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)是中国获得性心脏病最常见的原因。 孩子们。静脉注射免疫球蛋白(IVIG)治疗可降低冠状动脉病变的发生率 动脉瘤和长期心血管并发症的风险。建议将IVIG 在患病后10天内进行；然而，只有4.7%的人在第一次诊断时就得到了正确的诊断 去看医生。及时、准确地诊断KD是至关重要的，但没有黄金标准 诊断性测试。诊断的一个挑战是KD的临床症状与其他 儿科发热性疾病。我们以前使用临床和实验室进行统计学习。 测试变量以区分KD和发热性疾病，并在5个月内验证算法 美国的儿童医院。结果表明，它有可能成为一种计算机辅助工具 在超声心动图不容易的环境中的护理点做出决定 可用。在翻译和商业化之前，该算法需要在 庞大、多样化的人群，并以实时方式集成到患者监控平台中 供医疗保健提供者使用的筛查工具。在这个项目中，我们提出了三个具体目标 解决了核心假设，即KD筛查工具结合了我们之前确定的 在电子健康记录(EHR)中新发现的患者级别变量可以区分 在纽约不同种族的儿科人群中临床相似发热性疾病所致的KD 纽约市(NYC)。我们将与美国最大的公共卫生信息Healthix合作 与来自纽约市的1600多万名患者的数据进行交换(HIE)。在目标1中，我们将设置一个 来自Healthix NYC数据的KD和其他发热性疾病患者的儿科EHR仓库 消息来源。在目标2中，我们将识别患者之间差异表达的特征 KD和其他发热性疾病的患者，并开发一种改进算法来 KD与其他发热性疾病的鉴别。最后，我们将算法集成到HBI中 Spotlight解决方案。Spotlight解决方案包括一个医疗监控平台，该平台具有 容量数据基础设施和风险引擎，为提供商提供人工智能解决方案。我们预计 最终，基于HIE的儿科KD评估系统将准备好向HIE参与者发出警报 为长期心血管疾病提供及时的评估、治疗和随访 在纽约市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.