FW-HTF-RM: Introducing Patient-Specific Therapy Profiles in Electronic Health Records for Guiding Treatment Selection in the Era of Genomic Medicine

FW-HTF-RM：在电子健康记录中引入患者特定的治疗方案，以指导基因组医学时代的治疗选择

• 批准号: 2041339
• 负责人: Arjun Athreya
• 金额: $ 136.89万
• 依托单位: Mayo Clinic Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2041339&HistoricalAwards=false
• 关键词: FW; HTF; RM; Introducing; Patient

Clinicians (workers) rate electronic health record (EHR) systems (human-technology frontier), used to review and document patient's health status and enter orders for drug prescription (work), an 'F' for usability. Specifically, the EHR is often seen as a barrier to care, rather than a tool to facilitate high quality care. This is due, in part, to high volumes of EHR alerts that are automatically generated and must be addressed when prescribing medications to treat conditions (e.g., depression). Such alerts, which typically address potential adverse reactions ranging from life threatening to minor reactions, are based on population studies and are not patient-specific. Current EHR alerts also only advise what "not to do" and do not offer guidance (representing a significant knowledge gap) as to "what to do" (e.g., which alternative medication(s) should be considered instead). As a result, clinicians spend substantial amounts of time dealing with unhelpful EHR alerts (contributing to high work stress and burnout) and employ a costly "trial-and-error" approach to selecting drugs. Clinicians need a technology interface that facilitates care - one that seamlessly provides an estimated likelihood of efficacy and adverse drug reactions of a given medication for a particular patient. A "patient-specific drug EHR alert" would advance patient care (faster remission from depression), foster shared decision-making between clinicians and patients (more information readily available to individualizing therapy), and reduce worker stress and risk of burnout (improved human-technology frontier by improving EHR usability). This project is of significant public health importance given that new drugs are discovered at unprecedented rates and clinical evidence continues to accumulate showing that several genetic tests developed to individualize therapy have improved patient outcomes and demonstrated significant savings in healthcare costs. Education activities include a curriculum development for a new course on fundamentals of machine learning and genomic medicine. The researchers will also involve undergraduate and underrepresented community in the proposed research activities.The overarching goal of this project is to facilitate the integration of machine learning-based predictive analytics into EHR systems that use genomic and clinical data to tailor therapy for patients. The following objectives help achieve the overarching goal: (1) Develop a multi-task machine learning model that can simultaneously predict efficacy and associated adverse reactions to drug therapy, using patient's genomic, clinical and sociodemographic data. Different predictive approaches such as task clustering and task relation will be explored to provide the best predictive performance. This technology is enabled by the use of patient data from Mayo Clinic Biobank and clinical trials, and will be validated in a prospective patient cohort in routine practice at Mayo Clinic's Rochester and Florida campuses; (2) Conduct a "system usability study" to demonstrate that "patient-specific drug response profile" (i.e., efficacy and adverse reactions) improves EHR usability, which translates into reduced work stress, and perceived added value by clinicians; and (3) Establish clinician perceptions of added value in genomic technologies designed to individualize therapy, thereby characterizing facilitators and barriers of genomic-tailored EHR drug alerts. As a case study, this project will focus on antidepressant drugs used to treat major depressive disorder, leveraging data from over 10,000 patients in the Mayo Clinic Biobank.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

