PROGNOSIS AND VENTRICULAR CURVATURE IN TIMI TRIAL

TIMI试验中的预后和心室曲率

• 批准号: 3426782
• 负责人: FLORENCE SHEEHAN
• 金额: $ 7.55万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-01 至 1993-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3426782
• 关键词: angiography; artificial intelligence; brain ventriculography; computational neuroscience; computer assisted diagnosis; diagnosis design /evaluation; fibrinolysis; heart dimension /size; heart function; heart motion; heart ventricle; myocardial infarct sizing; myocardial infarction; noninvasive diagnosis; prognosis

The principle objective of the proposed research is to determine whether the diagnosis of regional dysfunction indicative of myocardial infarction, and the assessment of prognosis can be more accurately performed from analysis of regional left ventricular curvature than from analysis of left ventricular wall motion. Data accumulated during the NIH sponsored trial of Thrombolysis In Myocardial Infarction (TIMI) will be analyzed. An advanced computational technique will be used to correlate curvature measurements with diagnosis and with prognosis: the artificial neural network, which applies the experience gained from processing patient data to form associations between the input data (ventricular curvature) and the desired output (infarct diagnosis, prognosis). The accuracy with which the neural network assisted evaluation of regional left ventricular curvature can distinguish infarct (TIMI) patients from patients with normal coronary arteries will be compared with the diagnostic accuracy of traditional wall motion analysis and of neural network assisted analysis of wall motion. In addition, the ability of the neural network to predict survival from analysis of function will be compared with the accuracy of predicting survival using Cox regression analysis. The results of the study will help to evaluate patients with angina and nondiagnostic electrocardiograms, by improving the accuracy with which regional dysfunction can be identified. The results of the study may also help identify patients at high risk who may benefit from more aggressive therapy. It is envisioned that artificial neural network assisted analysis of regional ventricular curvature will ultimately be applied to two-dimensional echocardiography, to which curvature analysis is theoretically more suitable than wall motion, thus enabling noninvasive diagnosis and prognostication.

拟议研究的主要目的是确定是否 区域功能障碍的诊断指示心肌 梗塞，预后的评估可以更准确 从区域左心室曲率的分析中进行的 左心室壁运动的分析。 在 NIH赞助的心肌梗塞（TIMI）中溶栓的试验将 可以分析。 先进的计算技术将用于 将曲率测量与诊断和预后相关： 人工神经网络，适用于从中获得的经验 处理患者数据以在输入数据之间形成关联 （心室曲率）和所需的输出（梗塞诊断， 预后）。 神经网络协助的准确性 区域左心室曲率的评估可以区分梗塞 （TIMI）来自正常冠状动脉患者的患者将是 与传统墙运动分析的诊断准确性相比 和神经网络辅助壁运动的分析。 另外， 神经网络从分析中预测生存的能力 将功能与预测使用生存的精度进行比较 Cox回归分析。 研究结果将有助于评估 心绞痛和非诊断心电图的患者，通过改善 可以确定区域功能障碍的准确性。 这 该研究的结果还可能有助于识别出高风险的患者 受益于更具侵略性的疗法。 可以预见那个人造 神经网络辅助区域曲率分析将 最终应用于二维超声心动图，对此 曲率分析在理论上比壁运动更适合 实现无创诊断和预后。