MODELING IN ANTI CANCER DRUG DVMT PRECLINICAL STUDIES THOUGH CLINICAL TRIALS

我们将去年进展报告中所述的工作延长了 试图从一系列的唤醒中找到更好的唤醒预测因素 来自非脑电相关测量的变量。我们检查了 与唤醒相关的各种其他基于呼吸的测量方法。 以下是在逐个时代的基础上计算并比较的 对于视觉评分的唤醒指数：(1) 基于模糊的严重性指数(SI)；(2)SI的标准差；(3) 平均激发潮气量；(4)潮气量标准差； (5)吸气峰流量的最大呼吸变化； (6)最大潮气量与最小潮气量之比 音量。这些变量是从我们的13个数据库中计算出来的 睡眠呼吸紊乱的患者。以呼吸为主的 预测唤醒的灵敏度最高的变量(75%)是 SI的标准差。基于呼吸的变量与 预测唤醒的最高特异度是标准差 潮气量(81%)。然而，与此相关的敏感性 变量只有68%。因此，与呼吸相关的措施 敏感度和特异度综合得分最高的是 SI的标准差。

We extended the work described in last year's progress report by attempting to find improved predictors of arousal from an array of variables derived from non-EEG-related measurements. We examined various other respiratory-based measures for correlation to arousal. The following were calculated on an epoch-to-epoch basis and compared to the visually-scored arousal index: (1) the average value of the fuzzy-based severity index, SI; (2) the standard deviation of SI; (3) average inspired tidal volume; (4) standard deviation of tidal volume; (5) the maximum breath-to-breath change in inspiratory peak flow; and (6) the ratio of the maximum tidal volume over the minimum tidal volume. These variables were computed from our database of 13 patients with sleep-disordered breathing. The respiratory-based variable with the highest sensitivity of predicting arousal (75%) was the standard deviation of SI. The respiratory-based variable with the highest specificity in predicting arousal was the standard deviation of tidal volume (81%). However, the sensitivity associated with this variable was only 68%. Thus, the respiratory-related measure with best overall scores in both sensitivity and specificity was the standard deviation of SI.