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. !

项目摘要 尽管技术进步，心血管疾病（CVD）仍然是死亡，残疾， 然而，在准确的心血管风险预测方面存在巨大差距， 预防，特别是在种族/族裔少数群体中。此外，CVD风险存在显著异质性， 以及西班牙裔和亚裔亚组的分类结果。目前的心血管风险评估 工具尚未在这些不同的人群中得到很好的验证，并且在很大程度上仍然不清楚为什么少数人 患者开始接受挽救生命的治疗的可能性更小，而停止的可能性更大。罗德里格斯博士的总体目标是 K 01应用程序是为了解决有关CVD预测和治疗的知识不足， 种族/少数民族人口。拟议的研究将利用电子健康记录（EHR）数据， 一个由NHLBI资助的已建立的队列，其中包含了分类的西班牙裔和亚裔患者。使用此 Rodriguez博士将首先在分类的亚洲人群中测试ACC/AHA合并队列方程， 西班牙裔亚组使用来自两个大型医疗保健机构的1，234，751例患者的大型多元化混合支付者队列 北方加州和夏威夷的系统。其次，她将建立新的CVD风险预测模型， 使用机器学习技术的患者亚组。最后，她将确定他汀类药物使用不足的原因， 在EHR中使用自然语言处理。这项研究将评估现有的数据， 来自稳定人群的真实临床实践，将为未来的风险预测模型和胆固醇水平提供信息。 不同种族/民族群体的治疗指南。该提案符合NHBLI的战略目标 通过利用流行病学和数据科学来了解和 解决复杂的健康问题。这一提议也将为罗德里格斯博士实现她的长期目标做好准备， 成为CVD预防和少数民族健康的国家领导人和独立调查员。拟议 教学和应用数据科学经验，包括先进流行病学方法的培训， 机器学习，将准备罗德里格斯博士将她的研究应用到CVD预防的其他领域， 人口。该培训计划建立在斯坦福大学在卫生服务研究方面的优势基础上， 流行病学和生物医学信息学。她的导师团队由Latha Palaniappan博士领导，包括 心血管预防和卫生服务研究专家（Heidenreich博士，共同导师），应用 统计分析（Robert Tibshirani博士，顾问），EHR中的机器学习（Nigam Shah博士，顾问）， 慢性疾病预测和医疗决策（Michael Pignone博士，顾问）。罗德里格斯医生 团队致力于确保提案的成功，并监督她在他们的高级培训 各自的专业领域。本K 01申请中提出的研究和培训计划将培养博士。 罗德里格斯成为一个独特的和高技能的临床研究人员准备竞争R级资金和推出 她的独立研究生涯 !