Ezzati Ali, Lipton Richard B
Department of Neurology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA.
J Alzheimers Dis. 2020;74(1):55-63. doi: 10.3233/JAD-190822.
The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). One strategy to boost the power of trials is to enroll individuals who are more likely to progress targeted using data-driven predictive models.
To investigate if machine learning (ML) models can effectively predict clinical disease progression (cognitive decline) in mild-to-moderate AD patients during the timeframe of a phase III clinical trial.
Data from 202 participants with a diagnosis of AD at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) was used to train ML classifiers that can differentiate between individuals who had declining cognitive function (DC) and individuals with stable cognitive function (SC). DC was defined as any downward change in the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-cog) score over 12 months of follow-up. SC was defined by the absence of decline in ADAS-cog. Trained models were applied to data from 77 participants from the placebo arm of the phase III trial of Semagacestat (LFAN study) to identify subgroups of SC versus DC.
Only 74.8% of ADNI participants and 63.6% of LFAN participants had cognitive decline after one year of follow up. K-nearest neighbors (kNN) classifier had an accuracy of 68.3%, sensitivity of 80.1%, and specificity of 33.3% for identifying decliners in ADNI (training sample). In LFAN (validation sample), the model showed an overall accuracy of 61.3%, sensitivity of 65.5%, and specificity of 47.0% in identifying decliners at the 12 months of follow-up. The model had a positive predictive value of 80.8%, which was 17.2% more than the base prevalence of decliners.
Machine learning predictive models can be effectively used to boost the power of clinical trials by reducing the sample size.
阿尔茨海默病（AD）临床试验的理想参与者应在未接受治疗（即安慰剂组）时出现认知衰退，并且对所研究的治疗干预措施（即药物组）有反应。提高试验效力的一种策略是招募那些使用数据驱动的预测模型更有可能出现目标进展的个体。
研究机器学习（ML）模型能否在III期临床试验期间有效预测轻度至中度AD患者的临床疾病进展（认知衰退）。
来自阿尔茨海默病神经影像倡议（ADNI）的202名基线诊断为AD的参与者的数据用于训练ML分类器，该分类器可区分认知功能下降（DC）的个体和认知功能稳定（SC）的个体。DC定义为在12个月的随访期间阿尔茨海默病评估量表认知子量表（ADAS-cog）评分的任何下降变化。SC定义为ADAS-cog无下降。将训练好的模型应用于司美吉布（LFAN研究）III期试验安慰剂组的77名参与者的数据，以识别SC与DC的亚组。
在随访一年后，只有74.8%的ADNI参与者和63.6%的LFAN参与者出现认知衰退。K近邻（kNN）分类器在识别ADNI（训练样本）中的衰退者时，准确率为68.3%，灵敏度为80.1%，特异性为33.3%。在LFAN（验证样本）中，该模型在随访12个月时识别衰退者的总体准确率为61.3%，灵敏度为65.5%，特异性为47.0%。该模型的阳性预测值为80.8%，比衰退者的基础患病率高17.2%。
机器学习预测模型可通过减少样本量有效用于提高临床试验的效力。
