ObjectiveIt is essential to develop predictive algorithms for Amyotrophic Lateral Sclerosis (ALS) disease progression to allow for efficient clinical trials and patient care. The best existing predictive models rely on several months of baseline data and have only been validated in clinical trial research datasets. We asked whether a model developed using clinical research patient data could be applied to the broader ALS population typically seen at a tertiary care ALS clinic.MethodsBased on the PRO‐ACT ALS database, we developed random forest (RF), pre‐slope, and generalized linear (GLM) models to test whether accurate, unbiased models could be created using only baseline data. Secondly, we tested whether a model could be validated with a clinical patient dataset to demonstrate broader applicability.ResultsWe found that a random forest model using only baseline data could accurately predict disease progression for a clinical trial research dataset as well as a population of patients being treated at a tertiary care clinic. The RF Model outperformed a pre‐slope model and was similar to a GLM model in terms of root mean square deviation at early time points. At later time points, the RF Model was far superior to either model. Finally, we found that only the RF Model was unbiased and was less subject to overfitting than either of the other two models when applied to a clinic population.InterpretationWe conclude that the RF Model delivers superior predictions of ALS disease progression.

SummaryBackgroundLithium has neuroprotective effects in cell and animal models of amyotrophic lateral sclerosis (ALS), and a small pilot study in patients with ALS showed a significant effect of lithium on survival. We aimed to assess whether lithium improves survival in patients with ALS.MethodsThe lithium carbonate in amyotrophic lateral sclerosis (LiCALS) trial is a randomised, double-blind, placebo-controlled trial of oral lithium taken daily for 18 months in patients with ALS. Patients aged at least 18 years who had ALS according to the revised El Escorial criteria, had disease duration between 6 and 36 months, and were taking riluzole were recruited from ten centres in the UK. Patients were randomly assigned (1:1) to receive either lithium or matched placebo tablets. Randomisation was via an online system done at the level of the individual by block randomisation with randomly varying block sizes, stratified by study centre and site of disease onset (limb or bulbar). All patients and assessing study personnel were masked to treatment assignment. The primary endpoint was the rate of survival at 18 months and was analysed by intention to treat. This study is registered with Eudract, number 2008-006891-31.FindingsBetween May 26, 2009, and Nov 10, 2011, 243 patients were screened, 214 of whom were randomly assigned to receive lithium (107 patients) or placebo (107 patients). Two patients discontinued treatment and one died before the target therapeutic lithium concentration could be achieved. 63 (59%) of 107 patients in the placebo group and 54 (50%) of 107 patients in the lithium group were alive at 18 months. The survival functions did not differ significantly between groups (Mantel-Cox log-rank χ2 on 1 df=1·64; p=0·20). After adjusting for study centre and site of onset using logistic regression, the relative odds of survival at 18 months (lithium vs placebo) was 0·71 (95% CI 0·40–1·24). 56 patients in the placebo group and 61 in the lithium group had at least one serious adverse event.InterpretationWe found no evidence of benefit of lithium on survival in patients with ALS, but nor were there safety concerns, which had been identified in previous studies with less conventional designs. This finding emphasises the importance of pursuing adequately powered trials with clear endpoints when testing new treatments.FundingThe Motor Neurone Disease Association of Great Britain and Northern Ireland.

ObjectiveTo test the safety, tolerability, and urate‐elevating capability of the urate precursor inosine taken orally or by feeding tube in people with amyotrophic lateral sclerosis (ALS).MethodsThis was a pilot, open‐label trial in 25 participants with ALS. Treatment duration was 12 weeks. The dose of inosine was titrated at pre‐specified time points to elevate serum urate levels to 7–8 mg/dL. Primary outcomes were safety (as assessed by the occurrence of adverse events [AEs]) and tolerability (defined as the ability to complete the 12‐week study on study drug). Secondary outcomes included biomarkers of oxidative stress and damage. As an exploratory analysis, observed outcomes were compared with a virtual control arm built using prediction algorithms to estimate ALSFRS‐R scores.ResultsTwenty‐four out of 25 participants (96%) completed 12 weeks of study drug treatment. One participant was unable to comply with study visits and was lost to follow‐up. Serum urate rose to target levels in 6 weeks. No serious AEs attributed to study drug and no AEs of special concern, such as urolithiasis and gout, occurred. Selected biomarkers of oxidative stress and damage had significant changes during the study period. Observed changes in ALSFRS‐R did not differ from baseline predictions.InterpretationInosine appeared safe, well tolerated, and effective in raising serum urate levels in people with ALS. These findings, together with epidemiological observations and preclinical data supporting a neuroprotective role of urate in ALS models, provide the rationale for larger clinical trials testing inosine as a potential disease‐modifying therapy for ALS.

