Introduction: 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.
Areas covered: 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.
Expert opinion: 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.
Keywords: Gene wide association analysis; IVIG resistance; Kawasaki disease; Machine learning; Transcriptomics.