L.","family":"Rider","sequence":"additional","affiliation":[{"name":"Department of Health System & Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, VA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lisa","family":"Pompeii","sequence":"additional","affiliation":[{"name":"Division of Patient Services Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5059-4404","authenticated-orcid":false,"given":"Duc","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Division of Epidemiology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sara","family":"Anvari","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA"},{"name":"Division of Immunology, Allergy and Retrovirology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA"},{"name":"Texas Children's Hospital, William T. Shearer Center for Human Immunobiology, Houston, TX, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"bibr1-23333928241264020","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2016.12.966"},{"key":"bibr2-23333928241264020","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2017.03.027"},{"key":"bibr3-23333928241264020","doi-asserted-by":"publisher","DOI":"10.1016\/j.anai.2021.01.020"},{"key":"bibr4-23333928241264020","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaci.2015.12.968"},{"key":"bibr5-23333928241264020","doi-asserted-by":"crossref","unstructured":"Agrawal R, Imieli\u0144ski T, Swami A. Mining association rules between sets of items in large databases. 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