Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing, 100191, China.
Department of Neurology, Peking University Third Hospital, 49 Huayuan North Road, Haidian District, Beijing, 100191, China.
J Neurol. 2021 Sep;268(9):3361-3370. doi: 10.1007/s00415-021-10508-7. Epub 2021 Mar 10.
Increasing prognostic models for amyotrophic lateral sclerosis (ALS) have been developed. However, no comprehensive evaluation of these models has been done. The purpose of this study was to map the prognostic models for ALS to assess their potential contribution and suggest future improvements on modeling strategy.
Databases including Medline, Embase, Web of Science, and Cochrane library were searched from inception to 20 February 2021. All studies developing and/or validating prognostic models for ALS were selected. Information regarding modelling method and methodological quality was extracted.
A total of 28 studies describing the development of 34 models and the external validation of 19 models were included. The outcomes concerned were ALS progression (n = 12; 35%), change in weight (n = 1; 3%), respiratory insufficiency (n = 2; 6%), and survival (n = 19; 56%). Among the models predicting ALS progression or survival, the most frequently used predictors were age, ALS Functional Rating Scale/ALS Functional Rating Scale-Revised, site of onset, and disease duration. The modelling method adopted most was machine learning (n = 16; 47%). Most of the models (n = 25; 74%) were not presented. Discrimination and calibration were assessed in 12 (35%) and 2 (6%) models, respectively. Only one model by Westeneng et al. (Lancet Neurol 17:423-433, 2018) was assessed with overall low risk of bias and it performed well in both discrimination and calibration, suggesting a relatively reliable model for practice.
This study systematically reviewed the prognostic models for ALS. Their usefulness is questionable due to several methodological pitfalls and the lack of external validation done by fully independent researchers. Future research should pay more attention to the addition of novel promising predictors, external validation, and head-to-head comparisons of existing models.
已经开发出越来越多用于肌萎缩侧索硬化症 (ALS) 的预后模型。然而，尚未对这些模型进行全面评估。本研究的目的是绘制 ALS 的预后模型图，以评估其潜在贡献，并就建模策略提出未来的改进建议。
从建库至 2021 年 2 月 20 日，检索了 Medline、Embase、Web of Science 和 Cochrane 图书馆在内的数据库，以纳入所有开发和/或验证 ALS 预后模型的研究。提取了关于建模方法和方法学质量的信息。
共纳入了 28 项研究，描述了 34 项模型的开发和 19 项模型的外部验证。结局包括 ALS 进展（n=12；35%）、体重变化（n=1；3%）、呼吸功能不全（n=2；6%）和生存（n=19；56%）。在预测 ALS 进展或生存的模型中，最常使用的预测指标是年龄、肌萎缩侧索硬化功能评定量表/肌萎缩侧索硬化功能评定量表修订版、发病部位和疾病持续时间。采用最多的建模方法是机器学习（n=16；47%）。大多数模型（n=25；74%）未呈现。分别在 12 个（35%）和 2 个（6%）模型中评估了区分度和校准度。只有 Westeneng 等人的一项模型（Lancet Neurol 17:423-433, 2018）被评估为整体低偏倚风险，并且在区分度和校准度方面表现良好，表明这是一个用于实践的相对可靠的模型。
本研究系统地回顾了 ALS 的预后模型。由于存在一些方法学缺陷以及缺乏完全独立的研究人员进行外部验证，因此其有用性值得怀疑。未来的研究应更加关注增加新的有前途的预测指标、外部验证以及现有模型的直接比较。
