期刊
JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY
卷 141, 期 4, 页码 1354-+出版社
MOSBY-ELSEVIER
DOI: 10.1016/j.jaci.2017.11.027
关键词
Allergy diagnosis; eosinophils; eosinophilic esophagitis; chronic allergic inflammation; IgE; machine learning; medical algorithm
资金
- Research Council of Boston Children's Hospital
- Food Allergy Research Education
- Crohn's and Colitis Foundation
- Fonds zur Forderung der wissenschaftlichen Forschung [DK W1248]
- Helmsley Charitable Trust through the Very Early Onset Inflammatory Bowel Disease Consortium
- National Institutes of Health (NIH) [NIHDK094971]
- NIH [R01DK61931, R01DK68271, R24DK099803, R01AI121186]
- NIH grant of the Harvard Digestive Diseases Center [P30DK034854]
- Austrian Science Fund (FWF) [W1248] Funding Source: Austrian Science Fund (FWF)
Background: Diagnostic evaluation of eosinophilic esophagitis (EoE) remains difficult, particularly the assessment of the patient's allergic status. Objective: This study sought to establish an automated medical algorithm to assist in the evaluation of EoE. Methods: Machine learning techniques were used to establish a diagnostic probability score for EoE, p(EoE), based on esophageal mRNA transcript patterns from biopsies of patients with EoE, gastroesophageal reflux disease and controls. Dimensionality reduction in the training set established weighted factors, which were confirmed by immunohistochemistry. Following weighted factor analysis, p(EoE) was determined by random forest classification. Accuracy was tested in an external test set, and predictive power was assessed with equivocal patients. Esophageal IgE production was quantified with epsilon germ line (IGHE) transcripts and correlated with serum IgE and the T(H)2-type mRNA profile to establish an IGHE score for tissue allergy. Results: In the primary analysis, a 3-class statistical model generated a p(EoE) score based on common characteristics of the inflammatory EoE profile. A p(EoE) >= 25 successfully identified EoE with high accuracy (sensitivity: 90.9%, specificity: 93.2%, area under the curve: 0.985) and improved diagnosis of equivocal cases by 84.6%. The p(EoE) changed in response to therapy. A secondary analysis loop in EoE patients defined an IGHE score of >= 37.5 for a patient subpopulation with increased esophageal allergic inflammation. Conclusions: The development of intelligent data analysis from a machine learning perspective provides exciting opportunities to improve diagnostic precision and improve patient care in EoE. The p(EoE) and the IGHE score are steps toward the development of decision trees to define EoE subpopulations and, consequently, will facilitate individualized therapy.
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