Improving Cardiovascular Drug Safety With Automated Bleeding Classification

通过自动出血分类提高心血管药物安全性

• 批准号: 9899862
• 负责人: Rashmee U. Shah
• 金额: $ 16.31万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2021-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9899862
• 关键词: Accounting; Address; Adult; Adverse event; Affect; Age; Algorithms; Anticoagulants; Atrial Fibrillation; Biomedical Research; Cardiology; Cardiovascular Agents; Cardiovascular Diseases; Classification; Clinical; Code; Comparative Effectiveness Research; Competence; Complication; Computer Assisted; Computerized Medical Record; Data; Data Science; Development Plans; Devices; Diagnosis; Ensure; Event; Foundations; Future; Goals; Healthcare; Hemorrhage; Incidence; Institution; Interdisciplinary Study; K-Series Research Career Programs; Knowledge; Lead; Leadership; Learning; Letters; Link; Manuals; Measures; Mentors; Mentorship; Methodology; Methods; Modeling; Monitor; Natural Language Processing; Oral; Patients; Pharmaceutical Preparations; Pharmacotherapy; Population; Pragmatic clinical trial; Prevalence; Provider; Quality of life; Race; Records; Reproducibility; Research; Research Methodology; Research Personnel; Research Training; Risk; Safety; Severities; Site; Standardization; Stroke; Stroke prevention; System; Techniques; Testing; Text; Training; United States Centers for Medicare and Medicaid Services; Utah; Validation; Vital Status; acute coronary syndrome; advanced analytics; aging population; analytical method; base; biomedical informatics; career; career development; clinically relevant; cohort; improved; interdisciplinary approach; medication compliance; medication safety; mortality; neglect; novel; novel therapeutics; patient safety; personalized medicine; portability; public health relevance; screening; sex; skills; stroke risk; stroke therapy; tool

PROJECT SUMMARY Atrial fibrillation (AF) treatment often includes drug therapy with oral anticoagulants (OAC) to prevent stroke. Bleeding, however, is a common complication of these drugs, affecting up to one in four patients. The Center for Medicare and Medicaid Services recently prioritized OAC-related drug safety as a key quality measure. Currently, however, no method exists to accurately identify bleeding events and severity in large populations. Prior methods use diagnoses codes, which lack sensitivity and clinical detail, or manual chart review, which cannot be implemented in large populations. The proposed research aims address this knowledge gap by applying a natural language processing (NLP)-based approach to identify bleeding events and classify severity in a real-world AF population. The tools will be validated in patients treated at a different institution, to ensure reproducibility across provider settings. In addition, we will apply the bleeding classification tool to evaluate the association between bleeding severity and mortality. Dr. Shah is an emerging young investigator whose career development plan is focused on acquiring the biomedical informatics skills to needed to accurately identify and reduce patient harm. Her training plan focuses on learning core competencies in natural language processing, with the goal of turning the wealth of data in the electronic medical record into useable knowledge. She will combine mentorship from established experts and targeted coursework to acquire skills in biomedical informatics, data science, advanced analytic methods, and research leadership. Completion of these research and training aims will create a platform for future R01 proposals by: (i) enabling safety focused comparative effectiveness research in AF (ii) setting the stage to identify bleeding complications in other cardiovascular diseases and (iii) developing a skill set that allows leadership of a multidisciplinary research team. Through this career development plan, Dr. Shah will build upon her prior training in clinical cardiology and research methodology and lay a strong foundation for a high impact research career.

项目总结 房颤(AF)的治疗通常包括口服抗凝剂(OAC)预防中风的药物治疗。 然而，出血是这些药物的常见并发症，影响多达四分之一的患者。《中心》 对于Medicare和Medicaid Services，最近将与OAC相关的药物安全列为关键质量指标。 然而，目前还没有方法可以准确地确定大量人群中的出血事件和严重程度。 现有的方法使用缺乏敏感度和临床细节的诊断代码，或手动检查图表，这 不能在大量人口中实施。拟议的研究旨在通过以下方式解决这一知识差距 应用基于自然语言处理(NLP)的方法识别出血事件并对严重程度进行分类 在一个真实的AF人群中。这些工具将在另一家机构接受治疗的患者中进行验证，以确保 不同提供商设置的重现性。此外，我们将应用出血分类工具来评估 出血严重程度与死亡率之间的关系。沙阿博士是一位崭露头角的年轻研究员，他的职业生涯 发展计划的重点是获得生物医学信息学技能，以准确识别和 减少对病人的伤害。她的培训计划侧重于学习自然语言处理的核心能力， 目标是将电子病历中的丰富数据转化为有用的知识。她会的 结合来自知名专家的指导和有针对性的课程工作，获得生物医学方面的技能 信息学、数据科学、高级分析方法和研究领导力。完成这些研究 培训目标将通过以下方式为未来的R01提案创建平台：(I)使安全重点放在比较 房颤的有效性研究(II)为确定其他心血管疾病的出血并发症奠定基础 以及(Iii)培养一套能够领导多学科研究团队的技能。通过这件事 职业发展计划，沙阿博士将以她以前在临床心脏病学和研究方面的培训为基础 并为高影响力的研究事业奠定了坚实的基础。

项目成果

Development of Electronic Health Record-Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction.

• DOI: 10.1001/jamanetworkopen.2020.35782
• 发表时间: 2021-01-04
• 期刊: JAMA network open
• 影响因子: 13.8
• 作者: Matheny ME;Ricket I;Goodrich CA;Shah RU;Stabler ME;Perkins AM;Dorn C;Denton J;Bray BE;Gouripeddi R;Higgins J;Chapman WW;MacKenzie TA;Brown JR
• 通讯作者: Brown JR

Data and Information in the Sea of Electronic Health Records.

电子健康记录海洋中的数据和信息。

• DOI: 10.1161/circoutcomes.118.005247
• 发表时间: 2018
• 期刊: Circulation. Cardiovascular quality and outcomes
• 作者: Shah,RashmeeU;Matheny,MichaelE
• 通讯作者: Matheny,MichaelE

Peri-procedural complications in women: an alarming and consistent trend.

女性围手术期并发症：一个令人震惊且一致的趋势。

• DOI: 10.1093/eurheartj/ehz193
• 发表时间: 2019
• 期刊: European heart journal
• 影响因子: 39.3
• 作者: Wang,Libo;Selzman,KimberlyA;Shah,RashmeeU
• 通讯作者: Shah,RashmeeU

Modeling reductions in SARS-CoV-2 transmission and hospital burden achieved by prioritizing testing using a clinical prediction rule.

通过使用临床预测规则优先进行测试，对 SARS-CoV-2 传播和医院负担的减少进行建模。

• DOI: 10.1101/2020.07.07.20148510
• 发表时间: 2020
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Reimer,JodyR;Ahmed,ShariaM;Brintz,Benjamin;Shah,RashmeeU;Keegan,LindsayT;Ferrari,MatthewJ;Leung,DanielT
• 通讯作者: Leung,DanielT

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.

• DOI: 10.1016/j.jbi.2021.103851
• 发表时间: 2021-08
• 期刊: Journal of biomedical informatics
• 影响因子: 4.5
• 作者: Reeves RM;Christensen L;Brown JR;Conway M;Levis M;Gobbel GT;Shah RU;Goodrich C;Ricket I;Minter F;Bohm A;Bray BE;Matheny ME;Chapman W
• 通讯作者: Chapman W