Center for Undiagnosed Diseases at Stanford Administrative Supplement
斯坦福大学未确诊疾病中心行政增刊
基本信息
- 批准号:10677455
- 负责人:
- 金额:$ 45.32万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-14 至 2023-04-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAdministrative SupplementAdvocacyBiological AssayCaringClinicalCohort StudiesCollaborationsDataData AnalysesDiagnosisDiagnosticDiseaseEnrollmentEvaluationFamilyFamily memberFoundationsFundingGenesGenetic CounselingGenomicsGoalsGrantHospitalsInsuranceLanguageLettersLiteratureMedicalMedical RecordsMethodsMissionModelingMolecular DiagnosisMultilingualismParticipantPatient CarePatient advocacyPatientsPeer GroupPhasePhenotypePhilanthropic FundPopulationProcessProviderRare DiseasesRecording of previous eventsResearchResearch ActivityResourcesSamplingSiteStandardizationSystemTechnologyTestingTimeTranslational ResearchTravelTriageUnderinsuredUnderrepresented PopulationsVisitWorkaccurate diagnosisbaseclinical diagnosisclinical practiceclinical research sitecomputerized toolscostdata integrationdata sharingexome sequencingexperiencegene functiongenome sequencinggenomic dataimprovedmembermetabolomicsmultiple omicsnovelnovel strategiesoutreachparticipant enrollmentpatient engagementphenotypic datapreservationprobandprogramsrecruitresearch clinical testingsafety netsample collectionstem cell biologytechnology developmenttelehealthtranscriptomics
项目摘要
ABSTRACT
Although gene panels and exome and genome sequencing (GS) have enabled molecular diagnosis for many
rare disease patients, a significantly large proportion of these patients remain undiagnosed. A combination of
clinical expertise, advanced genomic and multi-omic technologies and efficient computational tools is needed
for higher diagnostic yield. Lack of insurance or denial of test reimbursement by insurance further negatively
impacts rare disease patients from underprivileged and economically weak backgrounds, making it critical to
improve the outreach and diversity in rare disease study cohorts. The Center for Undiagnosed Diseases (CUD)
at Stanford will continue our efforts toward sustainability, refinement of methods, and integration with regular
clinical practice. Here, we propose a program of study that will (1) facilitate accurate diagnosis of patients with
undiagnosed diseases, with emphasis on those without or with limited insurance or economical or language
barriers; (2) use novel approaches in data analysis and integration of different ‘omes to improve diagnostic
rates; and (3) enhance our understanding of the impact of diversity on the diagnostic process. In Aim 1, we
propose to triage and enroll patients, complete clinical evaluations and associated work. This will include pre-
visit chart review and genetic counseling followed by an individualized visit during which standardized
phenotypic and environmental data are collected. Biosamples facilitate genomic, multi-omic, and cellular
evaluation of disease. We will apply novel computational approaches for systematic integration of genomic,
transcriptomic, metabolomic, and phenotypic data with the entire medical literature to improve diagnostic yield.
We will continue our efforts to seek diagnosis using available resources for all participants. In Aim 2, we will
focus on increasing participant diversity, engagement, and advocacy. This will be accomplished by expanding
our local patient advocacy partnerships, including a local UDN PEER group. We aim to increase applications
from underrepresented and underinsured patients through regional outreach to safety net hospital systems and
providers. Further, our streamlined evaluations will increase access to underserved participants. Finally, we will
prioritize onboarding multilingual staff to enhance participant experience.
摘要
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Caring for the Caregivers - Covid-19 Vaccination for Essential Members of the Health Care Team.
- DOI:10.1056/nejmpv2101339
- 发表时间:2021-03-04
- 期刊:
- 影响因子:0
- 作者:Halley MC;Mangurian C
- 通讯作者:Mangurian C
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Euan A Ashley其他文献
Artificial Intelligence in Molecular Medicine. Reply.
分子医学中的人工智能。
- DOI:
10.1056/nejmc2308776 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Bruna Gomes;Euan A Ashley - 通讯作者:
Euan A Ashley
Prediction of diagnosis and diastolic filling pressure by AI-enhanced cardiac MRI: a modelling study of hospital data.
通过人工智能增强心脏 MRI 预测诊断和舒张充盈压:医院数据的建模研究。
- DOI:
10.1016/s2589-7500(24)00063-3 - 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
D. Lehmann;Bruna Gomes;Niklas Vetter;Olivia Braun;Ali Amr;Thomas Hilbel;Jens Müller;Ulrich Köthe;Christoph Reich;E. Kayvanpour;F. Sedaghat;Manuela Meder;J. Haas;Euan A Ashley;Wolfgang Rottbauer;D. Felbel;Raffi Bekeredjian;H. Mahrholdt;Andreas Keller;P. Ong;Andreas Seitz;H. Hund;N. Geis;F. André;Sandy Engelhardt;Hugo A Katus;Norbert Frey;Vincent Heuveline;Benjamin Meder - 通讯作者:
Benjamin Meder
Euan A Ashley的其他文献
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{{ truncateString('Euan A Ashley', 18)}}的其他基金
Diagnosing the Unknown for Care and Advancing Science (DUCAS)
诊断未知的护理和推进科学 (DUCAS)
- 批准号:
10682163 - 财政年份:2023
- 资助金额:
$ 45.32万 - 项目类别:
Diagnosing the Unknown for Care and Advancing Science (DUCAS)
诊断未知的护理和推进科学 (DUCAS)
- 批准号:
10872436 - 财政年份:2023
- 资助金额:
$ 45.32万 - 项目类别:
Systematically mapping variant effects for cardiovascular genes
系统地绘制心血管基因的变异效应
- 批准号:
10501975 - 财政年份:2022
- 资助金额:
$ 45.32万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
10083762 - 财政年份:2020
- 资助金额:
$ 45.32万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
10576926 - 财政年份:2020
- 资助金额:
$ 45.32万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
9884435 - 财政年份:2020
- 资助金额:
$ 45.32万 - 项目类别:
Structure function relationships from deep mutational scanning in human cardiomyopathy
人类心肌病深度突变扫描的结构功能关系
- 批准号:
10364603 - 财政年份:2020
- 资助金额:
$ 45.32万 - 项目类别:
What comes next? Engaging stakeholders in governance of participant data and relationships during the sunset of large genomic medicine research initiatives
接下来是什么?
- 批准号:
10162151 - 财政年份:2018
- 资助金额:
$ 45.32万 - 项目类别:
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