Chen Rong, Krejza Jaroslaw, Arkuszewski Michal, Zimmerman Robert A, Herskovits Edward H, Melhem Elias R
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, USA.
Department of Neurology, Medical University of Silesia, Katowice, Poland.
Adv Med Sci. 2017 Mar;62(1):151-157. doi: 10.1016/j.advms.2016.09.002. Epub 2017 Mar 6.
For children with sickle cell disease (SCD) and at low risk category of stroke, we aim to build a predictive model to differentiate those with decline of intelligence-quotient (IQ) from counterparts without decline, based on structural magnetic-resonance (MR) imaging volumetric analysis.
This preliminary prospective cohort study included 25 children with SCD, homozygous for hemoglobin S, with no history of stroke and transcranial Doppler mean velocities below 170cm/s at baseline. We administered the Kaufman Brief Intelligence Test (K-BIT) to each child at yearly intervals for 2-4 years. Each child underwent MR examination within 30 days of the baseline K-BIT evaluation date. We calculated K-BIT change rates, and used rate of change in K-BIT to classify children into two groups: a decline group and a non-decline group. We then generated predictive models to predict K-BIT decline/non-decline based on regional gray-matter (GM) volumes computed from structural MR images.
We identified six structures (the left median cingulate gyrus, the right middle occipital gyrus, the left inferior occipital gyrus, the right fusiform gyrus, the right middle temporal gyrus, the right inferior temporal gyrus) that, when assessed for volume at baseline, are jointly predictive of whether a child would suffer subsequent K-BIT decline. Based on these six regional GM volumes and the baseline K-BIT, we built a prognostic model using the K algorithm. The accuracy, sensitivity and specificity were 0.84, 0.78 and 0.86, respectively.
GM volumetric analysis predicts subsequent IQ decline for children with SCD.
对于患有镰状细胞病（SCD）且处于低中风风险类别的儿童，我们旨在基于结构磁共振（MR）成像体积分析建立一个预测模型，以区分智商（IQ）下降的儿童和未下降的儿童。
这项初步前瞻性队列研究纳入了25名血红蛋白S纯合子的SCD儿童，他们无中风病史，且基线时经颅多普勒平均速度低于170cm/s。我们每隔一年对每个儿童进行2至4年的考夫曼简易智力测验（K-BIT）。每个儿童在基线K-BIT评估日期后的30天内接受MR检查。我们计算了K-BIT变化率，并使用K-BIT变化率将儿童分为两组：下降组和非下降组。然后，我们基于从结构MR图像计算出的区域灰质（GM）体积生成预测模型，以预测K-BIT下降/未下降情况。
我们确定了六个结构（左侧中央扣带回、右侧枕中回、左侧枕下回、右侧梭状回、右侧颞中回、右侧颞下回），在基线时对其体积进行评估时，这些结构共同预测儿童随后是否会出现K-BIT下降。基于这六个区域GM体积和基线K-BIT，我们使用K算法建立了一个预后模型。其准确性、敏感性和特异性分别为0.84、0.78和0.86。
GM体积分析可预测SCD儿童随后的智商下降情况。
