Machine Learning to Reduce Hypertension Treatment Clinical Inertia

机器学习减少高血压治疗的临床惰性

• 批准号: 10413958
• 负责人: Gabriel S Tajeu
• 金额: $ 14.78万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10413958
• 关键词: Address; Adult; Algorithms; Antihypertensive Agents; Attitude; Award; Bass; Blood Pressure; Cardiovascular Diseases; Caring; Chronic Disease; Clinic Visits; Clinical; Clinical Management; Clinical Research; Clinical Treatment; Complex; Computerized Medical Record; Data; Diabetic Retinopathy; Education; Event; Failure; Focus Groups; Future; Goals; Guidelines; Health; Health Services; Health system; Heart failure; Hospital Mortality; Hypertension; Intervention; Interview; Knowledge; Leadership; Length of Stay; Life Style Modification; Logistic Regressions; Machine Learning; Measurement; Medicine; Mentors; Modeling; Monitor; Obesity; Patients; Pattern; Persons; Pharmaceutical Preparations; Philadelphia; Physicians; Population; Population Intervention; Populations at Risk; Primary Health Care; Principal Investigator; Provider; Public Health; Qualitative Research; Randomized; Regimen; Reporting; Research; Research Personnel; Research Technics; Resources; Risk; Statistical Methods; Testing; Time; Training; Uncertainty; United States; Universities; Validation; Visit; Zoran; algorithm development; base; blood pressure control; cardiovascular disorder risk; cardiovascular risk factor; career; clinical care; clinical decision support; clinical practice; design; diet and exercise; dosage; effective therapy; experience; high risk; high risk population; hypertension control; hypertension treatment; improved; informant; machine learning algorithm; machine learning prediction algorithm; medication compliance; mid-career faculty; mortality; outcome prediction; pilot test; prediction algorithm; predictive modeling; predictive tools; prototype; random forest; risk prediction; skills; socioeconomics; support tools; tool; tool development; underserved area

PROJECT SUMMARY/ABSTRACT Hypertension (HTN) is a main risk factor for cardiovascular disease (CVD). It can be managed through use of antihypertensive medications and lifestyle modification (i.e., diet and exercise). Adults who have controlled BP have close to a 50% decreased risk of CVD-related mortality compared to those with uncontrolled BP. However, using current guidelines, over 40% of the 100 million US adults with HTN have controlled BP. Thus, uncontrolled BP remains a significant and largely unchecked public health issue in the United States. Interventions to improve BP control exist (i.e., medication adherence and diet and exercise), but they are mostly complex and resource intensive. If resources to improve HTN control are to be efficiently used, identifying patients who are likely to have persistently uncontrolled BP may provide an opportunity for targeted and tailored intervention and close monitoring. Application of a machine learning algorithm (MLA) to electronic medical record (EMR) data could identify patients likely to have uncontrolled BP. The use and amount of data in EMRs continues to expand and improve in quality. The Temple Health EMR contains extensive demographic, clinical, prescribing, and dispensing data. MLAs are ideally suited to harness this complex and abundant data. MLAs recognize patterns in data that may be difficult to detect by researchers and identify variables that are important for prediction of an outcome. They have been used to predict hospital length of stay, hospital mortality rates, and even detect diabetic retinopathy. The objective of the major study proposed in this K01 application is to first, develop and validate a random forest MLA to predict risk of uncontrolled BP among adults with HTN. Second, identify barriers and facilitators to implementing an MLA-clinical decision support (CDS) tool that predicts uncontrolled BP into clinical settings by conducting key-informant interviews and focus groups with primary care clinicians and patients. Third, Compare clinical management of hypertension with and without a prototype of an MLA-CDS tool through a case-vignette study. These aims align with my career goal of lowering CVD risk in adults. I will accomplish these aims with the help of an accomplished mentoring team focused on hypertension and antihypertensive medication adherence, machine learning, and qualitative interviewing and CDS-tool development. During the timeframe of this study I will gain experience in (1) MLA development and validation, (2) qualitative interviewing skills to develop CDS tools for clinicians, and (3) leadership skills for principal investigators. These skills will allow me to become an independent investigator focused on identifying people at high risk for chronic disease related to CVD events and developing tools to assist physicians in lowering CVD risk at the population level.

项目摘要/摘要 高血压（HTN）是心血管疾病（CVD）的主要危险因素。可以通过使用 降压药物和生活方式修饰（即饮食和运动）。控制BP的成年人 与不受控制的BP相比，与CVD相关死亡率的风险接近50％。 但是，使用当前的指南，在拥有HTN的1亿美国成年人中，超过40％控制了BP。因此， 在美国，不受控制的BP仍然是一个重要且在很大程度上不受限制的公共卫生问题。 存在改善BP控制的干预措施（即药物依从性，饮食和运动），但它们是 主要是复杂而资源密集的。如果要有效地使用改善HTN控制的资源， 确定可能持续不受控制的BP的患者可能为有针对性的患者提供机会 并量身定制干预和密切监控。 将机器学习算法（MLA）应用于电子病历（EMR）数据可以识别 患者可能患有不受控制的BP。 EMR中的数据的使用和数量继续扩展和改进 质量。 Temple Health EMR包含广泛的人口统计，临床，处方和分配数据。 MLA非常适合利用这一复杂而丰富的数据。 MLA识别数据中的模式 很难检测到研究人员并确定对于预测结果很重要的变量。他们 已被用来预测住院时间，医院死亡率，甚至检测到糖尿病性视网膜病。 在本K01应用中提出的主要研究的目的是首先，开发和验证随机 森林MLA可以预测HTN成年人不受控制的BP的风险。第二，确定障碍和促进者 实施MLA临床决策支持（CDS）工具，该工具将不受控制的BP预测到临床环境中 通过与初级保健临床医生和患者进行密钥信息访谈和焦点小组。第三， 通过有或没有MLA-CD工具的原型比较高血压的临床管理 病例维格特研究。这些目的与我的职业目标相吻合，即降低成人CVD风险。我会完成的 这些目的是在专注于高血压和降压性的精通指导团队的帮助下 药物依从性，机器学习以及定性访谈和CDS-Tool开发。在 这项研究的时间表我将获得（1）MLA开发和验证的经验，（2）定性访谈 为临床医生开发CD工具的技能，以及（3）首席研究人员的领导才能。这些技能会 允许我成为一名专注于确定慢性病风险高风险的人 与CVD事件和开发工具有关，以帮助医生降低人口水平的CVD风险。