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

加速型非ST段抬高心肌事件的心电图检测 胸痛分类(ECG-SMART-2) 摘要 显然有必要开发改进的工具来对寻求胸部急救的患者的风险进行分层 疼痛，这是在急性护理环境中遇到的最常见和可能致命的情况之一。12- 导联心电图一直是胸痛初步评估的支柱，但目前只对一小部分患者有诊断作用 ST段抬高心肌梗死患者亚组。在过去的资助期内，我们建立了 我们已知的多医院、结果相关、院前12导联心电图库的最大数据库(n=4,132)。 利用这个多专家、多层次的地面事实注释数据库，我们开发并验证了小说、 基于机器学习的心电解释算法，可以识别非ST段抬高的急性冠状动脉 事件。使用最先进的可解释性工具包，我们识别出机械的心电信号 与缺血有关，可作为急性冠脉综合征的合理标记物。我们现在的目标是 这些通过在床边扩展和构建这些模型来广泛应用于临床的努力 验证和实时临床部署。本次续签申请的具体目标是：1)建设和 外部验证基于心电的多任务智能决策支持系统；2)构建和部署真正的 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) 要对此智能心电系统进行前瞻性临床验证，包括静默部署和 在两个临床站点进行评估。最终的成果是用于检测的智能心电解释系统 以及对疑似急性冠脉综合征患者进行分层，以充分准备部署到 旨在改善非ST段抬高冠状动脉综合征预后的临床试验。这种智能系统， 结合训练有素的急救人员(医生、护士和护理人员)的判断， 将更准确地识别急性冠状动脉闭塞患者进行超早期干预。这个系统 将简化对非特定胸痛的护理，而不仅仅是昂贵和耗时的通宵 观察一系列心肌酶和刺激性试验。

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.