From the Centre for Healthy Brain Ageing (CHeBA) (J.W.L., J.D.C., H.B., D.M.L., B.C.P.L., P.S.S.), UNSW, Sydney, Australia; No affiliation (D.W.D.); Department of Neurology (H.-J.B.), Seoul National University School of Medicine, Seoul National University Bundang Hospital, Seongnam; Department of Neurology (J.-S.L.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea; Department of Neurology and Laboratory of Functional Neurosciences (O.G., M.R.), University Hospital of Amiens, France; Department of Psychiatry and Neuropsychology (S.K., F.V.), School for Mental Health and Neuroscience (MHeNs), Alzheimer Center Limburg, Maastricht University; Department of Neurology (J.S.), School for Cardiovascular diseases (CARIM), Maastricht University Medical Center (MUMC+), the Netherlands; Memory Aging and Cognition Centre (C.C., X.X., E.J.C.), Departments of Pharmacology and Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore; The Second Affiliated Hospital and School of Public Health (X.X.), Zhejiang University School of Medicine, Hangzhou, China; National Neuroscience Institute (N.K.); Duke-NUS Medical School (N.K.), Singapore; University of Lille (R.B., T.D., A.-M.M.), Inserm, CHU de Lille, Lille Neuroscience and Cognition, France; Dementia Collaborative Research Centre (H.B., P.S.S.), UNSW Medicine, UNSW, Sydney, Australia; Clinic of Neurology (L.T., S.M.), UH "Alexandrovska," Medical University-Sofia; and Kaneff University Hospital (N.P.), Ruse, Bulgaria.
Neurology. 2023 Jun 6;100(23):e2331-e2341. doi: 10.1212/WNL.0000000000207281. Epub 2023 Apr 18.
Past studies on poststroke cognitive function have focused on the average performance or change over time, but few have investigated patterns of cognitive trajectories after stroke. This project used latent class growth analysis (LCGA) to identify clusters of patients with similar patterns of cognition scores over the first-year poststroke and the extent to which long-term cognitive outcome is predicted by the clusters ("trajectory groups").
Data were sought from the Stroke and Cognition consortium. LCGA was used to identify clusters of trajectories based on standardized global cognition scores at baseline (T) and at the 1-year follow-up (T). One-step individual participant data meta-analysis was used to examine risk factors for trajectory groups and association of trajectory groups with cognition at the long-term follow-up (T).
Nine hospital-based stroke cohorts with 1,149 patients (63% male; mean age 66.4 years [SD 11.0]) were included. The median time assessed at T was 3.6 months poststroke, 1.0 year at T, and 3.2 years at T. LCGA identified 3 trajectory groups, which were characterized by different mean levels of cognition scores at T (low-performance, -3.27 SD [0.94], 17%; medium-performance, -1.23 SD [0.68], 48%; and high-performance, 0.71 SD [0.77], 35%). There was significant improvement in cognition for the high-performance group (0.22 SD per year, 95% CI 0.07-0.36), but changes for the low-performance and medium-performance groups were not significant (-0.10 SD per year, 95% CI -0.33 to 0.13; 0.11 SD per year, 95% CI -0.08 to 0.24, respectively). Factors associated with the low- (vs high-) performance group include age (relative risk ratio [RRR] 1.18, 95% CI 1.14-1.23), years of education (RRR 0.61, 95% CI 0.56-0.67), diabetes (RRR 3.78, 95% CI 2.08-6.88), large artery vs small vessel strokes (RRR 2.77, 95% CI 1.32-5.83), and moderate/severe strokes (RRR 3.17, 95% CI 1.42-7.08). Trajectory groups were predictive of global cognition at T, but its predictive power was comparable with scores at T.
The trajectory of cognitive function over the first-year poststroke is heterogenous. Baseline cognitive function ∼3.6 months poststroke is a good predictor of long-term cognitive outcome. Older age, lower levels of education, diabetes, large artery strokes, and greater stroke severity are risk factors for lower cognitive performance over the first year.
既往关于卒中后认知功能的研究主要集中在平均表现或随时间的变化，但很少有研究探讨卒中后认知轨迹的模式。本项目采用潜在类别增长分析（LCGA）来识别卒中后第一年认知评分具有相似模式的患者群体，并确定这些群体（“轨迹组”）的长期认知结局的预测程度。
数据来自卒中与认知联合会。LCGA 用于根据基线（T）和 1 年随访（T）时的标准化总体认知评分识别轨迹组。采用一步个体参与者数据荟萃分析来检验轨迹组的风险因素，并探讨轨迹组与长期随访（T）时认知的相关性。
纳入了 9 个基于医院的卒中队列，共 1149 例患者（63%为男性；平均年龄 66.4 岁[标准差 11.0]）。T 时评估的中位时间为卒中后 3.6 个月，T 时为 1.0 年，T 时为 3.2 年。LCGA 识别出 3 个轨迹组，其特征在于 T 时的认知评分平均水平不同（低表现组为-3.27 SD[0.94]，占 17%；中表现组为-1.23 SD[0.68]，占 48%；高表现组为 0.71 SD[0.77]，占 35%）。高表现组的认知功能有显著改善（每年增加 0.22 SD，95%置信区间 0.07-0.36），但低表现组和中表现组的变化不显著（每年分别增加 0.10 SD，95%置信区间 0.33-0.13；0.11 SD，95%置信区间 0.08-0.24）。与低表现组（与高表现组相比）相关的因素包括年龄（相对风险比[RRR]1.18，95%置信区间 1.14-1.23）、受教育年限（RRR 0.61，95%置信区间 0.56-0.67）、糖尿病（RRR 3.78，95%置信区间 2.08-6.88）、大动脉 vs 小血管卒中（RRR 2.77，95%置信区间 1.32-5.83）和中度/重度卒中（RRR 3.17，95%置信区间 1.42-7.08）。轨迹组可以预测 T 时的总体认知，但预测能力与 T 时的评分相当。
卒中后第一年的认知功能轨迹是异质的。卒中后 3.6 个月时的基线认知功能是长期认知结局的良好预测指标。年龄较大、受教育程度较低、糖尿病、大动脉卒中和更严重的卒中是卒中后第一年认知表现较低的危险因素。
