Literature DB >> 28043682

Efficacy of Four Scoring Systems in Predicting Intravenous Immunoglobulin Resistance in Children with Kawasaki Disease in a Children's Hospital in Beijing, North China.

Ruixia Song1, Wei Yao2, Xiaohui Li3.   

Abstract

OBJECTIVE: To evaluate the predictive efficacies of 4 existing scoring systems for intravenous immunoglobulin (IVIG) resistance in Kawasaki disease (KD) in hospitalized children with KD in a children's hospital affiliated with the Capital Institute of Pediatrics, Beijing, China. STUDY
DESIGN: We retrospectively analyzed 1569 children with KD treated at our children's hospital between January 2010 and December 2015. Age, sex, clinical manifestations, and pretreatment hematologic indicators were recorded. Scores were assigned using 4 existing scoring systems: Egami, Kobayashi, San Diego, and Formosa. A 4-case table test was used to determine prediction efficacies.
RESULTS: There were 63 IVIG-resistant cases (41 males, 22 females; average age, 2.5 years). Nine cases were classified as high risk for IVIG resistance by the Egami system, and this system had a sensitivity of 14% and a specificity of 86%. Ten cases had Kobayashi high-risk scores, and this system had a sensitivity of 16% and a specificity of 85%. The San Diego system assigned 60 cases as high-risk, and had a sensitivity of 95% and specificity of 3%. Finally, 27 cases had Formosa scores in the high-risk category, and this system had a sensitivity of 43% and a specificity of 47%.
CONCLUSIONS: None of the evaluated systems for assessing the risk for IVIG resistance displayed the combination of sensitivity and specificity necessary for screening. Our analyses show that the 4 scoring systems have limited utility in predicting IVIG resistance among patients with KD in our population.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Kawasaki disease; intravenous immunoglobulin resistance; scoring systems

Mesh:

Substances:

Year:  2016        PMID: 28043682     DOI: 10.1016/j.jpeds.2016.12.018

Source DB:  PubMed          Journal:  J Pediatr        ISSN: 0022-3476            Impact factor:   4.406


  21 in total

1.  The factors affecting the disease course in Kawasaki disease.

Authors:  Elif Arslanoglu Aydin; Ilker Ertugrul; Yelda Bilginer; Ezgi Deniz Batu; Hafize Emine Sonmez; Selcan Demir; Zehra Serap Arici; Erdal Sag; Dursun Alehan; Seza Ozen
Journal:  Rheumatol Int       Date:  2019-05-28       Impact factor: 2.631

2.  Etanercept With IVIg for Acute Kawasaki Disease: A Randomized Controlled Trial.

Authors:  Michael A Portman; Nagib S Dahdah; April Slee; Aaron K Olson; Nadine F Choueiter; Brian D Soriano; Sujatha Buddhe; Carolyn A Altman
Journal:  Pediatrics       Date:  2019-05-02       Impact factor: 7.124

Review 3.  Association of Genetic Polymorphisms in Kawasaki Disease with the Response to Intravenous Immunoglobulin Therapy.

Authors:  E Sapountzi; L Fidani; A Giannopoulos; A Galli-Tsinopoulou
Journal:  Pediatr Cardiol       Date:  2022-07-30       Impact factor: 1.838

4.  Prediction of intravenous immunoglobulin resistance in patients with Kawasaki disease according to the duration of illness prior to treatment.

Authors:  Kee-Soo Ha; JungHwa Lee; Kwang Chul Lee
Journal:  Eur J Pediatr       Date:  2019-11-12       Impact factor: 3.183

5.  A comparison of efficacy of six prediction models for intravenous immunoglobulin resistance in Kawasaki disease.

Authors:  Weiguo Qian; Yunjia Tang; Wenhua Yan; Ling Sun; Haitao Lv
Journal:  Ital J Pediatr       Date:  2018-03-09       Impact factor: 2.638

Review 6.  Dissecting Kawasaki disease: a state-of-the-art review.

Authors:  S M Dietz; D van Stijn; D Burgner; M Levin; I M Kuipers; B A Hutten; T W Kuijpers
Journal:  Eur J Pediatr       Date:  2017-06-27       Impact factor: 3.183

Review 7.  Indirect-comparison meta-analysis of treatment options for patients with refractory Kawasaki disease.

Authors:  Han Chan; Huan Chi; Hui You; Mo Wang; Gaofu Zhang; Haiping Yang; Qiu Li
Journal:  BMC Pediatr       Date:  2019-05-17       Impact factor: 2.125

8.  Thrombospondin-2 predicts response to treatment with intravenous immunoglobulin in children with Kawasaki disease.

Authors:  Shuai Yang; Ruixia Song; Xiaohui Li; Ting Zhang; Jin Fu; Xiaodai Cui
Journal:  BMJ Paediatr Open       Date:  2018-01-03

9.  A machine learning approach to predict intravenous immunoglobulin resistance in Kawasaki disease patients: A study based on a Southeast China population.

Authors:  Tengyang Wang; Guanghua Liu; Hongye Lin
Journal:  PLoS One       Date:  2020-08-27       Impact factor: 3.240

10.  Predictive model based on gene and laboratory data for intravenous immunoglobulin resistance in Kawasaki disease in a Chinese population.

Authors:  Li Meng; Zhen Zhen; Xiao-Hui Li; Yue Yuan; Qian Jiang; Wei Yao; Ming-Ming Zhang; Ai-Jie Li; Lin Shi
Journal:  Pediatr Rheumatol Online J       Date:  2021-06-26       Impact factor: 3.054

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