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Prediction Models for Intravenous Immunoglobulin Non-Responders of Kawasaki Disease Using Machine Learning

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Abstract

Background and Objective

Intravenous immunoglobulin (IVIG) is a prominent therapeutic agent for Kawasaki disease (KD) that significantly reduces the incidence of coronary artery anomalies. Various methodologies, including machine learning, have been employed to develop IVIG non-responder prediction models; however, their validation and reproducibility remain unverified. This study aimed to develop a predictive scoring system for identifying IVIG nonresponders and rigorously test the accuracy and reliability of this system.

Methods

The study included an exposure group of 228 IVIG non-responders and a control group of 997 IVIG responders. Subsequently, a predictive machine learning model was constructed. The Shizuoka score, including variables such as the “initial treatment date” (cutoff: < 4 days), sodium level (cutoff: < 133 mEq/L), total bilirubin level (cutoff: ≥ 0.5 mg/dL), and neutrophil-to-lymphocyte ratio (cutoff: ≥ 2.6), was established. Patients meeting two or more of these criteria were grouped as high-risk IVIG non-responders. Using the Shizuoka score to stratify IVIG responders, propensity score matching was used to analyze 85 patients each for IVIG and IVIG-added prednisolone treatment in the high-risk group. In the IVIG plus prednisolone group, the IVIG non-responder count significantly decreased (p < 0.001), with an odds ratio of 0.192 (95% confidence interval 0.078–0.441).

Conclusions

Intravenous immunoglobulin non-responders were predicted using machine learning models and validated using propensity score matching. The initiation of initial IVIG-added prednisolone treatment in the high-risk group identified by the Shizuoka score, crafted using machine learning models, appears useful for predicting IVIG non-responders.

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Acknowledgements

We thank the current and former members of the Shizuoka Kawasaki Disease Study Group for their cooperation in conducting this study. We also thank Hidemasa Sakai, Department of Pediatrics, Shizuoka City Shizuoka Hospital; Shinichiro Sano, Department of Diabetes and Metabolism, Shizuoka Children’s Hospital; Naoe Akiyama, Department of Pediatrics, Fuji City General Hospital; Masashi Harazaki, Department of Pediatrics, Medical Genetics, Shizuoka General Hospital; Tetuya Fukuoka, Department of Pediatrics, Shizuoka Saiseikai General Hospital; and Isao Miyairi, Department of Pediatrics, Hamamatsu University School of Medicine. ChatGPT was used for English proofreading.

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Authors and Affiliations

  1. Department of Pediatrics, Haibara Hospital, Makinohara City, Shizuoka, Japan

    Yoshifumi Miyagi

  2. The Shizuoka Kawasaki Disease Study Group, Shizuoka, Japan

    Satoru Iwashima

  3. Department of Pediatrics, Chutoen General Medical Center, 1-1 Shobugaike, Kakegawa, Shizuoka, 436-0040, Japan

    Satoru Iwashima

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  1. Yoshifumi Miyagi
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Correspondence to Satoru Iwashima.

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of Interest

Yoshifumi Miyagi and Satoru Iwashima declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical Approval

The study protocol conformed to the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Chutoen Medical Center (approval date: 5 August, 2019; approval number: 100).

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Author Contributions

All authors made substantial contributions to the conception of the study. All authors read and approved the final manuscript. YM and SI interpreted the data and wrote the paper.

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Miyagi, Y., Iwashima, S. Prediction Models for Intravenous Immunoglobulin Non-Responders of Kawasaki Disease Using Machine Learning. Clin Drug Investig 44, 425–437 (2024). https://doi.org/10.1007/s40261-024-01373-z

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