Dobrynina L A, Gadzhieva Z Sh, Shamtieva K V, Kremneva E I, Filatov A S, Bitsieva E T, Mirokova E D, Krotenkova M V
Research Center of Neurology, Moscow, Russia.
Zh Nevrol Psikhiatr Im S S Korsakova. 2022;122(4):52-60. doi: 10.17116/jnevro202212204152.
To search for sensitive predictors of cognitive impairment (CI) and an integrative index of their severity.
We assessed CI and diffusion-tensor MRI (DT-MRI) in the regions of interest (ROI) significant for CI in 74 patients (48 women, mean age 60.6±6.9 years) with cerebral small vessel disease (CSVD). The results of DT-MRI were used to construct a predictive model of CI using binary logistic regression and to calculate an integrative index of CI severity.
According to the constructed model, the predictors of CI were axial diffusivity (AD) of posterior frontal periventricular normal-appearing white matter (pvNAWM), right middle cingulum bundle (CB) and mid-posterior corpus callosum (CC). ROC analysis showed strong model predictive power for CI in cSVD (AUC (95% CI): 0.845 (0.740-0.950)). The threshold value of the AD predictors model for CI in cSVD was 0.53 (sensitivity 84%, specificity 76%). AD predictors of CI showed significant correlations with white matter hyperintensities volume and MoCA scores. The presence of CI as measured by neuropsychological testing and regression equation solution was corresponded to individual AD predictors of patients exceeding the CI model's threshold.
Disturbances in the AD of pvNAWM, right middle CB and mid-posterior CC associated with axonal damage are a predominant factor in the development of CI in CSVD. The predictors of CI and the integrative index of CI severity calculated on their basis can potentially be used as a tool for assessing the severity of CI and the effectiveness of treatment, as well as in clarifying the interaction between vascular and degenerative pathology and in developing measures for the prevention of CI in patients with MRI signs of cSVD.
寻找认知障碍（CI）的敏感预测指标及其严重程度的综合指数。
我们对74例（48名女性，平均年龄60.6±6.9岁）患有脑小血管病（CSVD）且CI相关感兴趣区域（ROI）的CI和扩散张量磁共振成像（DT-MRI）进行了评估。DT-MRI结果用于通过二元逻辑回归构建CI预测模型并计算CI严重程度的综合指数。
根据构建的模型，CI的预测指标为额叶后室周正常外观白质（pvNAWM）、右侧中间扣带束（CB）和胼胝体中后部（CC）的轴向扩散率（AD）。ROC分析显示该模型对cSVD中CI具有较强的预测能力（AUC（95%CI）：0.845（0.740-0.950））。cSVD中CI的AD预测指标模型的阈值为0.53（敏感性84%，特异性76%）。CI的AD预测指标与白质高信号体积和蒙特利尔认知评估量表（MoCA）评分显著相关。通过神经心理学测试和回归方程求解测得的CI存在情况与超过CI模型阈值的患者个体AD预测指标相对应。
与轴突损伤相关的pvNAWM、右侧中间CB和胼胝体中后部CC的AD紊乱是CSVD中CI发生的主要因素。CI的预测指标及其在此基础上计算出的CI严重程度综合指数有可能用作评估CI严重程度和治疗效果的工具，也可用于阐明血管性和退行性病变之间的相互作用以及制定预防有cSVD MRI征象患者发生CI的措施。
