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

• 批准号: 10477176
• 负责人: JAMES W SCHILLING
• 金额: $ 34.59万
• 依托单位: HBI SOLUTIONS INC.
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/10477176
• 关键词: Acute; Address; Age; Algorithms; Aneurysm; Automation; Big Data; Big Data Methods; Biological Markers; Blood; Boston; Businesses; Cardiovascular system; Caring; Cessation of life; Child; Childhood; Clinical; Colorado; Communities; Computer Assisted; Coronary Aneurysm; County; Data; Data Set; Data Sources; Decision Making; Diagnosis; Diagnostic tests; Disease; Disease Management; Echocardiography; Electronic Health Record; Environment; Ethnic Origin; Ethnic group; Evaluation; Fever; Goals; Gold; Health; Health Personnel; Healthcare; Heart Diseases; Illness Days; Incidence; Infection; Inflammatory; International; Intervention; Intravenous Immunoglobulins; Knowledge; Laboratories; Life; Long Island; Longterm Follow-up; Medical; Medicine; Modeling; Mucocutaneous Lymph Node Syndrome; Myocardial Infarction; New York City; Oranges; Outcome; Patient Care; Patient risk; Patients; Pattern; Pediatric Hospitals; Performance; Phase; Population; Population Heterogeneity; Precision Health; Predictive Analytics; Process; Provider; Public Health; Race; Risk; Risk Factors; Screening procedure; Small Business Technology Transfer Research; System; Taiwan; Testing; Time; Translating; Translations; Universities; Update; Validation; Visit; accurate diagnosis; base; billing data; clinical decision-making; cloud based; cohort; commercialization; connected care; cost; data infrastructure; data warehouse; diagnostic accuracy; differential expression; disease diagnostic; economic determinant; empowered; ethnic diversity; genomic data; improved; individual patient; innovation; mortality; patient screening; point of care; population health; screening; social; social determinants; statistical learning; structured data; tool; tv watching

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（KD）是导致获得性心脏病的最常见原因。 儿童静脉注射免疫球蛋白（IVIG）治疗可降低冠心病的发病率。 建议使用 IVIG 治疗动脉瘤和长期心血管并发症的风险。 患病后 10 天内进行诊断；但只有 4.7% 的患者在第一次时就得到了正确的诊断 及时、准确地诊断川崎病至关重要，但尚无黄金标准。 诊断测试的一个挑战是川崎病的临床症状与其他疾病的症状重叠。 我们之前通过临床和实验室应用统计学习。 测试变量以区分 KD 和发热性疾病，并在五个阶段验证了该算法 美国儿童医院的结果显示了其作为计算机辅助工具的潜力。 在无法轻易获得超声心动图的情况下在护理点做出决策 在翻译和商业化之前，需要对算法进行验证。 庞大、多样化的人群，并作为实时集成到患者监测平台中 在这个项目中，我们提出了三个具体目标。 解决了中心假设，即 KD 筛选工具结合了我们之前确定的 电子健康记录 (EHR) 中新发现的患者级别变量可以区分 新州不同种族儿童群体中临床相似发热性疾病导致的川崎病 我们将与美国最大的公共卫生信息公司 Healthix 合作。 与纽约市超过 1600 万患者的数据进行交换（HIE） 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 仓库 在目标 2 中，我们将识别患者之间差异表达的特征。 患有川崎病和其他二月疾病的患者，并开发一种改进的算法 最后，我们将把 KD 与其他发热性疾病区分开来。 Spotlight 解决方案。Spotlight 解决方案包括一个具有高功能的医疗保健监控平台。 我们期望能够为提供商提供人工智能解决方案的能力数据基础设施和风险引擎。 最终，基于 HIE 的儿科 KD 评估系统将准备好提醒 HIE 参与 提供者对长期心血管疾病进行及时评估、治疗和随访 纽约市和其他社区的后遗症。

项目成果

Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.

在台湾对美国多中心验证的川崎病诊断算法进行单中心盲测。

• 发表时间: 2022
• 作者: Kuo, Ho;Hao, Shiying;Jin, Bo;Chou, C James;Han, Zhi;Chang, Ling;Huang, Ying;Hwa, Kuoyuan;Whitin, John C;Sylvester, Karl G;Reddy, Charitha D;Chubb, Henry;Ceresnak, Scott R;Kanegaye, John T;Tremoulet, Adriana H;Burns, Jane C
• 通讯作者: Burns, Jane C