Pfohl Stephen R, Kim Renaid B, Coan Grant S, Mitchell Cassie S
Department of Biomedical Engineering, Georgia Institute of Technology, Emory University School of Medicine, Atlanta, GA, United States.
Department of Biomedical Informatics, Stanford University, Stanford, CA, United States.
Front Neuroinform. 2018 Jun 14;12:36. doi: 10.3389/fninf.2018.00036. eCollection 2018.
The heterogeneity of amyotrophic lateral sclerosis (ALS) survival duration, which varies from <1 year to >10 years, challenges clinical decisions and trials. Utilizing data from 801 deceased ALS patients, we: (1) assess the underlying complex relationships among common clinical ALS metrics; (2) identify which clinical ALS metrics are the "best" survival predictors and how their predictive ability changes as a function of disease progression. Analyses included examination of relationships within the raw data as well as the construction of interactive survival regression and classification models (generalized linear model and random forests model). Dimensionality reduction and feature clustering enabled decomposition of clinical variable contributions. Thirty-eight metrics were utilized, including Medical Research Council (MRC) muscle scores; respiratory function, including forced vital capacity (FVC) and FVC % predicted, oxygen saturation, negative inspiratory force (NIF); the Revised ALS Functional Rating Scale (ALSFRS-R) and its activities of daily living (ADL) and respiratory sub-scores; body weight; onset type, onset age, gender, and height. Prognostic random forest models confirm the dominance of patient age-related parameters decline in classifying survival at thresholds of 30, 60, 90, and 180 days and 1, 2, 3, 4, and 5 years. Collective prognostic insight derived from the overall investigation includes: multi-dimensionality of ALSFRS-R scores suggests cautious usage for survival forecasting; upper and lower extremities independently degenerate and are autonomous from respiratory decline, with the latter associating with nearer-to-death classifications; height and weight-based metrics are auxiliary predictors for farther-from-death classifications; sex and onset site (limb, bulbar) are not independent survival predictors due to age co-correlation. The dimensionality and fluctuating predictors of ALS survival must be considered when developing predictive models for clinical trial development or in-clinic usage. Additional independent metrics and possible revisions to current metrics, like the ALSFRS-R, are needed to capture the underlying complexity needed for population and personalized forecasting of survival.
肌萎缩侧索硬化症（ALS）患者的生存时长存在异质性，从不到1年到超过10年不等，这给临床决策和试验带来了挑战。利用801例已故ALS患者的数据，我们：（1）评估常见临床ALS指标之间潜在的复杂关系；（2）确定哪些临床ALS指标是“最佳”生存预测指标，以及它们的预测能力如何随疾病进展而变化。分析包括对原始数据内关系的检查，以及构建交互式生存回归和分类模型（广义线性模型和随机森林模型）。降维和特征聚类能够分解临床变量的贡献。使用了38项指标，包括医学研究委员会（MRC）肌肉评分；呼吸功能，包括用力肺活量（FVC）及其预测百分比、血氧饱和度、吸气负压（NIF）；修订版ALS功能评定量表（ALSFRS-R）及其日常生活活动（ADL）和呼吸子评分；体重；发病类型、发病年龄、性别和身高。预后随机森林模型证实，在30、60、90和180天以及1、2、3、4和5年的阈值下，与患者年龄相关的参数下降在生存分类中占主导地位。从整体研究中得出的总体预后见解包括：ALSFRS-R评分的多维度性表明在生存预测中应谨慎使用；上肢和下肢独立退化，且与呼吸功能下降无关，后者与更接近死亡的分类相关；基于身高和体重的指标是远离死亡分类的辅助预测指标；由于年龄的共同相关性，性别和发病部位（肢体、延髓）不是独立的生存预测指标。在开发用于临床试验或临床使用的预测模型时，必须考虑ALS生存的维度和波动的预测指标。需要额外的独立指标以及对当前指标（如ALSFRS-R）可能的修订，以捕捉人群和个性化生存预测所需的潜在复杂性。
