A data science approach to identify and manage Multisystem Inflammatory Syndrome in Children (MIS-C) associated with SARS-CoV-2 infection and Kawasaki disease in pediatric patients

摘要-自SARS-CoV-2大流行开始以来，出现了一种相关的新的多系统 儿童炎症性综合征(MIS-C)已有报道。有趣的是，患有MISC的患者遵循 表现、处理和临床病程与川崎病有些相似 疾病(KD)。目前，临床特征和管理如此重叠的原因尚不清楚 这种重叠是否是部分共同的病因或病理生理学的结果是激烈的主题 辩论。重叠程度意味着我们开发的一些临床预测工具 可以改变过去KD的用途，以加速开发用于MIS-C的临床支持决策工具。在……里面 在这项研究中，我们将首先(R61组件)系统地解决KD和MIS-C之间的重叠问题，并创建 基于显著机器学习的预测模型用于诊断/识别(目标1)、管理(目标2)、 和管理信息系统-C的短期和长期结果(目标3)，基于我们之前开发的预测模型 KD的过程类似于迁移学习。其次(R33组件)，我们将验证和评估 这些模型在预测性临床诊断决策支持系统中的性能和临床应用 以及儿科患者的治疗，这些患者表现出MIS-C或KD的特征。在这项研究中，我们 将包括3组患者：1)感染SARS-CoV-2的患者，有MIS-C(CDC标准)，无论 是否有KD的重叠症状，2)SARS-CoV-2感染患者调查BUT 最终未被诊断为MIS-C，以及3)KD但未感染SARS-CoV-2的患者。目标数据 将从登记的患者(900人用于培训，450人用于验证)中收集，用于深度表型分析和 生物标记物测量。医生对算法生成的预测的反馈将用于 建立临床实用程序。模型培训所需的数据将在活动的头两年积累(R61 )；算法的开发及其内部验证将同时进行。在 在2年后(33年资助期)，我们将进行外部验证，建立临床实用程序，增加实际- 将疫情监测数据及时打包到模型中，并为以后的算法进行验证 部署和整合电子健康记录。这个项目将是与 国际川崎病登记处(IKDR)联合会。IKDR财团有一个活跃的KD和 儿科COVID登记在全球35个地点，目前地点的数量正在扩大到60个地点。 已经在IKDR中心确认了600多名MIS-C患者，这使得这个项目显然是可行的 并完美地定位了IKDR进行这项研究。我们坚信，新兴数据科学的使用 方法和我们以前开发的算法在KD的上下文中，而不是专注于MIS-C 患者本身，将促进我们对MIS-C和KD的病因和病理生理学的理解 更快地导致了针对MIS-C患者的数据驱动管理协议的出现。

Summary – Since the SARS-CoV-2 pandemic began, the emergence of an associated novel multisystem inflammatory syndrome in children (MIS-C) has been reported. Interestingly, patients with MIS-C follow a presentation, management and clinical course that are somewhat similar to that of patients with Kawasaki disease (KD). Currently, the reason for such an overlap in clinical features and management is unclear and whether this overlap is the result of a partially shared etiology or pathophysiology is the subject of fierce debates. The degree of overlap implies that some of the clinical prediction tools that we have developed in the past for KD could be repurposed to accelerate the development of clinical support decision tools for MIS-C. In this study, we will first (R61 component) systematically address the overlap between KD and MIS-C and create salient machine-learning based prediction models for diagnosis/identification (Aim #1), management (Aim #2), and short- and long-term outcomes (Aim #3) of MIS-C based on our previously developed predictive models for KD in a process akin to transfer learning. Secondly (R33 component), we will validate and evaluate the performance and clinical utility of these models in a predictive clinical decision support system for the diagnosis and management of pediatric patients presenting with features indicative of either MIS-C or KD. In this study we will include 3 groups of patients: 1) patients with SARS-CoV-2 infection with MIS-C (CDC criteria) regardless of whether they have overlapping signs of KD, 2) patients with SARS-CoV-2 infection investigated for but eventually not diagnosed with MIS-C, and 3) patients with KD but without SARS-CoV-2 infection. Targeted data will be collected from enrolled patients (900 for training and 450 for validation) for deep phenotyping and biomarker measurements. Physician feedback on the predictions generated by the algorithm will be used to establish clinical utility. Data required for model training will be accrued in the first two years of activity (R61 period of the grant); the development of algorithms and their internal validation will occur concurrently. In the following 2 years (R33 period of the grant), we will perform external validation, establish clinical utility, add real- time epidemiological surveillance data to the models and finally package, and certify the algorithms for future deployment and for the integration in electronic health records. This project will be a collaboration with the International Kawasaki Disease Registry (IKDR) Consortium. The IKDR Consortium has an active KD and pediatric COVID registry in 35 sites across the world and the number of sites is currently expanding to 60+ sites. More than 600 MIS-C patients have already been identified at IKDR centers, making this project clearly feasible and perfectly positioning IKDR to perform this study. We strongly believe that the use of emerging data science methods and of our previously developed algorithms in the context of KD, as opposed to focusing on MIS-C patients alone, will boost our understanding of the etiology and pathophysiology of both MIS-C and KD and will more rapidly lead to the emergence of data-driven management protocols for patients with MIS-C.