메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
저널정보
대한진단검사의학회 Annals of Laboratory Medicine Annals of Laboratory Medicine 제38권 제1호
발행연도
2018.1
수록면
23 - 31 (9page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
Background: The identification of clinically meaningful specific immunoglobulin E (sIgE) is important for the diagnosis and management of allergic diseases. Various in vitro sIgE detection methods are available worldwide. Depending on the number of antigens that can be tested simultaneously, there are two representative methods: singleplex and multiplex. Singleplex sIgE detection is mainly provided by Thermo Fisher (ImmunoCAP) and Siemens (Immulite). This study aimed to compare the diagnostic agreement of two singleplex sIgE detection assays. Methods: Sera from 209 Korean patients with allergic disease were used to compare the ImmunoCAP and Immulite assays with respect to the following allergens: inhalant allergens (Dermatophagoides farinae, cat and dog dander, oak, rye grass, mugwort, Alternaria, German cockroach) and food allergens (hen’s egg white, cow’s milk, wheat, peanut, soybean, and shrimp). Data from 902 paired comparison tests were included for comparisons. Qualitative, semi-quantitative, and quantitative comparisons were performed using statistical analyses. Results: In qualitative comparisons, the positivity and negativity agreements ranged from 75% (wheat, shrimp) to 96% (Alternaria). Class consistency (classes 0–6) was well matched. Spearman’s rank correlation coefficients for all allergens except shrimp were over 0.7. In quantitative comparisons, all allergens excluding shrimp showed >0.7 intra-class correlation coefficients. Conclusions: The ImmunoCAP and Immulite systems showed similar performances. However, clinicians should consider fundamental methodological differences between the assays.

목차

등록된 정보가 없습니다.

참고문헌 (23)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0