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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要候选人：Michael Mathis 博士是一名心胸麻醉师，拥有麻醉学委员会认证以及密歇根大学先进的围手术期超声心动图检查。通过完成T32 研究培训补助金，马西斯博士在患者围手术期结果研究方面积累了专业知识患有晚期心血管疾病。他的长期职业目标是改善心脏病患者的护理通过利用围手术期电子医疗记录 (EHR) 数据进行早期诊断和治疗失败 (HF) 管理。该提案建立在 Mathis 博士的专业知识之上，为数据培训提供了受保护的时间推动改进心力衰竭诊断的分析技术所必需的科学方法。环境：密歇根大学是多中心围手术期的协调中心 Outcomes Group (MPOG)，一个由 50 多个麻醉科和外科科室组成的国际联盟与围手术期信息系统。 Sachin Kheterpal 博士（医学博士、工商管理硕士）是 Mathis 博士的主要导师，是 MPOG 的主任，也是 NIH 精准医学计划咨询小组的成员。这拟议的研究将在 Kheterpal 博士以及共同导师 Milo Engoren 的指导下完成，医学博士、Daniel Clauw 医学博士和 Kayvan Najarian 博士。心力衰竭诊断和数据专家顾问小组科学方法将为马西斯博士提供额外的指导。背景：心力衰竭是最常见的需要住院治疗的慢性疾病之一，并且携带高风险。死亡率。在围手术期，心力衰竭是主要心脏并发症的危险因素。尽管尽管护理方面取得了进步，但在减轻心力衰竭医疗负担方面进展甚微，困难归因于缺乏廉价、可靠的诊断措施。因此，心力衰竭患者可能无法被识别出来。处于早期阶段并且没有接受降低死亡率的治疗。围手术期是一个未被充分利用的时期改善心力衰竭诊断的机会。除了丰富的术前可用数据外，术中期间作为心脏负荷测试，通过该测试血流动力学对手术和麻醉的反应刺激以高分辨率记录。然而，这些数据仍然是心力衰竭评估的未开发资源。研究：拟议研究的目标是将围手术期作为一个机会心力衰竭的早期诊断。这两个具体目标是开发一种数据驱动的心力衰竭诊断算法，使用术前 EHR 数据（目标 1）以及术中 EHR 数据（目标 2）。这两个目标都将使用自动化从围手术期 EHR 中提取心力衰竭特征的技术，由 UM 开发，可扩展到多种通过 MPOG 基础设施的中心。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 Mathis 博士提供必要的数据科学计算经验，使其成为独立的医生研究员专注于改善心力衰竭患者的围手术期管理策略。