Nallapu Bhargav T, Petersen Kellen K, Qian Tianchen, Demirsoy Idris, Ghanbarian Elham, Davatzikos Christos, Lipton Richard B, Ezzati Ali
Saul B. Korey Department of Neurology, Albert Einstein College of Medicine, Bronx, NY.
Department of Neurology, Washington University in St. Louis, MO.
Neurology. 2025 Apr 22;104(8):e213490. doi: 10.1212/WNL.0000000000213490. Epub 2025 Mar 25.
Among the participants of Alzheimer disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals can increase power to detect treatment effects. We aimed to develop machine learning-based predictive models to identify persons unlikely to show decline on placebo treatment over 80 weeks.
We used the data from the placebo arm of EXPEDITION3 AD clinical trial and a subpopulation from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Participants in the EXPEDITION3 trial were patients with mild dementia and biomarker evidence of amyloid burden. For this study, participants were identified as those who demonstrated clinically meaningful cognitive decline (CMCD) or cognitively stable (CS) at final visit of the trial (week 80). Machine learning-based classifiers were trained to classify participants into CMCD vs CS groups using combinations of demographics, APOE genotype, neuropsychological tests, and biomarkers (volumetric MRI). The results were developed in 70% of the EXPEDITION3 placebo sample using 5-fold cross-validation. Trained models were then used to classify the participants in an internal validation sample and an external matched sample ADNI.
Eight hundred ninety-four of the 1,072 participants in the placebo arm of the EXPEDITION3 trial had necessary follow-up data, who were on average aged 72.7 (±7.7) years and 59% female. 55.8% of those participants showed CMCD (∼2 years younger than those without) at the final visit. In the independent validation sample within the EXPEDITION3 data, all the models showed high sensitivity and modest specificity. Positive predictive values (PPVs) of models were at least 11% higher than base prevalence of CMCD observed at the end of the trial. The subset of matched ADNI participants (ADNI, N = 105) were aged 74.5 (±6.4) years and 46% female. The models that were validated in ADNI also showed high sensitivity, modest specificity, and PPVs of at least 15% higher than the base prevalence in ADNI.
Our results indicate that predictive models have the potential to improve the design of AD trials through selective inclusion and exclusion criteria based on expected cognitive decline. Such predictive models need further validation across data from different AD clinical trials.
在阿尔茨海默病（AD）治疗试验的参与者中，40%的人在80周的随访期内未出现认知衰退。识别并排除这些个体能够增强检测治疗效果的效能。我们旨在开发基于机器学习的预测模型，以识别在80周安慰剂治疗期间不太可能出现衰退的个体。
我们使用了EXPEDITION3 AD临床试验安慰剂组的数据以及阿尔茨海默病神经影像学倡议（ADNI）的一个亚组数据。EXPEDITION3试验的参与者为患有轻度痴呆且有淀粉样蛋白负荷生物标志物证据的患者。在本研究中，参与者被确定为在试验最终访视（第80周）时出现具有临床意义的认知衰退（CMCD）或认知稳定（CS）的人。基于机器学习的分类器通过人口统计学、APOE基因型、神经心理学测试和生物标志物（容积MRI）的组合进行训练，以将参与者分类为CMCD组和CS组。结果在EXPEDITION3安慰剂样本的70%中使用5折交叉验证得出。然后使用训练好的模型对内部验证样本和外部匹配样本ADNI中的参与者进行分类。
EXPEDITION3试验安慰剂组的1072名参与者中有894人有必要的随访数据，他们的平均年龄为72.7（±7.7）岁，女性占59%。在最终访视时，这些参与者中有55.8%出现了CMCD（比未出现者年轻约2岁）。在EXPEDITION3数据中的独立验证样本中，所有模型均显示出高敏感性和适度的特异性。模型的阳性预测值（PPV）比试验结束时观察到的CMCD基础患病率至少高11%。匹配的ADNI参与者亚组（ADNI，N = 105）的年龄为74.5（±6.4）岁，女性占46%。在ADNI中验证的模型也显示出高敏感性、适度的特异性，且PPV比ADNI中的基础患病率至少高15%。
我们的结果表明，预测模型有潜力通过基于预期认知衰退的选择性纳入和排除标准来改进AD试验的设计。此类预测模型需要在来自不同AD临床试验的数据上进一步验证。
