An automated system to differentiate Kawasaki disease from febrile illness with real life clinical datasets in New York City

利用纽约市真实临床数据集区分川崎病和发热性疾病的自动化系统

基本信息

  • 批准号:
    10477176
  • 负责人:
  • 金额:
    $ 34.59万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-09-01 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

ABSTRACT – Kawasaki disease (KD) is the most common cause of acquired heart disease in children. Treatment with intravenous immunoglobulin (IVIG) reduces the incidence of coronary aneurysms and risk of long-term cardiovascular complications. IVIG is recommended to be given within 10 days of illness; however only 4.7% receive the correct diagnosis at the first medical visit. Timely and accurately diagnosis of KD is critical, yet there isn’t a gold standard diagnostic test. A challenge of diagnosis is that the clinical signs of KD overlap those of other pediatric febrile illnesses. We previously applied statistical learning using clinical and laboratory test variables to differentiate KD from febrile illnesses and validated the algorithm in five children’s hospitals in the US. Results showed its potential of being a computer-assist tool of decision making at point of care in the settings where echocardiography would not be readily available. Before translation and commercialization, the algorithm needs to be validated in a large, diverse population and integrated into a patient surveillance platform as a real-time screening tool for healthcare providers to use. In this project, we propose three specific aims to address the central hypothesis that a KD screening tool incorporating our previously identified and newly found patient-level variables in the electronic health record (EHR) can differentiate KD from clinically similar febrile illnesses in an ethnically diverse pediatric population in New York City (NYC). We will collaborate with Healthix, the nation’s largest public health information exchange (HIE) with data of over 16 million patients from NYC. In Aim 1, we will set up a pediatric EHR warehouse of patients with KD and other febrile illnesses from Healthix NYC data sources. In Aim 2, we will identify features that are differentially expressed between patients with KD and patients with other febrile illnesses, and develop an improved algorithm to differentiate KD from other febrile illnesses. Finally, we will integrate the algorithm into the HBI Spotlight Solutions. The Spotlight Solutions include a healthcare surveillance platform with high- capacity data infrastructure and risk engines to offer AI solutions to providers. We expect ultimately an HIE-based pediatric KD assessment system will be ready to alert HIE participating providers for timely evaluation, treatment and follow up for the long-term cardiovascular sequelae in NYC and other communities.
摘要川崎病(Kawasaki disease, KD)是获得性心脏病最常见的病因

项目成果

期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.
  • DOI:
    10.3389/fimmu.2022.1031387
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    7.3
  • 作者:
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JAMES W SCHILLING其他文献

JAMES W SCHILLING的其他文献

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{{ truncateString('JAMES W SCHILLING', 18)}}的其他基金

An automated system to interpret echocardiography to predict adverse outcomes in patients with right ventricular dysfunction in daily hospital practice
一种解释超声心动图的自动化系统,以预测日常医院实践中右心室功能障碍患者的不良后果
  • 批准号:
    10326000
  • 财政年份:
    2021
  • 资助金额:
    $ 34.59万
  • 项目类别:
ACQUISITION OF DNA SYNTHESIZER & PROTEIN SEQUENCER
收购 DNA 合成器
  • 批准号:
    3872112
  • 财政年份:
  • 资助金额:
    $ 34.59万
  • 项目类别:

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