Health Disparities of Patients with Fragile X from Diverse Racial & Ethnic Groups

不同种族的脆性 X 细胞患者的健康差异

• 批准号: 10645599
• 负责人: AREZOO MOVAGHAR
• 金额: $ 7.78万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-06 至 2023-11-13
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10645599
• 关键词: Acceleration; Age; Area; Artificial Intelligence; Biometry; Birth; Caring; Characteristics; Chicago; Clinic; Clinical; Clinical Data; Clinical Research; Code; Complex; Data; Development; Diagnosis; Diagnostic; Disease; Electronic Health Record; Epidemiology; Ethnic Origin; Ethnic Population; Face; Family; Female; Fragile X Syndrome; Frequencies; General Population; Genetic; Goals; Guidelines; Health; Health system; Healthcare Systems; Heterogeneity; Hispanic; Individual; Inherited; Institution; Intellectual functioning disability; Investigation; Knowledge; Letters; Link; Machine Learning; Medical; Medical History; Meta-Analysis; Modeling; Not Hispanic or Latino; Outcomes Research; Patient advocacy; Patient-Focused Outcomes; Patients; Pattern; Pediatrics; Penetrance; Personal Satisfaction; Phenotype; Physicians; Population; Population Study; Prevalence; ROC Curve; Race; Recommendation; Research; Sampling; Scientist; Screening procedure; Site; Social Sciences; Source; Stress; Subgroup; Symptoms; Techniques; Testing; Time; Translational Research; Universities; Variant; Wisconsin; autism spectrum disorder; biomedical informatics; burden of illness; case-based; clinical diagnosis; clinical practice; clinical risk; developmental psychology; ethnic diversity; experience; genetic disorder diagnosis; genetic testing; health care service utilization; health disparity; improved; innovation; insight; male; medical specialties; metropolitan; multidisciplinary; novel; novel strategies; patient population; patient subsets; predictive modeling; public health priorities; racial diversity; racial population; screening; sex; success; tool

Project Summary Fragile X syndrome (FXS) is the most prevalent monogenic cause of intellectual disability and autism. Clinical studies on patients being seen in specialized clinics have shown that this X-linked disorder has a substantial impact on the health and well-being of patients and families. FXS remains significantly underdiagnosed despite increased emphasis on identification of individuals with FXS, patient advocacy, and accessibility of genetic testing. The impact of this condition across racial and ethnic groups is unknown and the rate and prevalence of co-occurring conditions has not yet been characterized using representative populations. An unmet need exists to develop novel pre-screening approaches that are able to alert physicians to potentially undiagnosed cases and refer them for genetic testing. This application proposes research that will not only advance knowledge of clinical risk associated with FXS but will also offer insight into development of innovative pre-screening approaches that can improve diagnostic practices for other complex underdiagnosed conditions. The present research will be the largest population-level study of health characteristics of individuals with FXS from diverse racial and ethnic patient groups. The electronic health records (EHRs) from more than 15 million patients in CAPriCORN (the Chicago Area Patient-Centered Outcomes Research Network) will be used for this study. CAPriCORN includes 11 different health care systems/sites (with one FXS Specialized Clinic) in metropolitan Chicago. 1008 patients have been identified who have a diagnostic code for FXS in their EHRs (318 females, 690 males) of whom 293 are Non-White, 110 are Hispanic, and the others are non- Hispanic Whites. By including patients from diverse racial and ethnic groups, and by utilizing discovery-oriented approaches and artificial intelligence (AI) techniques, our proposed research will evaluate phenotypic characteristics of individuals with FXS more representative of the US population. Our Specific Aims are: (1) To detect the rate of FXS diagnosis (and under-diagnosis) in patients from diverse racial and ethnic groups; (2) To characterize the diagnostic patterns in FXS patients from diverse racial and ethnic groups; and (3) To construct an AI-assisted pre-screening tool to find potential cases with FXS who have not yet been diagnosed and validate the tool with racially and ethnically diverse patient groups. Additionally, we will investigate the impact of access to specialty clinics by characterizing the differences/similarities in age of diagnosis, diagnostic odyssey, and health care utilization between patients served by the Fragile X Clinic at Rush University and patients from other CAPriCORN sites. We have organized a multidisciplinary team of scientists with expertise in biostatistics, biomedical informatics, genetics, pediatrics, developmental psychology, epidemiology, and social science. With substantial experience in using EHR data for clinical and translational research, and with a long successful track record of conducting collaborative multi-institutional studies on FXS, our team has exceptional ability to achieve these research goals.

项目概要 脆性 X 综合征 (FXS) 是智力障碍和自闭症最常见的单基因原因。 对在专门诊所就诊的患者进行的临床研究表明，这种 X 连锁疾病具有 对患者及其家人的健康和福祉产生重大影响。 FXS 仍然显着 尽管越来越重视 FXS 患者的识别、患者宣传和 基因检测的可及性。这种情况对种族和族裔群体的影响尚不清楚，而且 尚未使用代表性人群来描述同时发生的病症的发生率和患病率。 开发新的预筛选方法的需求尚未得到满足，这些方法能够提醒医生潜在的潜在风险 未确诊病例并将其转介进行基因检测。该申请提出的研究不仅 增进对 FXS 相关临床风险的了解，但也将提供对创新开发的见解 预筛查方法可以改善其他复杂的未诊断疾病的诊断实践。 本研究将是针对患有以下疾病的个体健康特征的最大的人群水平研究 FXS 来自不同种族和民族的患者群体。来自超过 15 个国家的电子健康记录 (EHR) CAPriCORN（芝加哥地区以患者为中心的结果研究网络）的 100 万名患者将被使用 对于这项研究。 CAPriCORN 包括 11 个不同的医疗保健系统/站点（包括 1 个 FXS 专业诊所） 大都市芝加哥。已确定 1008 名患者体内有 FXS 诊断代码 EHR（318 名女性，690 名男性），其中 293 名非白人，110 名西班牙裔，其余为非白人 西班牙裔白人。通过纳入来自不同种族和族裔群体的患者，并利用以发现为导向的 方法和人工智能（AI）技术，我们提出的研究将评估表型 FXS 患者的特征更能代表美国人口。 我们的具体目标是： (1) 检测不同地区患者的 FXS 诊断率（和诊断不足率） 种族和族裔群体； (2) 描述来自不同种族和地区的 FXS 患者的诊断模式特征 族裔群体； (3) 构建人工智能辅助预筛查工具，以发现患有 FXS 的潜在病例 尚未对不同种族和民族的患者群体进行诊断和验证该工具。此外，我们 将通过描述年龄的差异/相似性来调查进入专科诊所的影响 Rush 的 Fragile X 诊所服务的患者之间的诊断、诊断奥德赛和医疗保健利用 大学和来自其他 CAPriCORN 站点的患者。我们组织了一个多学科的科学家团队 拥有生物统计学、生物医学信息学、遗传学、儿科、发展心理学、 流行病学和社会科学。拥有将 EHR 数据用于临床和转化的丰富经验 研究，并拥有对 FXS 进行多机构合作研究的长期成功记录， 我们的团队具有实现这些研究目标的卓越能力。