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<p>Multisystem inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection in children and presents with highly heterogeneous signs and symptoms. Disentangling the clinical manifestations of MIS-C by characterizing its subphenotypes can help identify children at risk for severe outcomes and may help identify more targeted therapies. Characterizing subphenotypes usually requires large sample sizes and can often benefit from combining data from multiple sources. However, joint analysis often faces two major challenges: the prohibition of sharing patient-level data due to privacy concerns and between-study population clinical heterogeneity. To address both challenges, we propose a one-shot summary statistics-based framework to transfer knowledge of the shared subphenotypes pre-trained on large-scale source studies to a target study. Study-specific subphenotype mixing proportions are used to explain between-study heterogeneity. Both real-data guided simulation studies and an application to the MIS-C analysis demonstrate the benefits of our method.</p>

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Pre-trained knowledge transfer with application in multisystem inflammatory syndrome subphenotyping in children

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Xiaokang Liu,
  • Naimin Jing,
  • Yiwen Lu,
  • Jason H. Moore,
  • Christopher B. Forrest,
  • James Zou,
  • Yong Chen

摘要

Multisystem inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection in children and presents with highly heterogeneous signs and symptoms. Disentangling the clinical manifestations of MIS-C by characterizing its subphenotypes can help identify children at risk for severe outcomes and may help identify more targeted therapies. Characterizing subphenotypes usually requires large sample sizes and can often benefit from combining data from multiple sources. However, joint analysis often faces two major challenges: the prohibition of sharing patient-level data due to privacy concerns and between-study population clinical heterogeneity. To address both challenges, we propose a one-shot summary statistics-based framework to transfer knowledge of the shared subphenotypes pre-trained on large-scale source studies to a target study. Study-specific subphenotype mixing proportions are used to explain between-study heterogeneity. Both real-data guided simulation studies and an application to the MIS-C analysis demonstrate the benefits of our method.