Advancing research and clinical care, and conducting successful and cost-effective clinical trials requires characterizing a given patient population. To gather a sufficiently large cohort of patients in rare diseases such as amyotrophic lateral sclerosis (ALS), we developed the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) platform. The PRO-ACT database currently consists of >8600 ALS patient records from 17 completed clinical trials, and more trials are being incorporated. The database was launched in an open-access mode in December 2012; since then, >400 researchers from >40 countries have requested the data. This review gives an overview on the research enabled by this resource, through several examples of research already carried out with the goal of improving patient care and understanding the disease. These examples include predicting ALS progression, the simulation of future ALS clinical trials, the verification of previously proposed predictive features, the discovery of novel predictors of ALS progression and survival, the newly identified stratification of patients based on their disease progression profiles, and the development of tools for better clinical trial recruitment and monitoring. Results from these approaches clearly demonstrate the value of large datasets for developing a better understanding of ALS natural history, prognostic factors, patient stratification, and more. The increasing use by the community suggests that further analyses of the PRO-ACT database will continue to reveal more information about this disease that has for so long defied our understanding.

IntroductionIn small trials, randomization can fail, leading to differences in patient characteristics across treatment arms, a risk that can be reduced by stratifying using key confounders. In ALS trials, riluzole use (RU) and bulbar onset (BO) have been used for stratification. We hypothesized that randomization could be improved by using a multifactorial prognostic score of predicted survival as a single stratifier.MethodsWe defined a randomization failure as a significant difference between treatment arms on a characteristic. We compared randomization failure rates when stratifying for RU and BO (“traditional stratification”) to failure rates when stratifying for predicted survival using a predictive algorithm. We simulated virtual trials using the PRO‐ACT database without application of a treatment effect to assess balance between cohorts. We performed 100 randomizations using each stratification method – traditional and algorithmic. We applied these stratification schemes to a randomization simulation with a treatment effect using survival as the endpoint and evaluated sample size and power.ResultsStratification by predicted survival met with fewer failures than traditional stratification. Stratifying predicted survival into tertiles performed best. Stratification by predicted survival was validated with an external dataset, the placebo arm from the BENEFIT‐ALS trial. Importantly, we demonstrated a substantial decrease in sample size required to reach statistical power.ConclusionsStratifying randomization based on predicted survival using a machine learning algorithm is more likely to maintain balance between trial arms than traditional stratification methods. The methodology described here can translate to smaller, more efficient clinical trials for numerous neurological diseases.

We developed ALS-VC, a GBM model trained with the PRO-ACT ALS dataset that provides vital capacity predictions generalizable to external datasets. The ALS-VC model could be helpful in advising and counseling patients, and, in clinical trials, it could be used to generate virtual control arms against which observed outcomes could be compared, or used to stratify patients into slowly, average, and rapidly progressing subgroups.

Background/Aims For single arm trials, a treatment is evaluated by comparing an outcome estimate to historically reported outcome estimates. Such a historically controlled trial is often analyzed as if the estimates from previous trials were known without variation and there is no trial-to-trial variation in their estimands. We develop a test of treatment efficacy and sample size calculation for historically controlled trials that considers these sources of variation. Methods We fit a Bayesian hierarchical model, providing a sample from the posterior predictive distribution of the outcome estimand of a new trial, which, along with the standard error of the estimate, can be used to calculate the probability that the estimate exceeds a threshold. We then calculate criteria for statistical significance as a function of the standard error of the new trial and calculate sample size as a function of difference to be detected. We apply these methods to clinical trials for amyotrophic lateral sclerosis using data from the placebo groups of 16 trials. Results We find that when attempting to detect the small to moderate effect sizes usually assumed in amyotrophic lateral sclerosis clinical trials, historically controlled trials would require a greater total number of patients than concurrently controlled trials, and only when an effect size is extraordinarily large is a historically controlled trial a reasonable alternative. We also show that utilizing patient level data for the prognostic covariates can reduce the sample size required for a historically controlled trial. Conclusion This article quantifies when historically controlled trials would not provide any sample size advantage, despite dispensing with a control group.

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