Mubeen Asim M, Asaei Ali, Bachman Alvin H, Sidtis John J, Ardekani Babak A
The Nathan S. Kline institute for psychiatric research, 140, Old Orangeburg road, 10962 Orangeburg, New York, USA.
The Nathan S. Kline institute for psychiatric research, 140, Old Orangeburg road, 10962 Orangeburg, New York, USA; Department of psychiatry, New York university school of medicine, New York, USA.
J Neuroradiol. 2017 Oct;44(6):381-387. doi: 10.1016/j.neurad.2017.05.008. Epub 2017 Jul 2.
Early prediction of incipient Alzheimer's disease (AD) dementia in individuals with mild cognitive impairment (MCI) is important for timely therapeutic intervention and identifying participants for clinical trials at greater risk of developing AD. Methods to predict incipient AD in MCI have mostly utilized cross-sectional data. Longitudinal data enables estimation of the rate of change of variables, which along with the variable levels have been shown to improve prediction power. While some efforts have already been made in this direction, all previous longitudinal studies have been based on observation periods longer than one year, hence limiting their practical utility. It remains to be seen if follow-up evaluations within shorter intervals can significantly improve the accuracy of prediction in this problem. Our aim was to determine the added value of incorporating 6-month longitudinal data for predicting progression from MCI to AD.
Using 6-months longitudinal data from 247 participants with MCI, we trained two Random Forest classifiers to distinguish between progressive MCI (n=162) and stable MCI (n=85) cases. These models utilized structural MRI, neurocognitive assessments, and demographic information. The first model (cross-sectional) only used baseline data. The second model (longitudinal) used data from both baseline and a 6-month follow-up evaluation allowing the model to additionally incorporate biomarkers' rate of change.
The longitudinal model (AUC=0.87; accuracy=80.2%) performed significantly better (P<0.05) than the cross-sectional model (AUC=0.82; accuracy=71.7%).
Short-term longitudinal assessments significantly enhance the performance of AD prediction models.
对轻度认知障碍（MCI）个体的早期阿尔茨海默病（AD）痴呆进行预测，对于及时进行治疗干预以及确定更易发展为AD的临床试验参与者非常重要。预测MCI中早期AD的方法大多使用横断面数据。纵向数据能够估计变量的变化率，并且已证明变量变化率与变量水平一起可提高预测能力。虽然已经在这个方向上做出了一些努力，但之前所有的纵向研究都基于超过一年的观察期，因此限制了它们的实际效用。较短间隔内的随访评估是否能显著提高这个问题的预测准确性还有待观察。我们的目的是确定纳入6个月纵向数据对预测从MCI进展为AD的附加价值。
我们使用来自247名MCI参与者的6个月纵向数据，训练了两个随机森林分类器，以区分进展性MCI（n = 162）和稳定性MCI（n = 85）病例。这些模型利用了结构MRI、神经认知评估和人口统计学信息。第一个模型（横断面模型）仅使用基线数据。第二个模型（纵向模型）使用基线数据和6个月随访评估的数据，使模型能够额外纳入生物标志物的变化率。
纵向模型（AUC = 0.87；准确率 = 80.2%）的表现显著优于横断面模型（AUC = 0.82；准确率 = 71.7%）（P < 0.05）。
短期纵向评估显著提高了AD预测模型的性能。
