Electrocardiographic Detection of Non-ST Elevation Myocardial Events for Accelerated Classification of Chest Pain Encounters (ECG-SMART 2)

非 ST 段抬高心肌事件的心电图检测，加速胸痛分类 (ECG-SMART 2)

• 批准号: 10633243
• 负责人: Salah S Al-Zaiti
• 金额: $ 67.42万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10633243
• 关键词: Acceleration; Accident and Emergency department; Acute; Acute Coronary Event; Acute myocardial infarction; Admission activity; Algorithms; American; Architecture; Attenuated; Benchmarking; Biological Markers; Bundle-Branch Block; Cardiac; Caring; Chest Pain; Classification; Clinical; Clinical Trials; Congestive Heart Failure; Consumption; Coronary; Coronary Occlusions; County; Data; Data Set; Databases; Decision Support Systems; Detection; Development; Diagnosis; Diagnostic; Early Intervention; Electrocardiogram; Emergency Care; Emergency medical service; Engineering; Enzymes; Evaluation; Event; Expert Systems; Funding; Hospitals; Infarction; Intelligence; Ischemia; Judgment; Lead; Left Ventricular Hypertrophy; Lesion; Link; Location; Machine Learning; Malpractice; Measurement; Modeling; Monitor; Myocardial; Myocardial Infarction; Myocardial Ischemia; Myocardium; North Carolina; Nurses; Oranges; Outcome; Paramedical Personnel; Patients; Pattern; Performance; Phenotype; Physicians; Public Health; Readiness; Recommendation; Sampling; Sex Bias; Site; Syndrome; System; Testing; Time; Training; Translating; Troponin; Universities; Validation; Work; acute care; acute coronary syndrome; biomarker identification; clinical practice; clinical research site; clinically actionable; coronary lesion; cost; detection platform; diabetic; emergency service responder; field study; graphical user interface; implementation barriers; improved; improved outcome; innovation; iterative design; machine learning algorithm; model building; mortality risk; multitask; novel; pain patient; patient stratification; patient subsets; pilot test; prospective; prototype; racial bias; repository; risk stratification; spatial integration; time use; tool; usability; user centered design; ventricular hypertrophy

Electrocardiographic Detection of Non-ST Elevation Myocardial Events for Accelerated Classification of Chest Pain Encounters (ECG-SMART-2) ABSTRACT There is a clear need to develop improved tools to stratify risk in patients who seek emergency care for chest pain, one of the most common and potentially deadly conditions encountered in acute care settings. The 12- lead ECG has been the mainstay of initial evaluation of chest pain yet is currently only diagnostic for a small subset of patients with ST-elevation myocardial infarction. Over the past funding period, we have built the largest database of multi-hospital, outcome-linked, prehospital 12-lead ECG repository known to us (n=4,132). Using this multi-expert, multi-tier ground truth annotated database, we have developed and validated novel, machine learning-based, ECG interpretation algorithms that could identify non-ST elevation acute coronary events. Using state-of-the-art interpretability toolkits, we identified ECG signatures that are mechanistically linked to ischemia and can serve as plausible markers of acute coronary syndrome. We now aim to move these extensive efforts to clinical use by expanding and building these models at the bedside for prospective validation and real-time clinical deployment. The specific aims of this renewal application are: 1) to build and externally validate a multi-task, ECG-based intelligent decision support system; 2) to build and deploy a real- time architecture for this intelligent system along with a clinician-facing graphical user interface platform; and 3) to perform a prospective clinical validation of this intelligent ECG system, including silent deployment and evaluation at two clinical sites. The final deliverable is an intelligent ECG interpretation system for detecting and stratifying patients with suspected acute coronary syndrome of sufficient readiness to be deployed in clinical trials aimed at improving outcomes in non-ST elevation coronary syndromes. Such intelligent system, when combined with the judgment of trained emergency personnel (physicians, nurses, and paramedics), would more accurately identify patients with acute coronary occlusions for ultra-early intervention. This system will streamline the care provided to non-specific chest pain beyond the costly and time-consuming overnight observations for serial cardiac enzymes and provocative testing.

