复发缓解型多发性硬化症患者认知轨迹与残疾进展之间的关联
Association Between Cognitive Trajectories and Disability Progression in Patients With Relapsing-Remitting Multiple Sclerosis.
作者信息
Merlo Daniel, Stankovich Jim, Bai Claire, Kalincik Tomas, Zhu Chao, Gresle Melissa, Lechner-Scott Jeannette, Kilpatrick Trevor, Barnett Michael, Taylor Bruce, Darby David, Butzkueven Helmut, Van der Walt Anneke
机构信息
From the Department of Neuroscience (D.M., J.S., C.Z., M.G., H.B., A.V.d.W.), Central Clinical School, Monash University; CORe (C.B., T. Kalincik), Department of Medicine at RMH, University of Melbourne; Department of Neurology (T. Kalincik, T. Kilpatrick), Royal Melbourne Hospital, Parkville; Department of Neurology (J.L.-S.), John Hunter Hospital; School of Medicine and Public Health (J.L.-S.), University of Newcastle; Florey Institute of Neuroscience and Mental Health (T. Kilpatrick, D.D.), Melbourne; Brain and Mind Centre (M.B.), University of Sydney; Department of Neurology (B.T.), Royal Hobart Hospital; Department of Neurology (D.D.), Box Hill Hospital; and Department of Neurology (H.B., A.V.d.W.), MSNI Service, Alfred Health, Melbourne, Australia.
出版信息
Neurology. 2021 Nov 16;97(20):e2020-e2031. doi: 10.1212/WNL.0000000000012850. Epub 2021 Sep 23.
BACKGROUND AND OBJECTIVES
Longitudinal cognitive trajectories in multiple sclerosis are heterogeneous and difficult to measure. We aimed to identify discrete longitudinal reaction time trajectories in relapsing-remitting multiple sclerosis using a computerized cognitive battery and to assess the association between trajectories of reaction time and disability progression.
METHODS
All participants serially completed computerized reaction time tasks measuring psychomotor speed, visual attention, and working memory. Participants completed at least 3 testing sessions over a minimum of 180 days. Longitudinal reaction times were modeled with latent class mixed models to identify groups of individuals sharing similar latent characteristics. Optimal models were validated for consistency and baseline associations with class membership tested using multinomial logistic regression. Interclass differences in the probability of reaction time worsening and the probability of 6-month confirmed disability progression were assessed with survival analysis.
RESULTS
A total of 460 people with relapsing-remitting multiple sclerosis were included in the analysis. For each task of the MSReactor battery, the optimal model comprised 3 latent classes. All MSReactor tasks could identify a group with high probability of reaction time slowing. The visual attention and working memory tasks could identify a group of participants who were 3.7 and 2.6 times more likely to experience a 6-month confirmed disability progression, respectively. Participants could be classified into predicted cognitive trajectories after just 5 tests with 64% to 89% accuracy.
DISCUSSION
Latent class modeling of longitudinal cognitive data collected by a computerized battery identified patients with worsening reaction times and increased risk of disability progression. Slower baseline reaction time, age, and disability increased assignment into this trajectory. Monitoring of cognition in clinical practice with computerized tests may enable detection of cognitive change trajectories and people with relapsing-remitting multiple sclerosis at risk of disability progression.
背景与目的
多发性硬化症的纵向认知轨迹具有异质性且难以测量。我们旨在使用计算机化认知测试组合来识别复发缓解型多发性硬化症中离散的纵向反应时间轨迹，并评估反应时间轨迹与残疾进展之间的关联。
方法
所有参与者连续完成测量心理运动速度、视觉注意力和工作记忆的计算机化反应时间任务。参与者在至少180天内完成至少3次测试。纵向反应时间采用潜在类别混合模型进行建模，以识别具有相似潜在特征的个体组。使用多项逻辑回归对最佳模型的一致性和与类别归属的基线关联进行验证。通过生存分析评估反应时间恶化概率和6个月确诊残疾进展概率的组间差异。
结果
共有460例复发缓解型多发性硬化症患者纳入分析。对于MSReactor测试组合的每项任务，最佳模型包含3个潜在类别。所有MSReactor任务都能识别出反应时间减慢可能性高的一组。视觉注意力和工作记忆任务能分别识别出6个月确诊残疾进展可能性高3.7倍和2.6倍的一组参与者。仅经过5次测试，参与者就能以64%至89%的准确率被分类到预测的认知轨迹中。
讨论
通过计算机化测试组合收集的纵向认知数据的潜在类别建模识别出反应时间恶化且残疾进展风险增加的患者。基线反应时间较慢、年龄较大和残疾程度较高会增加被分到该轨迹的可能性。在临床实践中使用计算机化测试监测认知可能有助于检测认知变化轨迹以及有残疾进展风险的复发缓解型多发性硬化症患者。
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