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)
基本信息
- 批准号:10320999
- 负责人:
- 金额:$ 78.38万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-01-01 至 2022-11-30
- 项目状态:已结题
- 来源:
- 关键词:2019-nCoVAddressAffectAlgorithmsBenignBiological MarkersCOVID-19 pandemicCOVID-19 patientCenters for Disease Control and Prevention (U.S.)ChildChildhoodClinicalClinical DataClinical Decision Support SystemsClinical ManagementCollaborationsComplexConsultationsDataData CollectionData ScienceDecision Support SystemsDevelopmentDiagnosisDiseaseElectronic Health RecordEnrollmentEpidemiologic MonitoringEpidemiologyEtiologyEvaluationFeedbackFunctional disorderFutureGrantHeart DiseasesImageInflammatoryInternationalInvestigationKnowledgeLeadManagement Decision Support SystemsMeasurementMethodsModelingMucocutaneous Lymph Node SyndromeMultisystem Inflammatory Syndrome in ChildrenNewly DiagnosedOutcomePatientsPerformancePhasePhysiciansPositioning AttributeProbabilityProcessProtocols documentationRecording of previous eventsRegistriesReportingResearch PersonnelResource-limited settingRiskSARS-CoV-2 exposureSARS-CoV-2 infectionSigns and SymptomsSiteSyndromeSystemTestingTimeTrainingValidationVascular DiseasesWorkadverse outcomealgorithm developmentapplication programming interfacebaseclinical decision supportclinical developmentclinical predictorscoronavirus diseasedesigndisease registryepidemiologic dataexperienceinteroperabilitylarge scale datamachine learning algorithmmachine learning predictionmedical complicationnoveloptimal treatmentspediatric patientsphenotypic biomarkerpredicting responsepredictive modelingprospectiveresponsesurveillance datatooltransfer learningtreatment response
项目摘要
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多个地点。
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。
项目成果
期刊论文数量(9)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Kawasaki Disease Arab Initiative [Kawarabi]: Establishment and Results of a Multicenter Survey.
- DOI:10.1007/s00246-022-02844-w
- 发表时间:2022-08
- 期刊:
- 影响因子:1.6
- 作者:
- 通讯作者:
Kawasaki Disease in the Time of COVID-19 and MIS-C: The International Kawasaki Disease Registry.
- DOI:10.1016/j.cjca.2023.06.001
- 发表时间:2024-01
- 期刊:
- 影响因子:6.2
- 作者:Harahsheh, Ashraf S.;Shah, Samay;Dallaire, Frederic;Manlhiot, Cedric;Khoury, Michael;Lee, Simon;Fabi, Marianna;Mauriello, Daniel;Tierney, Elif Seda Selamet;Sabati, Arash A.;Dionne, Audrey;Dahdah, Nagib;Choueiter, Nadine;Thacker, Deepika;Giglia, Therese M.;Truong, Dongngan T.;Jain, Supriya;Portman, Michael;Orr, William B.;Harris, Tyler H.;Szmuszkovicz, Jacqueline R.;Farid, Pedrom;McCrindle, Brian W.
- 通讯作者:McCrindle, Brian W.
Cardiac echocardiogram findings of severe acute respiratory syndrome coronavirus-2-associated multi-system inflammatory syndrome in children.
- DOI:10.1017/s1047951121003024
- 发表时间:2022-05
- 期刊:
- 影响因子:1
- 作者:Harahsheh AS;Krishnan A;DeBiasi RL;Olivieri LJ;Spurney C;Donofrio MT;Cross RR;Sharron MP;Frank LH;Berul CI;Christopher A;Dham N;Srinivasalu H;Ronis T;Smith KL;Kline JN;Parikh K;Wessel D;Bost JE;Litt S;Austin A;Zhang J;Sable CA
- 通讯作者:Sable CA
mRNA Coronavirus Disease 2019 Vaccine-Associated Myopericarditis in Adolescents: A Survey Study.
- DOI:10.1016/j.jpeds.2021.12.025
- 发表时间:2022-04
- 期刊:
- 影响因子:0
- 作者:Kohli U;Desai L;Chowdhury D;Harahsheh AS;Yonts AB;Ansong A;Sabati A;Nguyen HH;Hussain T;Khan D;Parra DA;Su JA;Patel JK;Ronai C;Bohun M;Freij BJ;O'Connor MJ;Rosanno JW;Gupta A;Salavitabar A;Dorfman AL;Hansen J;Frosch O;Profita EL;Maskatia S;Thacker D;Shrivastava S;Harris TH;Feingold B;Berger S;Campbell M;Idriss SF;Das S;Renno MS;Knecht K;Asaki SY;Patel S;Ashwath R;Shih R;Phillips J;Das B;Ramachandran P;Sagiv E;Bhat AH;Johnson JN;Taggart NW;Imundo J;Nakra N;Behere S;Patel A;Aggarwal A;Aljemmali S;Lang S;Batlivala SP;Forsha DE;Conners GP;Shaw J;Smith FC;Pauliks L;Vettukattil J;Shaffer K;Cheang S;Voleti S;Shenoy R;Komarlu R;Ryan SJ;Snyder C;Bansal N;Sharma M;Robinson JA;Arnold SR;Salvatore CM;Kumar M;Fremed MA;Glickstein JS;Perrotta M;Orr W;Rozema T;Thirumoorthi M;Mullett CJ;Ang JY
- 通讯作者:Ang JY
Kawasaki Disease Outcomes: It's Not Just the Heart!
川崎病的结果:不仅仅是心脏!
- DOI:10.1542/hpeds.2021-006466
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Harahsheh,AshrafS
- 通讯作者:Harahsheh,AshrafS
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Nagib Dahdah其他文献
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{{ truncateString('Nagib Dahdah', 18)}}的其他基金
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:
10733695 - 财政年份:2021
- 资助金额:
$ 78.38万 - 项目类别:
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 - 财政年份:2021
- 资助金额:
$ 78.38万 - 项目类别:
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:
10272448 - 财政年份:2021
- 资助金额:
$ 78.38万 - 项目类别:
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