出血性脑卒中患者皮质脊髓茎段病变负荷与弥散张量分数各向异性的结合预测预后

Tetsuo Koyama, Midori Mochizuki, Y. Uchiyama, K. Domen
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引用次数: 0

摘要

摘要 目的:本研究旨在评估将皮质脊髓束病变负荷(CST-LL)与病变半球皮质脊髓束扩散张量分数各向异性(CST-FA)相结合对运动结果的预测精度。研究方法回顾性地选取在我院发病后不久接受计算机断层扫描(CT)的普特曼和/或丘脑出血患者。CST-LL是在将CT图像与标准脑进行配准后计算得出的。扩散张量成像是在发病后第二周进行的。采用标准化的自动束成像技术计算 CST-FA。在附属康复机构出院时,我们使用脑卒中损伤评估组合(SIAS-motor total; null to full, 0 to 25)运动部分的总分对结果进行了评估。以 CST-LL 和 CST-FA 为解释变量,SIAS-运动总分为目标值,进行多变量回归分析。结果25 名患者参与了这项研究。SIAS 运动总值范围为 0 至 25(中位数为 17)。CST-LL 为 0.298 至 7.595 毫升(中位数为 2.522 毫升),病变侧 CST-FA 为 0.211 至 0.530 毫升(中位数为 0.409 毫升)。分析表明,这两个解释变量都是具有统计学意义的促成因素。估计的 t 值表明,这两个变量的贡献几乎相等。所得到的回归模型解释了目标值变异的 63.9%。结论将 CST-LL 与病变侧 CST-FA 结合可提高卒中预后预测模型的精确度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Outcome Prediction by Combining Corticospinal Tract Lesion Load with Diffusion-tensor Fractional Anisotropy in Patients after Hemorrhagic Stroke
ABSTRACT Objectives: The objective of this study was to evaluate the predictive precision of combining the corticospinal tract lesion load (CST-LL) with the diffusion-tensor fractional anisotropy of the corticospinal tract (CST-FA) in the lesioned hemispheres regarding motor outcomes. Methods: Patients with putaminal and/or thalamic hemorrhage who had undergone computed tomography (CT) soon after onset in our hospital were retrospectively enrolled. The CST-LL was calculated after registration of the CT images to a standard brain. Diffusion-tensor imaging was performed during the second week after onset. Standardized automated tractography was employed to calculate the CST-FA. Outcomes were assessed at discharge from our affiliated rehabilitation facility using total scores of the motor component of the Stroke Impairment Assessment Set (SIAS-motor total; null to full, 0 to 25). Multivariate regression analysis was performed with CST-LL and CST-FA as explanatory variables and SIAS-motor total as a target value. Results: Twenty-five patients participated in this study. SIAS-motor total ranged from 0 to 25 (median, 17). CST-LL ranged from 0.298 to 7.595 (median, 2.522) mL, and the lesion-side CST-FA ranged from 0.211 to 0.530 (median, 0.409). Analysis revealed that both explanatory variables were detected as statistically significant contributory factors. The estimated t values indicated that the contributions of these two variables were almost equal. The obtained regression model accounted for 63.9% of the variability of the target value. Conclusions: Incorporation of the CST-LL with the lesion-side CST-FA enhances the precision of the stroke outcome prediction model.
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