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

一种数据科学方法，用于识别和管理与儿科患者 SARS-CoV-2 感染和川崎病相关的儿童多系统炎症综合征 (MIS-C)

• 批准号: 10847802
• 负责人: Nagib Dahdah
• 金额: $ 151.67万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10847802
• 关键词: 2019-nCoV; Acceleration; Address; Affect; Algorithms; Benign; Biological Markers; COVID-19 pandemic; COVID-19 patient; Certification; Child; Childhood; Clinical; Clinical Data; Clinical Decision Support Systems; Clinical Management; Collaborations; Complex; Consultations; Data; Data Collection; Data Science; Decision Support Systems; Development; Diagnosis; Disease; Electronic Health Record; Enrollment; Epidemiologic Monitoring; Epidemiology; Etiology; Evaluation; Feedback; Functional disorder; Future; Grant; Heart Diseases; Image; Inflammatory; International; Investigation; Knowledge; Lead; Management Decision Support Systems; Measurement; Methods; Modeling; Mucocutaneous Lymph Node Syndrome; Multisystem Inflammatory Syndrome in Children; Newly Diagnosed; Outcome; Patients; Performance; Phase; Phenotype; Physicians; Positioning Attribute; Probability; Process; Protocols documentation; Recording of previous events; Registries; Reporting; Research Personnel; Resource-limited setting; Risk; SARS-CoV-2 exposure; SARS-CoV-2 infection; Signs and Symptoms; Site; Syndrome; System; Testing; Time; Training; Validation; Vascular Diseases; Work; adverse outcome; algorithm development; application programming interface; clinical decision support; clinical development; clinical predictors; coronavirus disease; design; disease registry; epidemiologic data; experience; interoperability; large scale data; machine learning algorithm; machine learning prediction; medical complication; novel; optimal treatments; participant enrollment; pediatric patients; predicting response; predictive modeling; predictive tools; prospective; response; surveillance data; tool; transfer learning; treatment response

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.

摘要-自从SARS-CoV-2大流行开始以来，相关的新的多系统 儿童炎症综合征（MIS-C）。有趣的是，MIS-C患者遵循一个 与川崎患者的临床表现、治疗和临床过程有些相似 疾病（KD）。目前，临床特征和管理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。重叠的程度意味着我们在研究中开发的一些临床预测工具 过去的KD可以重新利用，以加速MIS-C临床支持决策工具的开发。在 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，并创建 用于诊断/识别（目标#1）、管理（目标#2）、 以及MIS-C的短期和长期结果（目标3），基于我们先前开发的预测模型， KD的过程类似于迁移学习。其次（R33组件），我们将验证和评估 这些模型在用于诊断的预测性临床决策支持系统中的性能和临床效用 以及对表现出MIS-C或KD特征的儿科患者的管理。本研究 将包括3组患者：1）SARS-CoV-2感染MIS-C（CDC标准）的患者，无论 他们是否有KD的重叠体征，2）对SARS-CoV-2感染患者进行调查， 最终未诊断为MIS-C; 3）KD但未感染SARS-CoV-2的患者。目标数据 将从入组患者中收集（900例用于培训，450例用于验证）用于深度表型分析， 生物标志物测量。医生对算法生成的预测的反馈将用于 建立临床效用。模型培训所需的数据将在活动的头两年累积（R61 在赠款期间）;算法的开发及其内部验证将同时进行。在 2年后（R33资助期），我们将进行外部验证，建立临床效用，增加真实的- 将流行病学监测数据与模型结合，最后对模型进行封装，并对算法进行验证，以备将来使用 部署和集成到电子健康记录中。该项目将是一个合作与 国际川崎病登记（IKDR）联盟。IKDR联盟有一个活跃的KD， 在全球35个研究中心进行儿科COVID登记，研究中心的数量目前正在扩大到60多个研究中心。 IKDR中心已经确定了600多名MIS-C患者，使该项目明显可行 并完美地定位IKDR来进行这项研究。我们坚信，使用新兴的数据科学 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，将提高我们对MIS-C和KD的病因学和病理生理学的理解， 更快地导致MIS-C患者的数据驱动管理协议的出现。