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; 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）是在 孩子们。用静脉免疫球蛋白（IVIG）治疗可降低冠状动脉的发生率 动脉瘤和长期心血管并发症的风险。建议ivig成为 在生病的10天内给予；但是，第一次只有4.7％的人接受正确的诊断 医疗访问。 KD的及时，准确诊断至关重要，但没有黄金标准 诊断测试。诊断的一个挑战是KD的临床迹象重叠了其他 小儿发热疾病。我们以前使用临床和实验室应用统计学习 测试变量将KD与发热疾病区分开，并在五个中验证了该算法 美国的儿童医院。结果表明，它可能成为计算机协助工具的潜力 在不容易超声心动图的设置中，在护理点进行决策 可用的。在翻译和商业化之前，需要在A中验证该算法 大量的潜水员人口，并将其纳入患者监视平台作为实时 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KD筛选工具纳入我们先前确定的中心假设 电子健康记录（EHR）中新发现的患者级变量可以区分 在新的小儿种群中，来自临床上相似的高温疾病的KD 约克市（纽约）。我们将与美国最大的公共卫生信息Healthix合作 与纽约市超过1600万患者的数据交换（HIE）。在AIM 1中，我们将建立一个 KD患者的小儿EHR仓库和其他Healthix NYC数据的发热疾病 来源。在AIM 2中，我们将确定患者之间不同表达的功能 KD和患有其他发热疾病的患者，并将改进的算法发展为 区分KD与其他高温疾病。最后，我们将将算法集成到HBI中 聚光解决方案。聚光灯解决方案包括一个具有高度的医疗保健监视平台 容量数据基础架构和风险引擎向提供商提供AI解决方案。我们期望 最终，基于HIE的儿科KD评估系统将准备好提醒HIE参与 提供及时评估，治疗和跟进的提供者长期心血管 纽约市和其他社区的后遗症。