PROGNOSIS AND VENTRICULAR CURVATURE IN TIMI TRIAL

拟议研究的主要目标是确定 心肌节段性功能障碍的诊断 脑梗塞和预后的评估可以更准确 从左心室局部曲率的分析而不是从 左室壁运动分析。期间积累的数据 NIH赞助的心肌梗死溶栓试验(TIMI)将 被分析。将使用一种先进的计算技术来 曲率测量与诊断和预后的相关性： 人工神经网络，它应用了从 处理患者数据以在输入数据之间形成关联 (室曲率)和期望的输出量(梗塞诊断、 预后)。神经网络辅助的精确度 评价左心室局部曲率可区分心肌梗死 (TIMI)来自冠状动脉正常患者的患者将 与传统室壁运动分析的诊断准确率比较 以及神经网络辅助的室壁运动分析。此外， 通过分析神经网络对生存的预测能力 函数将与预测生存的准确性进行比较。 COX回归分析。研究结果将有助于评估 心绞痛和非诊断性心电图的患者，通过改善 可识别区域功能障碍的准确性。这个 这项研究的结果也可能有助于确定哪些高危患者可能 从更积极的治疗中受益。据设想，人造的 神经网络辅助分析局部室壁曲率Will 最终应用于二维超声心动图，对其 曲率分析在理论上比壁面运动更适合，因此 实现非侵入性诊断和预测。

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.