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

摘要-川崎病（KD）是先天性心脏病最常见的病因， 孩子静脉注射免疫球蛋白（IVIG）可降低冠状动脉粥样硬化的发生率。 动脉瘤和长期心血管并发症的风险。IVIG建议 在发病后10天内给予;然而，只有4.7%的人在第一次接受正确的诊断 医疗访问。及时准确地诊断KD至关重要，但没有金标准 诊断测试诊断的一个挑战是KD的临床体征与其他KD的临床体征重叠。 小儿发热性疾病我们以前应用统计学习使用临床和实验室 测试变量以区分KD和发热性疾病，并在五个实验中验证了该算法。 美国的儿童医院。结果表明，它的潜力是一个计算机辅助工具， 在无法轻易进行超声心动图的环境中，在护理点做出决策 available.在翻译和商业化之前，该算法需要在 大，多样化的人群，并集成到患者监测平台作为实时 筛选工具，供医疗保健提供者使用。在这个项目中，我们提出了三个具体目标， 解决了中心假设，KD筛选工具，结合我们以前确定的 电子健康记录（EHR）中新发现的患者水平变量可以区分 新研究中不同种族儿科人群中临床相似发热性疾病引起的KD 约克市（纽约市）。我们将与美国最大的公共卫生信息公司Healthix合作， HIE交换（HIE），拥有来自纽约市超过1600万患者的数据。在目标1中，我们将建立一个 来自Healthix NYC数据的KD和其他发热性疾病患者的儿科EHR数据库 源在目标2中，我们将确定患者之间差异表达的特征 KD和其他发热性疾病患者，并开发一种改进的算法， 将KD与其他发热性疾病区分开来。最后，我们将把算法集成到HBI中 聚光灯解决方案。Spotlight解决方案包括一个医疗保健监控平台， 能力数据基础设施和风险引擎，为供应商提供人工智能解决方案。我们预计 最终，基于HIE的儿科KD评估系统将准备好提醒HIE参与 为长期心血管疾病患者提供及时的评估、治疗和随访 在纽约和其他社区的后遗症。

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.