PMID- 37458237
OWN - NLM
STAT- MEDLINE
DCOM- 20231102
LR  - 20231103
IS  - 1744-8409 (Electronic)
IS  - 1744-666X (Linking)
VI  - 19
IP  - 10
DP  - 2023 Jul-Dec
TI  - The state of play in tools for predicting immunoglobulin resistance in Kawasaki 
      disease.
PG  - 1273-1279
LID - 10.1080/1744666X.2023.2238122 [doi]
AB  - 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)-1beta 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.
FAU - Guo, Mindy Ming-Huey
AU  - Guo MM
AD  - Kawasaki Disease Center, Department of Pediatrics, Kaohsiung Chang Gung Memorial 
      Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
AD  - School of Medicine, Chung Shan Medical University, Taichung, Taiwan.
FAU - Kuo, Ho-Chang
AU  - Kuo HC
AD  - Kawasaki Disease Center, Department of Pediatrics, Kaohsiung Chang Gung Memorial 
      Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan.
AD  - Department of Respiratory Therapy, Kaohsiung Chang Gung Memorial Hospital, 
      Kaohsiung, Taiwan.
AD  - School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PT  - Review
DEP - 20230719
PL  - England
TA  - Expert Rev Clin Immunol
JT  - Expert review of clinical immunology
JID - 101271248
RN  - 0 (Immunoglobulins, Intravenous)
SB  - IM
MH  - Humans
MH  - Infant
MH  - *Immunoglobulins, Intravenous/therapeutic use
MH  - *Mucocutaneous Lymph Node Syndrome/diagnosis/drug therapy/genetics
MH  - Risk Factors
MH  - Retrospective Studies
MH  - Drug Resistance
OTO - NOTNLM
OT  - Gene wide association analysis
OT  - IVIG resistance
OT  - Kawasaki disease
OT  - Machine learning
OT  - Transcriptomics
EDAT- 2023/07/17 15:08
MHDA- 2023/07/17 15:09
CRDT- 2023/07/17 06:43
PHST- 2023/07/17 15:09 [medline]
PHST- 2023/07/17 15:08 [pubmed]
PHST- 2023/07/17 06:43 [entrez]
AID - 10.1080/1744666X.2023.2238122 [doi]
PST - ppublish
SO  - Expert Rev Clin Immunol. 2023 Jul-Dec;19(10):1273-1279. doi: 
      10.1080/1744666X.2023.2238122. Epub 2023 Jul 19.