Department of Neurology, University of Ulm, Ulm, Germany.
Center for Molecular Biology, Heidelberg University, Heidelberg, Germany.
Transl Neurodegener. 2021 Aug 26;10(1):31. doi: 10.1186/s40035-021-00257-y.
Interventional trials in amyotrophic lateral sclerosis (ALS) suffer from the heterogeneity of the disease as it considerably reduces statistical power. We asked if blood neurofilament light chains (NfL) could be used to anticipate disease progression and increase trial power.
In 125 patients with ALS from three independent prospective studies-one observational study and two interventional trials-we developed and externally validated a multivariate linear model for predicting disease progression, measured by the monthly decrease of the ALS Functional Rating Scale Revised (ALSFRS-R) score. We trained the prediction model in the observational study and tested the predictive value of the following parameters assessed at diagnosis: NfL levels, sex, age, site of onset, body mass index, disease duration, ALSFRS-R score, and monthly ALSFRS-R score decrease since disease onset. We then applied the resulting model in the other two study cohorts to assess the actual utility for interventional trials. We analyzed the impact on trial power in mixed-effects models and compared the performance of the NfL model with two currently used predictive approaches, which anticipate disease progression using the ALSFRS-R decrease during a three-month observational period (lead-in) or since disease onset (ΔFRS).
Among the parameters provided, the NfL levels (P < 0.001) and the interaction with site of onset (P < 0.01) contributed significantly to the prediction, forming a robust NfL prediction model (R = 0.67). Model application in the trial cohorts confirmed its applicability and revealed superiority over lead-in and ΔFRS-based approaches. The NfL model improved statistical power by 61% and 22% (95% confidence intervals: 54%-66%, 7%-29%).
The use of the NfL-based prediction model to compensate for clinical heterogeneity in ALS could significantly increase the trial power. NCT00868166, registered March 23, 2009; NCT02306590, registered December 2, 2014.
肌萎缩侧索硬化症（ALS）的介入性试验受到疾病异质性的困扰，因为这会大大降低统计效力。我们想知道血液神经丝轻链（NfL）是否可以用于预测疾病进展并增加试验效力。
我们在三个独立的前瞻性研究（一个观察性研究和两个介入性试验）中的 125 名 ALS 患者中，开发并外部验证了一个用于预测疾病进展的多变量线性模型，该模型以 ALS 功能评定量表修订版（ALSFRS-R）评分的每月下降来衡量。我们在观察性研究中训练了预测模型，并测试了以下在诊断时评估的参数的预测价值：NfL 水平、性别、年龄、发病部位、体重指数、疾病持续时间、ALSFRS-R 评分以及自疾病发病以来的每月 ALSFRS-R 评分下降。然后，我们将得出的模型应用于另外两个研究队列中，以评估其在干预性试验中的实际效用。我们在混合效应模型中分析了对试验效力的影响，并将 NfL 模型与两种当前使用的预测方法进行了比较，这两种方法使用三个月观察期（先导期）或自疾病发病以来（ΔFRS）的 ALSFRS-R 下降来预测疾病进展。
在所提供的参数中，NfL 水平（P<0.001）和与发病部位的相互作用（P<0.01）对预测有显著贡献，形成了一个稳健的 NfL 预测模型（R=0.67）。模型在试验队列中的应用证实了其适用性，并显示出优于先导期和基于ΔFRS 的方法的优越性。NfL 模型使统计效力提高了 61%和 22%（95%置信区间：54%-66%，7%-29%）。
使用基于 NfL 的预测模型来补偿 ALS 中的临床异质性可以显著提高试验效力。NCT00868166，2009 年 3 月 23 日注册；NCT02306590，2014 年 12 月 2 日注册。
