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：
10421285
负责人：
Michael Robert Mathis
金额：
$ 17.28万
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/10421285
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 Prognosis 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 diagnostic algorithm diagnostic strategy diagnostic tool diagnostic value effective intervention experience hemodynamics improved large datasets member mortality novel public health relevance response signal processing skills stressor

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.

项目摘要 /摘要候选人：迈克尔·马蒂斯（Michael Mathis）博士是一名心胸麻醉师，并具有麻醉学董事会认证和密歇根大学的高级围手术期超声心动图。通过完成T32 研究培训补助金Mathis博士已为患者开发了围手术期结果的专业知识患有晚期心血管疾病。他的长期职业目标是改善对心脏患者的护理通过利用围手术期电子保健记录（EHR）数据进行早期诊断和管理。该建议建立在Mathis博士的专业知识上，为数据培训提供了受保护的时间推动提出的用于改善HF诊断的分析技术所需的科学方法。环境：密歇根大学是多中心围手术期的协调中心成果集团（MPOG），一个由50多个麻醉学和外科部门组成的国际财团与围手术期信息系统。 MBA医学博士Sachin Kheterpal博士是Mathis博士的主要导师并且是MPOG的董事，也是NIH Precision Medicine Initiative咨询小组的成员。这拟议的研究将在Kheterpal博士的指导下以及联合会员Milo Engoren的指导下完成。医学博士，医学博士丹尼尔·克劳（Daniel Clauw）和凯文·纳贾（Kayvan Najarian）博士。 HF诊断和数据专家的咨询小组科学方法论将为Mathis博士提供其他指导。背景：HF是需要住院的最常见的慢性疾病之一死亡率。在围手术期间，HF是主要心脏并发症的危险因素。尽管护理的进步，减轻HF医疗保健负担几乎没有取得进展，这归因于缺乏廉价，可靠的诊断措施。因此，HF患者无法在早期阶段，不接受治疗以降低死亡率。围手术期的未充分利用改善HF诊断的机会。除了可用的大量术前数据外，术中期间是心脏应激测试，通过对手术和麻醉的血液动力学反应刺激以高分辨率记录。但是，这些数据仍然是HF评估的未开发资源。研究：拟议的研究的目的是将围手术期纳入一个机会早期诊断HF。这两个具体目的是使用数据驱动的HF开发数据驱动的诊断算法术前EHR数据（AIM 1）以及术中EHR数据（AIM 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，以UM开发，可扩展到多个通过MPOG基础架构中心。这项工作代表了围手术期评估的范式转变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，以成为独立的医师评估者的重点是改善HF患者的围手术期管理策略。

项目成果

期刊论文数量（24）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning.

DOI：
10.1097/aln.0000000000003150
发表时间：
2020-04
期刊：
Anesthesiology
影响因子：
8.8
作者：
Burns ML;Mathis MR;Vandervest J;Tan X;Lu B;Colquhoun DA;Shah N;Kheterpal S;Saager L
通讯作者：
Saager L

Variation in propofol induction doses administered to surgical patients over age 65.

DOI：
10.1111/jgs.17139
发表时间：
2021-08
期刊：
Journal of the American Geriatrics Society
影响因子：
6.3
作者：
Schonberger RB;Bardia A;Dai F;Michel G;Yanez D;Curtis JP;Vaughn MT;Burg MM;Mathis M;Kheterpal S;Akhtar S;Shah N
通讯作者：
Shah N

Sugammadex versus Neostigmine for Reversal of Neuromuscular Blockade and Postoperative Pulmonary Complications (STRONGER): A Multicenter Matched Cohort Analysis.

DOI：
10.1097/aln.0000000000003256
发表时间：
2020-06
期刊：
Anesthesiology
影响因子：
8.8
作者：
Kheterpal S;Vaughn MT;Dubovoy TZ;Shah NJ;Bash LD;Colquhoun DA;Shanks AM;Mathis MR;Soto RG;Bardia A;Bartels K;McCormick PJ;Schonberger RB;Saager L
通讯作者：
Saager L

Transesophageal Echocardiography for Cardiac Surgery Patients With Prior Esophagectomies: Insights From a 15-Year Institutional Experience.

对既往接受过食管切除术的心脏手术患者进行经食管超声心动图检查：来自 15 年机构经验的见解。

DOI：
10.1016/j.echo.2022.12.020
发表时间：
2023
期刊：
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography
影响因子：
0
作者：
Ebadi-Tehrani,MehranM;Smith,EricD;Chang,AndrewC;Ailawadi,Gorav;Blank,Ross;Palardy,Maryse;Mathis,MichaelR
通讯作者：
Mathis,MichaelR

Making Sense of Big Data to Improve Perioperative Care: Learning Health Systems and the Multicenter Perioperative Outcomes Group.