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: 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
• 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

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

项目成果

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

Reduced Echocardiographic Inotropy Index after Cardiopulmonary Bypass Is Associated With Complications After Cardiac Surgery: An Institutional Outcomes Study.

• DOI: 10.1053/j.jvca.2021.01.041
• 发表时间: 2021-09
• 期刊: Journal of cardiothoracic and vascular anesthesia
• 影响因子: 2.8
• 作者: Mathis MR;Duggal NM;Janda AM;Fennema JL;Yang B;Pagani FD;Maile MD;Hofer RE;Jewell ES;Engoren MC
• 通讯作者: Engoren MC

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
• 作者: Ebadi-Tehrani,MehranM;Smith,EricD;Chang,AndrewC;Ailawadi,Gorav;Blank,Ross;Palardy,Maryse;Mathis,MichaelR
• 通讯作者: Mathis,MichaelR

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.

Sugammadex与Neostigmine的神经肌肉阻滞和术后肺并发症的逆转（更强）：多中心匹配的队列分析。

• 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