心电图检测非ST段抬高心肌事件 胸痛发作分类（ECG-SMART-2） 摘要 有一个明确的需要，以开发改进的工具，以分层的风险，病人谁寻求紧急护理胸部 疼痛，在急性护理环境中遇到的最常见和潜在致命的条件之一。12- 导联心电图一直是胸痛初始评估的主要手段，但目前仅用于小部分患者的诊断。 ST段抬高型心肌梗死患者亚组。在过去的资助期内，我们已经建立了 我们已知的最大的多医院、与结果相关的院前12导联ECG数据库（n = 4，132）。 使用这个多专家，多层地面实况注释数据库，我们已经开发和验证了新的， 基于机器学习的ECG解读算法，可识别非ST段抬高急性冠状动脉 事件使用最先进的可解释性工具包，我们确定了机械地 与缺血有关，可以作为急性冠状动脉综合征的合理标志物。我们现在的目标是 这些广泛的努力，临床使用，扩大和建立这些模型在床边的前瞻性 验证和实时临床部署。本次续期申请的具体目标是：1）建立和 外部验证多任务、基于ECG的智能决策支持系统; 2）构建和部署真实的 该智能系统的时间架构以及面向临床医生的图形用户界面平台;以及3） 对该智能ECG系统进行前瞻性临床验证，包括静默部署， 在两个临床试验机构进行评估。最终交付的是一个智能心电图解释系统，用于检测 并对疑似急性冠状动脉综合征的患者进行分层， 旨在改善非ST段抬高冠状动脉综合征结局的临床试验。这样的智能系统， 当与受过训练的急救人员（医生、护士和护理人员）的判断相结合时， 将更准确地识别急性冠状动脉闭塞患者进行超早期干预。该系统 将简化为非特异性胸痛提供的护理， 观察系列心肌酶和激发试验。

项目成果

Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact.

机器学习用于首次医疗接触时闭塞性心肌梗死的心电图诊断和风险分层。

• DOI: 10.21203/rs.3.rs-2510930/v1
• 发表时间: 2023
• 期刊: Research square
• 作者: Al-Zaiti,Salah;Martin-Gill,Christian;Zégre-Hemsey,Jessica;Bouzid,Zeineb;Faramand,Ziad;Alrawashdeh,Mohammad;Gregg,Richard;Helman,Stephanie;Riek,Nathan;Kraevsky-Phillips,Karina;Clermont,Gilles;Akcakaya,Murat;Sereika,Susan;VanDam,
• 通讯作者: VanDam,

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.

• DOI: 10.1038/s41591-023-02396-3
• 发表时间: 2023-07
• 期刊: NATURE MEDICINE
• 影响因子: 82.9
• 作者: Al-Zaiti, Salah S.;Martin-Gill, Christian;Zegre-Hemsey, Jessica K.;Bouzid, Zeineb;Faramand, Ziad;Alrawashdeh, Mohammad O.;Gregg, Richard E.;Helman, Stephanie;Riek, Nathan T.;Kraevsky-Phillips, Karina;Clermont, Gilles;Akcakaya, Murat;Sereika, Susan M.;Van Dam, Peter;Smith, Stephen W.;Birnbaum, Yochai;Saba, Samir;Sejdic, Ervin;Callaway, Clifton W.
• 通讯作者: Callaway, Clifton W.

Unsupervised machine learning identifies symptoms of indigestion as a predictor of acute decompensation and adverse cardiac events in patients with heart failure presenting to the emergency department.

无监督机器学习可识别消化不良症状，作为急诊室心力衰竭患者急性代偿失调和不良心脏事件的预测因子。

• DOI: 10.1016/j.hrtlng.2023.05.012
• 发表时间: 2023
• 期刊: Heart & lung : the journal of critical care
• 作者: Kraevsky-Phillips,Karina;Sereika,SusanM;Bouzid,Zeineb;Hickey,Gavin;Callaway,CliftonW;Saba,Samir;Martin-Gill,Christian;Al-Zaiti,SalahS
• 通讯作者: Al-Zaiti,SalahS

Your neighborhood matters: A machine-learning approach to the geospatial and social determinants of health in 9-1-1 activated chest pain.

• DOI: 10.1002/nur.22199
• 发表时间: 2022-04
• 期刊: Research in nursing & health
• 影响因子: 2
• 作者: Faramand Z;Alrawashdeh M;Helman S;Bouzid Z;Martin-Gill C;Callaway C;Al-Zaiti S
• 通讯作者: Al-Zaiti S

Nonspecific electrocardiographic abnormalities are associated with increased length of stay and adverse cardiac outcomes in prehospital chest pain.

• DOI: 10.1016/j.hrtlng.2018.09.001
• 发表时间: 2019-03
• 期刊: Heart & lung : the journal of critical care
• 作者: Rivero D;Alhamaydeh M;Faramand Z;Alrawashdeh M;Martin-Gill C;Callaway C;Drew B;Al-Zaiti S
• 通讯作者: Al-Zaiti S