SURPASS: (Statin Use and Risk Prediction of Atherosclerotic Cardiovascular Disease in minority Subgroups)
SURPASS:(少数亚组中他汀类药物的使用和动脉粥样硬化性心血管疾病的风险预测)
基本信息
- 批准号:10080751
- 负责人:
- 金额:$ 17.13万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-02-01 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdherenceAmericanAmerican Heart AssociationAreaAsiansAssessment toolAtherosclerosisBiometryCalibrationCaliforniaCardiologyCardiovascular DiseasesCardiovascular systemCause of DeathCholesterolChronic DiseaseClinicalClinical DataCodeComplexDataData ScienceDecision MakingDiagnosisDiscriminationDisease OutcomeElectronic Health RecordEnsureEpidemiologic MethodsEpidemiologyEquationEthnic groupEventFoundationsFundingFutureGoalsGuidelinesHawaiiHealthHealth Care CostsHealth Services ResearchHealth systemHealthcare SystemsHeterogeneityHispanicsHypersensitivityInterventionKnowledgeLaboratoriesLeadLifeMachine LearningMedicalMentorsMentorshipMexican AmericansMinorityMinority GroupsModelingModernizationNational Heart, Lung, and Blood InstituteNatural Language ProcessingNot Hispanic or LatinoOutcomePatientsPerformancePhysiciansPopulationPopulation HeterogeneityPreventionPrevention strategyPuerto RicanResearchResearch PersonnelResearch TrainingRiskRisk AssessmentRisk EstimateRisk FactorsSavingsScientistSouth AsianStable PopulationsStatistical Data InterpretationStatistical MethodsSubgroupTechniquesTechnologyTestingTrainingTraining ProgramsUnited StatesUniversitiesValidationVeterans Health AdministrationWomanWorkatherosclerosis riskbasebiomedical informaticscardiovascular disorder preventioncardiovascular disorder riskcardiovascular risk factorcareerclinical practicecohortcollegedisabilitydisparity reductionepidemiologic dataethnic minority populationevidence baseexperiencehealth differencehealth disparityhealth inequalitieshigh riskhigh risk populationi(19)improvedinnovationmachine learning algorithmmachine learning methodmenminority healthmortalityneural networkpatient subsetsprediction algorithmprevention servicepreventive interventionpublic health interventionracial and ethnicracial and ethnic disparitiesracial diversityracial minorityrandom forestrisk predictionrisk prediction modelside effectskillsstructured datasuccesssupervised learningsupport vector machinetreatment guidelinesunstructured data
项目摘要
PROJECT SUMMARY
Despite advances in technology, cardiovascular disease (CVD) remains the leading cause of death, disability,
and healthcare costs in the U.S. Yet, there is a tremendous gap in accurate cardiovascular risk prediction and
prevention, particularly in racial/ethnic minorities. Furthermore, there is significant heterogeneity in CVD risks
and outcomes for disaggregated Hispanic and Asian subgroups. The current cardiovascular risk assessment
tools have not been well-validated in these diverse populations, and it remains largely unknown why minority
patients are less likely to start and more likely to stop life-saving therapies. The overall goal of Dr. Rodriguez’s
K01 application is to address gaps in knowledge about CVD prediction and treatment in understudied
racial/ethnic minority populations. The proposed study will utilize the electronic health record (EHR) data from
an established NHLBI-funded cohort enriched with disaggregated Hispanic and Asian patients. Using this
cohort, Dr. Rodriguez will first test the ACC/AHA Pooled Cohort Equations in disaggregated Asian and
Hispanic subgroups using a large diverse mixed-payer cohort of 1,234,751 patients from two large healthcare
systems in Northern California and Hawaii. Secondly, she will build new CVD risk prediction models for diverse
patient subgroups using machine learning techniques. Finally, she will identify reasons for statin underuse and
discontinuation using natural language processing in the EHR. This study, which will evaluate existing data
from real-world clinical practice in a stable population, will inform future risk prediction models and cholesterol
treatment guidelines for diverse racial/ethnic groups. The proposal is aligned with the NHBLI’s strategic goals
to eliminate health disparities and inequities by leveraging epidemiology and data science to understand and
solve complex health problems. This proposal will also prepare Dr. Rodriguez to meet her long-term goal of
becoming a national leader and independent investigator in CVD prevention and minority health. The proposed
didactic and applied data science experiences, including training in advanced epidemiological methods and
machine learning, will prepare Dr. Rodriguez to apply her research to other areas of CVD prevention and
populations. This training program builds on the strengths of Stanford University in health services research,
epidemiology, and biomedical informatics. Her mentorship team, led by Dr. Latha Palaniappan, includes
experts in cardiovascular prevention and health services research (Dr. Heidenreich, co-mentor), applied
statistical analyses (Dr. Robert Tibshirani, advisor), machine learning in the EHR (Dr. Nigam Shah, advisor),
and chronic disease prediction and medical decision making (Dr. Michael Pignone, advisor). Dr. Rodriguez’s
team is committed to ensuring the success of the proposal as well as overseeing her advanced training in their
respective areas of expertise. The research and training plan proposed in this K01 application will develop Dr.
Rodriguez into a unique and highly-skilled clinician researcher ready to compete for R-level funding and launch
her independent research career.
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项目总结
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Fatima Rodriguez其他文献
Fatima Rodriguez的其他文献
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{{ truncateString('Fatima Rodriguez', 18)}}的其他基金
SURPASS: (Statin Use and Risk Prediction of Atherosclerotic Cardiovascular Disease in minority Subgroups)
SURPASS:(少数亚组中他汀类药物的使用和动脉粥样硬化性心血管疾病的风险预测)
- 批准号:
10676853 - 财政年份:2019
- 资助金额:
$ 17.13万 - 项目类别:
SURPASS: (Statin Use and Risk Prediction of Atherosclerotic Cardiovascular Disease in minority Subgroups)
SURPASS:(少数亚组中他汀类药物的使用和动脉粥样硬化性心血管疾病的风险预测)
- 批准号:
10460110 - 财政年份:2019
- 资助金额:
$ 17.13万 - 项目类别:
SALUD: Study of Disaggregated Latinos in the US to Address Disparities
SALUD:针对美国分类拉丁裔群体的研究,以解决差异问题
- 批准号:
9115842 - 财政年份:2017
- 资助金额:
$ 17.13万 - 项目类别:
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