Mobile Diagnosis of Congenital Genetic Conditions: A Model for Screening and Surveillance in Low-Resource Settings

先天性遗传病的移动诊断：资源匮乏环境下的筛查和监测模型

• 批准号: 10267068
• 负责人: Marius George Linguraru
• 金额: $ 40.95万
• 依托单位: CHILDREN'S RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10267068
• 关键词: Accounting; Address; Affect; Aneuploidy; Appearance; Artificial Intelligence; Biometry; Birth Rate; Capillary Electrophoresis; Caring; Cellular Phone; Cheek structure; Child; Child Mortality; Competence; Computer software; Computers; Congenital Abnormality; Congenital Heart Defects; Copy Number Polymorphism; Country; Coupled; DNA sequencing; Data; Databases; Democratic Republic of the Congo; Detection; Development; Diagnosis; Diagnostic; Disease; Down Syndrome; Early Diagnosis; Environment; Environmental Risk Factor; Ethics; Face; Family; Fellowship; Future; General Population; Genetic; Genetic Diseases; Genetic Materials; Genetic Predisposition to Disease; Health; Health Personnel; Healthcare; Hearing; High Prevalence; Hospitals; Income; Individual; Infection; Infrastructure; International; Intervention; Laboratories; Machine Learning; Materials Testing; Measures; Modeling; Morbidity - disease rate; Morphology; Neonatal Screening; Newborn Infant; Other Genetics; Outcome; Patient risk; Phenotype; Photography; Physicians; Pilot Projects; Point Mutation; Population; Population Heterogeneity; Population Surveillance; Prevalence; Public Health; Publications; Registries; Research Institute; Resources; Sampling; Scientist; Services; Societies; Students; Surveillance Program; Swab; Syndrome; System; Technology; Testing; Time; Training; Washington; accurate diagnosis; base; burden of illness; computerized; congenital anomaly; cost; data acquisition; data registry; design; diagnostic screening; diagnostic technologies; disability; facial recognition software; follow-up; genetic disorder diagnosis; genetic resource; global health; health care quality; health care service; improved; innovation; innovative technologies; insertion/deletion mutation; intervention program; mHealth; mortality risk; neonatal health; novel; novel strategies; nutrition; point of care; prevent; programs; rapid diagnosis; screening; smartphone Application; standard of care; tool; transposon/insertion element

SUMMARY Congenital anomalies represent an increasing burden of disease worldwide, accounting for millions of birth defect-related disabilities with a disproportionate impact on Low to Middle Income Countries (LIMCs). Many harbor genetic etiologies, for which no confirmatory diagnosis can be made due to the dearth of diagnostic technologies in most LMICs. The inability to rapidly and accurately diagnose individuals that harbor a genetic syndrome increases the risk of mortality and morbidity (as a number of manageable congenital anomalies may be hidden, such as congenital heart defects or hearing infections) and prevents the accurate determination of prevalence rates, critical for public health surveillance and intervention programs. The first part (R21) of this project addresses these gaps using two synergistic mobile health intervention tools to screen for syndromic conditions and specifically demonstrate that a specific diagnostic of Down syndrome (expandable to all aneuploidies and those diseases resulting from copy number variants, point mutations and insertions/deletions) can be performed with minimal resources, in the Democratic Republic of the Congo (DRC). Aim 1 will be to train and validate AI-guided smartphone-based technology to screen for syndromic conditions, while Aim 2 will create low-cost, rapid initial genetic diagnostic capacity in the DRC. In the expansion part of the proposal (R33), we will test whether the implementation of a registry measuring health outcomes can be used as a scalable model for future newborn screening and health surveillance in a low-resource setting. To this effect, Aim 3 will build infrastructure for birth defects detection, genetic confirmation, competence building, and practice and outcomes surveillance in low-resource conditions with two parallel sub-aims: Aim 3a will assess the feasibility of the diagnostic capacity on a large population sample and provide a tool to measure specific health outcomes, while Aim 3b will establish a small-scale and functional database/registry of morphological, genetic, and health outcomes data. In limited resources settings, comprehensive systems to detect, refer, treat and surveil individuals with congenital anomalies are non-existent. Our innovative technologies will address this gap, build local capacity of diagnostic screening and of a data registry, allowing for early diagnosis and condition-specific care, likely to lower morbidity and mortality of children with non-communicable syndromic conditions.

总结 先天性异常是全世界日益增加的疾病负担， 与缺陷有关的残疾对中低收入国家（LIMCs）的影响不成比例。许多 隐藏遗传病因，由于缺乏诊断性的方法，无法进行确证性诊断。 大多数中低收入国家的技术。无法快速准确地诊断携带基因缺陷的个体， 综合征增加了死亡率和发病率的风险（因为许多可控制的先天性异常可能 隐藏，如先天性心脏病或听力感染），并阻止准确确定 流行率，对公共卫生监测和干预计划至关重要。 该项目的第一部分（R21）使用两种协同的移动的健康干预工具来解决这些差距 筛查综合征并明确证明唐氏综合征的特异性诊断 （可扩展到所有非整倍性和由拷贝数变异、点突变和突变引起的那些疾病） 插入/删除）可以用最少的资源进行，在刚果民主共和国 （刚果民主共和国） 目标1将是训练和验证基于人工智能引导的智能手机技术，以筛查综合征。 目标2将在刚果民主共和国建立低成本、快速的初步基因诊断能力。 在提案的扩展部分（R33），我们将测试注册表测量的实施是否 健康结果可用作未来新生儿筛查和健康监测的可扩展模型， 低资源配置。为此，目标3将建立出生缺陷检测、遗传学和生物医学基础设施。 确认，能力建设，以及在低资源条件下的实践和结果监督 有两个平行的子目标：目标3a将评估诊断能力的可行性， 人口抽样，并提供一个工具来衡量具体的健康结果，而目标3b将建立一个 小规模和功能性数据库/形态学、遗传学和健康结果数据登记处。 在资源有限的情况下，全面的系统，以发现，转介，治疗和监测个人与 先天性异常是不存在的我们的创新技术将解决这一差距， 诊断筛查和数据登记，允许早期诊断和特定条件的护理，可能 降低患有非传染性综合症的儿童的发病率和死亡率。