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: 10080751
• 负责人: Fatima Rodriguez
• 金额: $ 17.13万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 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: https://reporter.nih.gov/project-details/10080751
• 关键词: Address; Adherence; American; American Heart Association; Area; Asians; Assessment tool; Atherosclerosis; Biometry; Calibration; California; Cardiology; Cardiovascular Diseases; Cardiovascular system; Cause of Death; Cholesterol; Chronic Disease; Clinical; Clinical Data; Code; Complex; Data; Data Science; Decision Making; Diagnosis; Discrimination; Disease Outcome; Electronic Health Record; Ensure; Epidemiologic Methods; Epidemiology; Equation; Ethnic group; Event; Foundations; Funding; Future; Goals; Guidelines; Hawaii; Health; Health Care Costs; Health Services Research; Health system; Healthcare Systems; Heterogeneity; Hispanics; Hypersensitivity; Intervention; Knowledge; Laboratories; Lead; Life; Machine Learning; Medical; Mentors; Mentorship; Mexican Americans; Minority; Minority Groups; Modeling; Modernization; National Heart, Lung, and Blood Institute; Natural Language Processing; Not Hispanic or Latino; Outcome; Patients; Performance; Physicians; Population; Population Heterogeneity; Prevention; Prevention strategy; Puerto Rican; Research; Research Personnel; Research Training; Risk; Risk Assessment; Risk Estimate; Risk Factors; Savings; Scientist; South Asian; Stable Populations; Statistical Data Interpretation; Statistical Methods; Subgroup; Techniques; Technology; Testing; Training; Training Programs; United States; Universities; Validation; Veterans Health Administration; Woman; Work; atherosclerosis risk; base; biomedical informatics; cardiovascular disorder prevention; cardiovascular disorder risk; cardiovascular risk factor; career; clinical practice; cohort; college; disability; disparity reduction; epidemiologic data; ethnic minority population; evidence base; experience; health difference; health disparity; health inequalities; high risk; high risk population; i(19); improved; innovation; machine learning algorithm; machine learning method; men; minority health; mortality; neural network; patient subsets; prediction algorithm; prevention service; preventive intervention; public health intervention; racial and ethnic; racial and ethnic disparities; racial diversity; racial minority; random forest; risk prediction; risk prediction model; side effect; skills; structured data; success; supervised learning; support vector machine; treatment guidelines; unstructured data

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. !

项目总结