Grassi Massimiliano, Rouleaux Nadine, Caldirola Daniela, Loewenstein David, Schruers Koen, Perna Giampaolo, Dumontier Michel
Department of Clinical Neurosciences, Hermanas Hospitalarias, Villa San Benedetto Menni Hospital, FoRiPsi, Albese con Cassano, Italy.
Department of Biomedical Sciences, Humanitas University, Milan, Italy.
Front Neurol. 2019 Jul 16;10:756. doi: 10.3389/fneur.2019.00756. eCollection 2019.
Despite the increasing availability in brain health related data, clinically translatable methods to predict the conversion from Mild Cognitive Impairment (MCI) to Alzheimer's disease (AD) are still lacking. Although MCI typically precedes AD, only a fraction of 20-40% of MCI individuals will progress to dementia within 3 years following the initial diagnosis. As currently available and emerging therapies likely have the greatest impact when provided at the earliest disease stage, the prompt identification of subjects at high risk for conversion to AD is of great importance in the fight against this disease. In this work, we propose a highly predictive machine learning algorithm, based only on non-invasively and easily in-the-clinic collectable predictors, to identify MCI subjects at risk for conversion to AD. The algorithm was developed using the open dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI), employing a sample of 550 MCI subjects whose diagnostic follow-up is available for at least 3 years after the baseline assessment. A restricted set of information regarding sociodemographic and clinical characteristics, neuropsychological test scores was used as predictors and several different supervised machine learning algorithms were developed and ensembled in final algorithm. A site-independent stratified train/test split protocol was used to provide an estimate of the generalized performance of the algorithm. The final algorithm demonstrated an AUROC of 0.88, sensitivity of 77.7%, and a specificity of 79.9% on excluded test data. The specificity of the algorithm was 40.2% for 100% sensitivity. The algorithm we developed achieved sound and high prognostic performance to predict AD conversion using easily clinically derived information that makes the algorithm easy to be translated into practice. This indicates beneficial application to improve recruitment in clinical trials and to more selectively prescribe new and newly emerging early interventions to high AD risk patients.
尽管与脑健康相关的数据越来越多，但仍缺乏可临床转化的方法来预测轻度认知障碍（MCI）向阿尔茨海默病（AD）的转化。虽然MCI通常先于AD出现，但在初次诊断后的3年内，只有20%-40%的MCI个体将进展为痴呆症。由于目前可用的和新出现的疗法在疾病最早阶段提供时可能具有最大的影响，因此迅速识别有转化为AD高风险的受试者在对抗这种疾病方面非常重要。在这项工作中，我们提出了一种高度预测性的机器学习算法，该算法仅基于非侵入性且易于在临床收集的预测指标，以识别有转化为AD风险的MCI受试者。该算法是使用来自阿尔茨海默病神经影像倡议（ADNI）的开放数据集开发的，采用了550名MCI受试者的样本，其诊断随访在基线评估后至少可用3年。一组关于社会人口统计学和临床特征、神经心理学测试分数的受限信息被用作预测指标，并开发了几种不同的监督机器学习算法，并将其整合到最终算法中。使用与地点无关的分层训练/测试分割协议来估计算法的广义性能。最终算法在排除的测试数据上的曲线下面积（AUROC）为0.88，灵敏度为77.7%，特异性为79.9%。该算法在灵敏度为100%时的特异性为40.2%。我们开发的算法使用易于从临床得出的信息实现了良好且高的预后性能，以预测AD转化，这使得该算法易于转化为实践。这表明该算法在改善临床试验招募以及更有选择性地为AD高风险患者开新的和新出现的早期干预措施方面具有有益的应用。
