Abstract
BackgroundInterventional 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.MethodsIn 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).ResultsAmong 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%).ConclusionThe use of the NfL-based prediction model to compensate for clinical heterogeneity in ALS could significantly increase the trial power.NCT00868166, registered March23, 2009; NCT02306590, registered December 2, 2014.
Highlights
The Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R) score has become the predominantly used primary outcome parameter in amyotrophic lateral sclerosis (ALS) trials [1, 2]
We investigated the predictive value of blood neurofilament light chains (NfL) levels at the time of diagnosis and the clinical parameters sex, age, site of onset, body mass index, disease duration, monthly ALSFRS-R decrease since disease onset (ΔFRS), and ALSFRS-R score at diagnosis as independent variables
The splitting allowed us to compare the ALSFRS-R slopes during the interventional period with ALSFRS-R slopes predicted for each patient in three different ways: (1) using our prediction model with NfL levels and clinical parameters assessed at study baseline, (2) using Monthly ALSFRS-R decrease since disease onset (ΔFRS), and (3) using the ALSFRS-R slope during the lead-in period
Summary
The Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R) score has become the predominantly used primary outcome parameter in ALS trials [1, 2]. The ALSFRS-R assesses the functional capability of ALS patients in daily life, and the score points lost per month are an established parameter for disease progression rate [3]. The disease heterogeneity is a major challenge in the design of ALSFRS-R-based trials [8,9,10]. Due to the low prevalence of ALS, the heterogeneity cannot be compensated by increasing the number of trial participants [16]. 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 antici‐ pate disease progression and increase trial power
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