Lim Jae-Sung, Noh Maengseok, Kim Beom Joon, Han Moon-Ku, Kim SangYun, Jang Myung Suk, Lee Youngjo, Ha Il Do, Yu Kyung-Ho, Lee Byung-Chul, Kang Yeonwook, Lee Juneyoung, Bae Hee-Joon
Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea.
Department of Statistics, Pukyong National University, Busan, Republic of Korea.
Dement Geriatr Cogn Disord. 2017;44(5-6):311-319. doi: 10.1159/000484477. Epub 2018 Jan 26.
BACKGROUND/AIMS: Most studies of poststroke cognitive impairment (PSCI) have analyzed cognitive levels at specific time points rather than their changes over time. Furthermore, they seldom consider correlations between cognitive domains. We aimed to investigate the effects of these methodological considerations on determining significant PSCI predictors in a longitudinal stroke cohort.
In patients who underwent neuropsychological tests at least twice after stroke, we adopted a multilevel hierarchical mixed-effects model with domain-specific cognitive changes and a multivariate model for multiple outcomes to reflect their correlations.
We enrolled 375 patients (median follow-up of 34.1 months). Known predictors of PSCI were generally associated with cognitive levels; however, most of the statistical significances disappeared when cognitive changes were set as outcomes, except age for memory, prior stroke and baseline cognition for executive/attention domain, and baseline cognition for visuospatial function. The multivariate analysis which considered multiple outcomes simultaneously further altered these associations.
This study shows that defining outcomes as changes over time and reflecting correlations between outcomes may affect the identification of predictors of PSCI.
背景/目的:大多数关于卒中后认知障碍(PSCI)的研究分析了特定时间点的认知水平,而非其随时间的变化。此外,这些研究很少考虑认知领域之间的相关性。我们旨在研究这些方法学考量因素对在纵向卒中队列中确定PSCI显著预测因素的影响。
在卒中后至少接受两次神经心理学测试的患者中,我们采用了具有特定领域认知变化的多层次分层混合效应模型和反映其相关性的多变量多结局模型。
我们纳入了375例患者(中位随访时间为34.1个月)。已知的PSCI预测因素通常与认知水平相关;然而,当将认知变化作为结局时,大多数统计学显著性消失,除了记忆方面的年龄、执行/注意力领域的既往卒中及基线认知,以及视觉空间功能的基线认知。同时考虑多个结局的多变量分析进一步改变了这些关联。
本研究表明,将结局定义为随时间的变化并反映结局之间的相关性可能会影响PSCI预测因素的识别。