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.

拟议研究的主要目标是确定是否 心肌局部功能障碍的诊断 可以更准确地评估梗死的预后 根据区域左心室曲率分析进行 分析左心室壁运动。 期间积累的数据 NIH 赞助的心肌梗塞溶栓试验 (TIMI) 将 进行分析。 将使用先进的计算技术 将曲率测量与诊断和预后相关联： 人工神经网络，它应用了从 处理患者数据以形成输入数据之间的关联 （心室曲率）和所需的输出（梗塞诊断， 预测）。 神经网络辅助的准确性 评估局部左心室曲率可区分梗塞 (TIMI) 冠状动脉正常的患者将被 与传统室壁运动分析的诊断准确性比较 以及神经网络辅助室壁运动分析。 此外， 神经网络通过分析预测生存的能力 函数将与使用预测生存的准确性进行比较 考克斯回归分析。 研究结果将有助于评估 患有心绞痛和非诊断性心电图的患者，通过改善 识别区域功能障碍的准确性。 这 研究结果还可能有助于识别高危患者 受益于更积极的治疗。 预计人工 神经网络辅助分析局部心室曲率 最终应用于二维超声心动图检查 理论上曲率分析比壁运动更合适，因此 实现无创诊断和预测。