临床医生（工作人员）对电子健康记录 (EHR) 系统（人类技术前沿）进行评级，该系统用于审查和记录患者的健康状况并输入药物处方（工作）订单，可用性为“F”。具体来说，电子病历通常被视为护理的障碍，而不是促进高质量护理的工具。这在一定程度上是由于自动生成的大量 EHR 警报在开药治疗疾病（例如抑郁症）时必须予以解决。此类警报通常针对从危及生命到轻微反应的潜在不良反应，基于人群研究，并非针对特定患者。当前的 EHR 警报也仅建议“不做什么”，而不提供关于“做什么”的指导（代表了重大的知识差距）（例如，应考虑使用哪种替代药物）。因此，临床医生花费大量时间处理无用的 EHR 警报（导致高工作压力和倦怠），并采用成本高昂的“试错”方法来选择药物。临床医生需要一种有利于护理的技术界面——能够无缝地为特定患者提供给定药物的疗效和药物不良反应的估计可能性。 “针对特定患者的药物 EHR 警报”将促进患者护理（更快地缓解抑郁症），促进临床医生和患者之间的共同决策（为个体化治疗提供更多信息），并减少工作人员压力和倦怠风险（通过提高 EHR 可用性来改善人类技术前沿）。鉴于新药以前所未有的速度被发现，并且临床证据不断积累，表明为个体化治疗而开发的几种基因测试改善了患者的治疗效果，并证明了医疗保健成本的显着节省，该项目具有重要的公共卫生重要性。教育活动包括机器学习和基因组医学基础知识新课程的课程开发。研究人员还将让本科生和代表性不足的社区参与拟议的研究活动。该项目的总体目标是促进将基于机器学习的预测分析集成到 EHR 系统中，该系统使用基因组和临床数据为患者量身定制治疗。以下目标有助于实现总体目标：（1）开发多任务机器学习模型，可以利用患者的基因组、临床和社会人口统计数据同时预测药物治疗的疗效和相关不良反应。将探索不同的预测方法，例如任务聚类和任务关系，以提供最佳的预测性能。这项技术是通过使用梅奥诊所生物库和临床试验的患者数据来实现的，并将在梅奥诊所罗切斯特和佛罗里达校区的常规实践中的前瞻性患者队列中得到验证； (2) 进行“系统可用性研究”，以证明“患者特定的药物反应概况”（即疗效和不良反应）可以提高 EHR 的可用性，从而减少工作压力，并让临床医生感受到附加值； (3) 建立临床医生对旨在个体化治疗的基因组技术附加价值的看法，从而描述基因组定制的 EHR 药物警报的促进因素和障碍。作为一个案例研究，该项目将重点关注用于治疗重度抑郁症的抗抑郁药物，利用梅奥诊所生物库中 10,000 多名患者的数据。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Systematic review: Wearable remote monitoring to detect nonalcohol/nonnicotine‐related substance use disorder symptoms

系统评价：可穿戴式远程监控检测非酒精/非尼古丁相关物质使用障碍症状

• DOI: 10.1111/ajad.13341
• 发表时间: 2022
• 期刊: The American Journal on Addictions
• 作者: Oesterle, Tyler S.;Karpyak, Victor M.;Coombes, Brandon J.;Athreya, Arjun P.;Breitinger, Scott A.;Correa da Costa, Sabrina;Dana) Gerberi, Danielle J.
• 通讯作者: Dana) Gerberi, Danielle J.

Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder.

• DOI: 10.3390/jpm12030412
• 发表时间: 2022-03-06
• 期刊: Journal of personalized medicine
• 作者: Grant CW;Barreto EF;Kumar R;Kaddurah-Daouk R;Skime M;Mayes T;Carmody T;Biernacka J;Wang L;Weinshilboum R;Trivedi MH;Bobo WV;Croarkin PE;Athreya AP
• 通讯作者: Athreya AP

Toward a Definition of “No Meaningful Benefit” From Antidepressant Treatment: An Equipercentile Analysis With Cross-Trial Validation Across Multiple Rating Scales

抗抑郁治疗“没有有意义的益处”的定义：跨多个评级量表的交叉试验验证的等百分位分析

• DOI: 10.4088/jcp.21m14239
• 发表时间: 2022
• 期刊: The Journal of Clinical Psychiatry
• 作者: Zhang, Carl;Virani, Sanya;Mayes, Taryn;Carmody, Thomas;Croarkin, Paul E.;Weinshilboum, Richard;Rush, A. John;Trivedi, Madhukar;Athreya, Arjun P.;Bobo, William V.
• 通讯作者: Bobo, William V.

Preliminary Evidence for Anhedonia as a Marker of Sexual Trauma in Female Adolescents.

• DOI: 10.2147/ahmt.s300150
• 发表时间: 2021
• 期刊: Adolescent health, medicine and therapeutics
• 作者: Sonmez AI;Lewis CP;Athreya AP;Shekunov J;Croarkin PE
• 通讯作者: Croarkin PE

A Characterization of the Clinical Global Impression Scale Thresholds in the Treatment of Adolescent Depression Across Multiple Rating Scales

跨多个评估量表治疗青少年抑郁症的临床总体印象量表阈值的表征

• DOI: 10.1089/cap.2021.0111
• 发表时间: 2022
• 期刊: Journal of Child and Adolescent Psychopharmacology
• 影响因子: 1.9
• 作者: Zhang, Carl Y.;Voort, Jennifer L.;Yuruk, Deniz;Mills, Jeffrey A.;Emslie, Graham J.;Kennard, Betsy D.;Mayes, Taryn;Trivedi, Madhukar;Bobo, William V.;Strawn, Jeffrey R.
• 通讯作者: Strawn, Jeffrey R.