Kawasaki Disease Center, Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
School of Medicine, Chung Shan Medical University, Taichung, Taiwan.
Expert Rev Clin Immunol. 2023 Jul-Dec;19(10):1273-1279. doi: 10.1080/1744666X.2023.2238122. Epub 2023 Jul 19.
Intravenous immunoglobulin (IVIG) resistance is an independent risk factor for the development of coronary artery lesions (CAL) in patients with Kawasaki disease (KD). Accurate identification of IVIG-resistant patients is one of the biggest clinical challenges in the treatment of KD.
In this review article, we will go over current IVIG resistance scoring systems and other biological markers of IVIG resistance, with a particular focus on advances in machine-based learning techniques and high-throughput omics data.
Traditional scoring models, which were developed using logistic regression, including the Kobayashi score and Egami score, are inadequate at identifying IVIG resistance in non-Japanese populations. Newer machine-learning methods and high-throughput technologies including transcriptomic and epigenetic arrays have identified several potential targets for IVIG resistance including gene expression of the Fc receptor, and components of the interleukin (IL)-1β and pyroptosis pathways. As we enter an age where access to big data has become more commonplace, interpretation of large data sets that are able take into account complexities in patient populations will hopefully usher in a new era of precision medicine, which will enable us to identify and treat KD patients with IVIG resistance with increased accuracy.
静脉注射免疫球蛋白（IVIG）耐药是川崎病（KD）患者发生冠状动脉病变（CAL）的独立危险因素。准确识别 IVIG 耐药患者是 KD 治疗中最大的临床挑战之一。
在这篇综述文章中，我们将回顾当前的 IVIG 耐药评分系统和其他 IVIG 耐药的生物学标志物，特别关注基于机器学习技术和高通量组学数据的进展。
使用逻辑回归开发的传统评分模型，包括 Kobayashi 评分和 Egami 评分，在识别非日本人群中的 IVIG 耐药性方面不够充分。新的机器学习方法和高通量技术，包括转录组和表观遗传学阵列，已经确定了几个 IVIG 耐药的潜在靶点，包括 Fc 受体的基因表达以及白细胞介素（IL）-1β 和细胞焦亡途径的组成部分。随着我们进入一个更容易获得大数据的时代，对能够考虑到患者人群复杂性的大型数据集的解释有望开创精准医学的新时代，使我们能够更准确地识别和治疗 IVIG 耐药的 KD 患者。
