Liu Xiao-Ping, Huang Yi-Shuang, Xia Han-Bing, Sun Yi, Lang Xin-Ling, Li Qiang-Zi, Liu Chun-Yi, Kuo Ho-Chang, Huang Wei-Dong, Liu Xi
The Department of Emergency and Pediatrics, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China.
Department of Pediatrics, Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan.
Front Pediatr. 2020 Dec 11;8:559389. doi: 10.3389/fped.2020.559389. eCollection 2020.
Kawasaki disease (KD) is a form of systemic vasculitis that occurs primarily in children under the age of 5 years old. No single laboratory data can currently distinguish KD from other febrile infection diseases. The purpose of this study was to establish a laboratory data model that can differentiate between KD and other febrile diseases caused by an infection in order to prevent coronary artery complications in KD. This study consisted of a total of 800 children (249 KD and 551 age- and gender-matched non-KD febrile infection illness) as a case-control study. Laboratory findings were analyzed using univariable, multivariable logistic regression, and nomogram models. We selected 562 children at random as the model group and 238 as the validation group. The predictive nomogram included high eosinophil percentage (100 points), high C-reactive protein (93 points), high alanine transaminase (84 points), low albumin (79 points), and high white blood cell (64 points), which generated an area under the curve of 0.873 for the model group and 0.905 for the validation group. Eosinophilia showed the highest OR: 5.015 (95% CI:-3.068-8.197) during multiple logistic regression. The sensitivity and specificity in the validation group were 84.1 and 86%, respectively. The calibration curves of the validation group for the probability of KD showed near an agreement to the actual probability. Eosinophilia is a major factor in this nomogram model and had high precision for predicting KD. This report is the first among the existing literature to demonstrate the important role of eosinophil in KD by nomogram.
川崎病（KD）是一种主要发生在5岁以下儿童的全身性血管炎。目前尚无单一实验室数据能够将KD与其他发热性感染疾病区分开来。本研究的目的是建立一个能够区分KD与其他感染性发热疾病的实验室数据模型，以预防KD中的冠状动脉并发症。本研究共纳入800名儿童（249例KD患儿和551例年龄及性别匹配的非KD发热性感染疾病患儿）作为病例对照研究。使用单变量、多变量逻辑回归和列线图模型对实验室检查结果进行分析。我们随机选择562名儿童作为模型组，238名作为验证组。预测列线图包括高嗜酸性粒细胞百分比（100分）、高C反应蛋白（93分）、高谷丙转氨酶（84分）、低白蛋白（79分）和高白细胞（64分），模型组曲线下面积为0.873，验证组为0.905。在多变量逻辑回归中，嗜酸性粒细胞增多症的OR值最高：5.015（95%CI：-3.068-8.197）。验证组的敏感性和特异性分别为84.1%和86%。验证组KD概率的校准曲线显示与实际概率接近一致。嗜酸性粒细胞增多症是该列线图模型中的一个主要因素，对预测KD具有较高的准确性。本报告是现有文献中首个通过列线图证明嗜酸性粒细胞在KD中重要作用的研究。
