Predicting functional decline and survival in amyotrophic lateral sclerosis.
Predicting functional decline and survival in amyotrophic lateral sclerosis.
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DOI：
10.1371/journal.pone.0174925
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Holbrook JD
中科院分区：
文献类型：
--
作者：
Ong ML;Tan PF;Holbrook JD
肌萎缩侧索硬化症病程的更好预测因子可以使更小和更有针对性的临床试验成为可能。部分为了实现这一目标，生命奖基金会收集了参与临床试验药物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至指数模型。根据肌萎缩侧索硬化功能评定量表修订版（ALSFRS-R）（快/慢进展）和生存率（高/低死亡风险）的总评分下降进行二元分类。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下降以及推导出的快/慢进展和高/低死亡风险类别。预测模型的性能通过使用接收者操作曲线和均方根误差在测试集中进行交叉验证来评估。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相当的准确度（AUC = 0.82）预测功能下降类别（快速或缓慢）。然而，通过基线受试者特征建立衰退分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分类。使用少量变量的组合，可以预测PRO-ACT中可用的1-2年时间范围内的功能下降和生存类别。这些发现可能对未来ALS临床试验的设计具有实用性。
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.