Ong Mei-Lyn, Tan Pei Fang, Holbrook Joanna D
Singapore Institute for Clinical Sciences (SICS), Agency of Science and Technology Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, Singapore, Singapore.
NIHR Biomedical Research Centre, University of Southampton, Southampton General Hospital, Tremona Road, Southampton, United Kingdom.
PLoS One. 2017 Apr 13;12(4):e0174925. doi: 10.1371/journal.pone.0174925. eCollection 2017.
Better predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database.
In this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors.
A model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class.
Using combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.
更好地预测肌萎缩侧索硬化症的病程可以使临床试验规模更小且更具针对性。部分为了实现这一目标，生命奖基金会收集了参与研究性药物临床试验的肌萎缩侧索硬化症患者的去识别记录，并将其提供给PRO-ACT数据库中的研究人员。
在本研究中，将PRO-ACT受试者的时间序列数据拟合到指数模型。得出肌萎缩侧索硬化症功能评定量表修订版（ALSFRS-R）总分下降（快速/缓慢进展）和生存（高/低死亡风险）的二元分类。通过交叉验证将数据分为训练集和测试集。将学习算法应用于训练集中的人口统计学、临床和实验室参数，以预测ALSFRS-R下降以及得出的快速/缓慢进展和高/低死亡风险类别。使用受试者操作特征曲线和均方根误差在测试集中通过交叉验证评估预测模型的性能。
使用包含基线后四个参数（体重、碱性磷酸酶、白蛋白和肌酸激酶）下降的增强算法创建的模型，能够以合理的准确性预测功能下降类别（快速或缓慢）（AUC = 0.82）。然而，通过基线受试者特征构建下降类预测模型的类似方法并不成功。相比之下，总胆红素、γ-谷氨酰转移酶、尿比重和ALSFRS-R项目评分——爬楼梯的基线值足以预测生存类别。
使用少量变量的组合可以在PRO-ACT提供的1 - 2年时间范围内预测功能下降和生存类别。这些发现可能对未来肌萎缩侧索硬化症临床试验的设计有用。
