Early Diagnosis of Heart Failure: A Perioperative Data-Driven Approach

心力衰竭的早期诊断：围手术期数据驱动的方法

• 批准号: 9895469
• 负责人: Michael Robert Mathis
• 金额: $ 17.23万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-05 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9895469
• 关键词: Affect; Algorithms; Anesthesia procedures; Anesthesiology; Anesthetics; Assessment tool; Award; Bayesian Analysis; Board Certification; Cardiac; Cardiovascular Diseases; Caring; Characteristics; Chronic; Clinic; Clinical; Computational Science; Consumption; Coronary Arteriosclerosis; Data; Data Science; Data Sources; Databases; Development Plans; Diabetes Mellitus; Diagnosis; Diagnostic; Discrimination; Disease; Doctor of Philosophy; Early Diagnosis; Echocardiography; Electronic Health Record; Entropy; Environment; Evaluation; Goals; Grant; Healthcare; Heart Rate; Heart failure; Hospitalization; Hypertension; Incidence; Information Systems; Infrastructure; International; Intraoperative Period; Knowledge; Laboratories; Learning; Life; Malignant Neoplasms; Measures; Medical History; Medical Records; Mentors; Methodological Studies; Methodology; Methods; Michigan; Modeling; Modernization; Natural Language Processing; Operative Surgical Procedures; Outcome; Outcomes Research; Patient Care; Patients; Performance; Perioperative; Physicians; Physiological; Positioning Attribute; Precision Medicine Initiative; Prevalence; Process; Research; Research Personnel; Research Training; Resolution; Resources; Retrospective Studies; Risk; Risk Assessment; Risk Factors; Sensitivity and Specificity; Statistical Models; Stimulus; Stress; Techniques; Testing; Thallium Myocardial Perfusion Imaging Stress Test; Time; Time Series Analysis; Training; United States National Institutes of Health; Universities; Work; adjudicate; algorithm training; base; blood pressure variability; care providers; career; career development; cohort; cost; cost effective; effective intervention; experience; hemodynamics; improved; large datasets; member; mortality; novel; outcome forecast; public health relevance; response; signal processing; skills; stressor; tool

PROJECT SUMMARY / ABSTRACT Candidate: Dr. Michael Mathis is a cardiothoracic anesthesiologist with board certification in anesthesiology and advanced perioperative echocardiography at the University of Michigan. Through completion of a T32 Research Training Grant, Dr. Mathis has developed expertise in perioperative outcomes research for patients with advanced cardiovascular disease. His long-term career goal is to improve care for patients with heart failure (HF) through harnessing perioperative electronic healthcare record (EHR) data for early diagnosis and management. This proposal builds on Dr. Mathis's expertise, providing protected time for training in data science methods necessary to drive forward the analytic techniques proposed for improving HF diagnosis. Environment: The University of Michigan is the coordinating center for the Multicenter Perioperative Outcomes Group (MPOG), an international consortium of over 50 anesthesiology and surgical departments with perioperative information systems. Dr. Sachin Kheterpal, MD, MBA is the primary mentor for Dr. Mathis, and is the Director for MPOG and member of the NIH Precision Medicine Initiative Advisory Panel. The proposed research will be completed under the guidance of Dr. Kheterpal, as well as co-mentors Milo Engoren, MD, Daniel Clauw, MD, and Kayvan Najarian, PhD. An advisory panel of experts in HF diagnosis and data science methodologies will provide Dr. Mathis with additional guidance. Background: HF is among the most common chronic conditions requiring hospitalization and carries high rates of mortality. In the perioperative period, HF is a risk factor for major cardiac complications. Despite advances in care, little progress has been made to reduce HF healthcare burden, with difficulties attributable to a lack of inexpensive, reliable diagnostic measures. Consequently, patients with HF can go unrecognized in early stages and do not receive treatments to reduce mortality. The perioperative period is an underutilized opportunity to improve HF diagnosis. Beyond the wealth of preoperative data available, the intraoperative period serves as a cardiac stress test through which hemodynamic responses to surgical and anesthetic stimuli are recorded with high resolution. Yet, this data remains an untapped resource for HF evaluation. Research: The goal of the proposed research is to incorporate the perioperative period as an opportunity for early diagnosis of HF. The two specific Aims are to develop a data-driven diagnostic algorithm for HF using preoperative EHR data (Aim 1) as well as intraoperative EHR data (Aim 2). Both aims will use automated techniques to extract features of HF from the perioperative EHR, developed at UM and scalable to multiple centers via the MPOG infrastructure. This work represents a paradigm shift in perioperative evaluation, using perioperative data as a diagnostic tool rather than a risk-assessment tool. The proposed research and training will provide Dr. Mathis with necessary data science computational experience to become an independent physician-investigator focused on improving perioperative management strategies for patients with HF.

项目总结/摘要 候选人：Michael马西斯博士是一名拥有麻醉学委员会认证的心胸麻醉师 以及密歇根大学的高级围手术期超声心动图通过完成T32 马西斯博士获得了研究培训补助金，在患者围手术期结局研究方面积累了专业知识 患有严重的心血管疾病他的长期职业目标是改善对心脏病患者的护理， 通过利用围手术期电子医疗记录（EHR）数据进行早期诊断， 管理该提案以马西斯博士的专业知识为基础，为数据培训提供了受保护的时间 科学的方法，必要的推动提出的分析技术，以改善HF诊断。 环境：密歇根大学是多中心围手术期的协调中心 结果组（MPOG），一个由50多个麻醉和外科部门组成的国际联盟 围手术期信息系统Sachin Kheterpal博士，医学博士，MBA是马西斯博士的主要导师， 他是MPOG的主任，也是NIH精准医学倡议咨询小组的成员。的 拟议的研究将在Kheterpal博士和共同导师米洛恩戈伦的指导下完成， 医学博士、医学博士丹尼尔克劳和博士凯万纳贾里安。HF诊断和数据专家咨询小组 科学方法将为马西斯博士提供额外的指导。 背景：HF是最常见的需要住院治疗的慢性疾病之一， 死亡率。在围手术期，HF是主要心脏并发症的危险因素。尽管 护理方面取得了进展，但在减轻HF医疗负担方面却进展甚微，困难归因于 缺乏廉价、可靠的诊断手段。因此，HF患者可能无法识别， 早期阶段，不接受治疗，以降低死亡率。围手术期是一个未充分利用的 改善HF诊断的机会。除了丰富的术前数据外，术中 期间作为心脏负荷试验，通过该试验， 以高分辨率记录刺激。然而，这些数据仍然是HF评估的未开发资源。 研究：拟议研究的目标是将围手术期作为一个机会， HF的早期诊断两个具体目标是开发一种数据驱动的HF诊断算法， 术前EHR数据（目标1）以及术中EHR数据（目标2）。两个目标都将使用自动化 从围手术期EHR中提取HF特征的技术，在UM开发，可扩展到多个 通过MPOG基础设施。这项工作代表了围手术期评估的范式转变，使用 围手术期数据作为诊断工具，而不是风险评估工具。拟议的研究和培训 将为马西斯博士提供必要的数据科学计算经验，使其成为一名独立的 医生-研究者关注于改善HF患者的围手术期管理策略。