Cognitive Neuroscience Division of the Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA.
Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, North Carolina, USA.
Alzheimers Dement. 2021 Oct;17(10):1698-1708. doi: 10.1002/alz.12336. Epub 2021 May 14.
Identifying the course of Alzheimer's disease (AD) for individual patients is important for numerous clinical applications. Ideally, prognostic models should provide information about a range of clinical features across the entire disease process. Previously, we published a new comprehensive longitudinal model of AD progression with inputs/outputs covering 11 interconnected clinical measurement domains.
Here, we (1) validate the model on an independent cohort; and (2) demonstrate the model's utility in clinical applications by projecting changes in 6 of the 11 domains.
Survival and prevalence curves for two representative outcomes-mortality and dependency-generated by the model accurately reproduced the observed curves both overall and for patients subdivided according to risk levels using an independent Cox model.
The new model, validated here, effectively reproduces the observed course of AD from an initial visit assessment, allowing users to project coordinated developments for individual patients of multiple disease features.
确定个体患者的阿尔茨海默病（AD）病程对于许多临床应用非常重要。理想情况下，预后模型应提供有关整个疾病过程中一系列临床特征的信息。此前，我们发表了一种新的 AD 进展综合纵向模型，其输入/输出涵盖 11 个相互关联的临床测量领域。
在这里，我们（1）在独立队列中验证模型；（2）通过预测 11 个领域中的 6 个领域的变化，展示模型在临床应用中的实用性。
模型生成的两个代表性结果——死亡率和依赖性的生存和患病率曲线，无论是整体还是根据风险水平使用独立 Cox 模型细分的患者，都准确地再现了观察到的曲线。
在此验证的新模型有效地再现了从初始就诊评估中观察到的 AD 病程，允许用户预测多个疾病特征的个体患者的协调发展。
