Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, USA.
Neurology. 2012 Jun 5;78(23):1853-9. doi: 10.1212/WNL.0b013e318258f799. Epub 2012 May 9.
To develop multivariate models for prediction of early motor deficit improvement in acute stroke patients with focal extremity paresis, using admission clinical and imaging data.
Eighty consecutive patients with motor deficit due to first-ever unilateral stroke underwent CT perfusion (CTP) within 9 hours of symptom onset. Limb paresis was prospectively assessed using admission and discharge NIH Stroke Scale (NIHSS) scoring. CTP scans were coregistered to the MNI-152 brain space and subsegmented to 146 pairs of cortical/subcortical regions based on preset atlases. Stepwise multivariate binary logistic regressions were performed to determine independent clinical and imaging predictors of paresis improvement.
The rates of early motor deficit improvement were 18/49 (37%), 15/42 (36%), 8/25 (32%), and 7/23 (30%) for the right arm, right leg, left arm, and left leg, respectively. Admission NIHSS was the only independent clinical predictor of early limb motor deficit improvement. Relative CTP values of the inferior frontal lobe white matter, lower insular cortex, superior temporal gyrus, retrolenticular portion of internal capsule, postcentral gyrus, precuneus parietal gyri, putamen, and caudate nuclei were also independent predictors of motor improvement of different limbs. The multivariate predictive models of motor function improvement for each limb had 84%-92% accuracy, 79%-100% positive predictive value, 75%-94% negative predictive value, 83%-88% sensitivity, and 80%-100% specificity.
We developed pilot multivariate models to predict early motor functional improvement in acute stroke patients using admission NIHSS and atlas-based location-weighted CTP data. These models serve as a "proof-of-concept" for prospective location-weighted imaging prediction of clinical outcome in acute stroke.
利用发病后早期的临床和影像学数据，为伴有局灶性肢体瘫痪的急性脑卒中患者建立预测早期运动功能改善的多变量模型。
连续 80 例因首次单侧脑卒中出现运动功能障碍的患者在发病后 9 小时内行 CT 灌注（CTP）检查。采用入院和出院时的 NIH 卒中量表（NIHSS）评分对肢体瘫痪进行前瞻性评估。将 CTP 扫描与 MNI-152 脑空间配准，并根据预设图谱将其细分为 146 对皮质/皮质下区域。采用逐步多元二项逻辑回归确定与瘫痪改善相关的独立临床和影像学预测因素。
右侧上肢、右侧下肢、左侧上肢和左侧下肢的早期运动功能障碍改善率分别为 18/49（37%）、15/42（36%）、8/25（32%）和 7/23（30%）。入院 NIHSS 是早期肢体运动功能障碍改善的唯一独立临床预测因素。额下回白质、下岛叶皮质、颞上回、内囊后肢、中央后回、楔前叶、壳核和尾状核的相对 CTP 值也是不同肢体运动改善的独立预测因素。各肢体运动功能改善的多元预测模型具有 84%-92%的准确率、79%-100%的阳性预测值、75%-94%的阴性预测值、83%-88%的敏感度和 80%-100%的特异度。
我们利用入院 NIHSS 和基于图谱的位置加权 CTP 数据，建立了预测急性脑卒中患者早期运动功能改善的预测模型。这些模型为急性脑卒中患者基于位置加权影像学预测临床结局提供了“概念验证”。
