Barrett Matthew J, Negida Ahmed, Mukhopadhyay Nitai, Kim Jin K, Nawaz Huma, Jose Jefin, Testa Claudia
Department of Neurology, Virginia Commonwealth University, Richmond, Virginia, USA.
Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA.
Mov Disord. 2024 May;39(5):855-862. doi: 10.1002/mds.29749. Epub 2024 Mar 11.
Intrastriatal delivery of potential therapeutics in Huntington's disease (HD) requires sufficient caudate and putamen volumes. Currently, volumetric magnetic resonance imaging is rarely done in clinical practice, and these data are not available in large research cohorts such as Enroll-HD.
The objective of this study was to investigate whether predictive models can accurately classify HD patients who exceed caudate and putamen volume thresholds required for intrastriatal therapeutic interventions.
We obtained and merged data for 1374 individuals across three HD cohorts: IMAGE-HD, PREDICT-HD, and TRACK-HD/TRACK-ON. We imputed missing data for clinical variables with >72% non-missing values and used the model-building algorithm BORUTA to identify the 10 most important variables. A random forest algorithm was applied to build a predictive model for putamen volume >2500 mm and caudate volume >2000 mm bilaterally. Using the same 10 predictors, we constructed a logistic regression model with predictors significant at P < 0.05.
The random forest model with 1000 trees and minimal terminal node size of 5 resulted in 83% area under the curve (AUC). The logistic regression model retaining age, CAG repeat size, and symbol digit modalities test-correct had 85.1% AUC. A probability cutoff of 0.8 resulted in 5.4% false positive and 66.7% false negative rates.
Using easily obtainable clinical data and machine learning-identified initial predictor variables, random forest, and logistic regression models can successfully identify people with sufficient striatal volumes for inclusion cutoffs. Adopting these models in prescreening could accelerate clinical trial enrollment in HD and other neurodegenerative disorders when volume cutoffs are necessary enrollment criteria. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
在亨廷顿舞蹈病（HD）中，向纹状体内递送潜在治疗药物需要足够的尾状核和壳核体积。目前，容积磁共振成像在临床实践中很少进行，并且在诸如Enroll-HD等大型研究队列中无法获得这些数据。
本研究的目的是调查预测模型是否能够准确分类超过纹状体内治疗干预所需尾状核和壳核体积阈值的HD患者。
我们获取并合并了来自三个HD队列（IMAGE-HD、PREDICT-HD和TRACK-HD/TRACK-ON）的1374名个体的数据。我们对非缺失值>72%的临床变量的缺失数据进行了插补，并使用模型构建算法BORUTA来识别10个最重要的变量。应用随机森林算法构建双侧壳核体积>2500 mm和尾状核体积>2000 mm的预测模型。使用相同的10个预测因子，我们构建了一个逻辑回归模型，其中预测因子在P < 0.05时具有显著性。
具有1000棵树且最小终端节点大小为5的随机森林模型的曲线下面积（AUC）为83%。保留年龄、CAG重复长度和符号数字模态测试校正值的逻辑回归模型的AUC为85.1%。概率截断值为0.8时，假阳性率为5.4%，假阴性率为66.7%。
使用易于获得的临床数据以及机器学习识别的初始预测变量，随机森林和逻辑回归模型能够成功识别出具有足够纹状体体积以符合纳入截断标准的人群。当体积截断是必要的纳入标准时，在预筛选中采用这些模型可以加速HD和其他神经退行性疾病的临床试验入组。© 2024作者。《运动障碍》由Wiley Periodicals LLC代表国际帕金森和运动障碍协会出版。
