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.

概括 先天性异常在全球范围内造成了日益沉重的疾病负担，导致数百万人出生 与缺陷相关的残疾对中低收入国家 (LIMC) 造成不成比例的影响。许多 具有遗传性病因，由于缺乏诊断手段，无法做出确诊 大多数中低收入国家的技术。无法快速准确地诊断携带某种遗传基因的个体 综合征会增加死亡和发病的风险（因为许多可控制的先天性异常可能会增加死亡率） 被隐藏，例如先天性心脏病或听力感染）并妨碍准确确定 患病率，对于公共卫生监测和干预计划至关重要。 该项目的第一部分 (R21) 使用两种协同移动健康干预工具来解决这些差距 筛查综合症并具体证明唐氏综合症的特定诊断 （可扩展到所有非整倍体以及由拷贝数变异、点突变和 在刚果民主共和国，可以用最少的资源进行插入/删除 （刚果民主共和国）。 目标 1 将是训练和验证人工智能引导的基于智能手机的技术来筛查综合症 目标 2 将在刚果民主共和国建立低成本、快速的初始基因诊断能力。 在提案的扩展部分（R33）中，我们将测试是否实施注册表测量 健康结果可以用作未来新生儿筛查和健康监测的可扩展模型 低资源设置。为此，目标 3 将建设出生缺陷检测、遗传 资源匮乏条件下的确认、能力建设以及实践和结果监测 有两个并行的子目标：目标 3a 将评估大规模诊断能力的可行性 人口样本并提供衡量特定健康结果的工具，而目标 3b 将建立一个 形态学、遗传和健康结果数据的小规模功能数据库/注册表。 在资源有限的情况下，建立全面的系统来检测、转诊、治疗和监测患有此类疾病的个人 先天性异常是不存在的。我们的创新技术将弥补这一差距，建设当地能力 诊断筛查和数据登记，允许早期诊断和针对具体情况的护理，可能 降低患有非传染性疾病的儿童的发病率和死亡率。