Amyotrophic Lateral Sclerosis Survival Score (ALS-SS): A simple scoring system for early prediction of patient survival

Accurate personalized survival prediction for amyotrophic lateral sclerosis patients

Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease. Accurately predicting the survival time for ALS patients can help patients and clinicians to plan for future treatment and care. We describe the application of a machine-learned tool that incorporates clinical features and cortical thickness from brain magnetic resonance (MR) images to estimate the time until a composite respiratory failure event for ALS patients, and presents the prediction as individual survival distributions (ISDs). These ISDs provide the probability of survival (none of the respiratory failures) at multiple future time points, for each individual patient. Our learner considers several survival prediction models, and selects the best model to provide predictions. We evaluate our learned model using the mean absolute error margin (MAE-margin), a modified version of mean absolute error that handles data with censored outcomes. We show that our tool can provide helpful information for patients and clinicians in planning future treatment.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous.

OBJECTIVE

This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS.

METHODS

We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics.

RESULTS

Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only.

CONCLUSION

This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting motor neurons, with broad heterogeneity in disease progression and survival in different patients. Therefore, an accurate prediction model will be crucial to implement timely interventions and prolong patient survival time.

METHODS

A total of 1260 ALS patients from the PRO-ACT database were included in the analysis. Their demographics, clinical variables, and death reports were included. We constructed an ALS dynamic Cox model through the landmarking approach. The predictive performance of the model at different landmark time points was evaluated by calculating the area under the curve (AUC) and Brier score.

RESULTS

Three baseline covariates and seven time-dependent covariates were selected to construct the ALS dynamic Cox model. For better prognostic analysis, this model identified dynamic effects of treatment, albumin, creatinine, calcium, hematocrit, and hemoglobin. Its prediction performance (at all landmark time points, AUC ≥ 0.70 and Brier score ≤ 0.12) was better than that of the traditional Cox model, and it predicted the dynamic 6-month survival probability according to the longitudinal information of individual patients.

INTERPRETATION

We developed an ALS dynamic Cox model with ALS longitudinal clinical trial datasets as the inputs. This model can not only capture the dynamic prognostic effect of both baseline and longitudinal covariates but also make individual survival predictions in real time, which are valuable for improving the prognosis of ALS patients and providing a reference for clinicians to make clinical decisions.

Role of brain 2-[18F]fluoro-2-deoxy-D-glucose-positron-emission tomography as survival predictor in amyotrophic lateral sclerosis

PURPOSE

The identification of prognostic tools in amyotrophic lateral sclerosis (ALS) would improve the design of clinical trials, the management of patients, and life planning. We aimed to evaluate the accuracy of brain 2-[¹⁸F]fluoro-2-deoxy-D-glucose-positron-emission tomography (2-[¹⁸F]FDG-PET) as an independent predictor of survival in ALS.

METHODS

A prospective cohort study enrolled 418 ALS patients, who underwent brain 2-[¹⁸F]FDG-PET at diagnosis and whose survival time was available. We discretized the survival time in a finite number of classes in a data-driven fashion by employing a k-means-like strategy. We identified "hot brain regions" with maximal power in discriminating survival classes, by evaluating the Laplacian scores in a class-aware fashion. We retained the top-m features for each class to train the classification systems (i.e., a support vector machine, SVM), using 10% of the ALS cohort as test set.

RESULTS

Data were discretized in three survival profiles: 0-2 years, 2-5 years, and > 5 years. SVM resulted in an error rate < 20% for two out of three classes separately. As for class one, the discriminant clusters included left caudate body and anterior cingulate cortex. The most discriminant regions were bilateral cerebellar pyramid in class two, and right cerebellar dentate nucleus, and left cerebellar nodule in class three.

CONCLUSION

Brain 2-[¹⁸F]FDG-PET along with artificial intelligence was able to predict with high accuracy the survival time range in our ALS cohort. Healthcare professionals can benefit from this prognostic tool for planning patients' management and follow-up. 2-[¹⁸F]FDG-PET represents a promising biomarker for individual patients' stratification in clinical trials. The lack of a multicentre external validation of the model warrants further studies to evaluate its generalization capability.

The hypometabolic state: a good predictor of a better prognosis in amyotrophic lateral sclerosis

BACKGROUND

Malnutrition and weight loss are negative prognostic factors for survival in patients with amyotrophic lateral sclerosis (ALS). However, energy expenditure at rest (REE) is still not included in clinical practice, and no data are available concerning hypometabolic state in ALS.

OBJECTIVE

To evaluate in a referral cohort of patients with ALS the prevalence of hypometabolic state as compared with normometabolic and hypermetabolic states, and to correlate it with clinical phenotype, rate of progression and survival.

DESIGN

We conducted a retrospective study examining REE measured by indirect calorimetry in patients with ALS referred to Milan, Limoges and Tours referral centres between January 2011 and December 2017. Hypometabolism and hypermetabolism states were defined when REE difference between measured and predictive values was ≤-10% and ≥10%, respectively. We evaluated the relationship between these metabolic alterations and measures of body composition, clinical characteristics and survival.

RESULTS

Eight hundred forty-seven patients with ALS were recruited. The median age at onset was 63.79 years (IQR 55.00-71.17). The male/female ratio was 1.26 (M/F: 472/375). Ten per cent of patients with ALS were hypometabolic whereas 40% were hypermetabolic. Hypometabolism was significantly associated with later need for gastrostomy, non-invasive ventilation and tracheostomy placement. Furthermore, hypometabolic patients with ALS significantly outlived normometabolic (HR=1.901 (95% CI 1.080 to 3.345), p=0.0259) and hypermetabolic (HR=2.138 (95% CI 1.154 to 3.958), p=0.0157) patients.

CONCLUSION

Hypometabolism in ALS is not uncommon and is associated with slower disease progression and better survival than normometabolic and hypermetabolic subjects. Indirect calorimetry should be performed at least at time of diagnosis because alterations in metabolism are correlated with prognosis.

Mapping of critical events in disease progression through binary classification: Application to amyotrophic lateral sclerosis

BACKGROUND

The progression of many degenerative diseases is tracked periodically using scales evaluating functionality in daily activities. Although estimating the timing of critical events (i.e., disease tollgates) during degenerative disease progression is desirable, the necessary data may not be readily available in scale records. Further, analysis of disease progression poses data challenges, such as censoring and misclassification errors, which need to be addressed to provide meaningful research findings and inform patients.

METHODS

We developed a novel binary classification approach to map scale scores into disease tollgates to describe disease progression leveraging standard/modified Kaplan-Meier analyses. The approach is demonstrated by estimating progression pathways in amyotrophic lateral sclerosis (ALS). Tollgate-based ALS Staging System (TASS) specifies the critical events (i.e., tollgates) in ALS progression. We first developed a binary classification predicting whether each TASS tollgate was passed given the itemized ALSFRS-R scores using 514 ALS patients' data from Mayo Clinic-Rochester. Then, we utilized the binary classification to translate/map the ALSFRS-R data of 3,264 patients from the PRO-ACT database into TASS. We derived the time trajectories of ALS progression through tollgates from the augmented PRO-ACT data using Kaplan-Meier analyses. The effects of misclassification errors, condition-dependent dropouts, and censored data in trajectory estimations were evaluated with Interval Censored Kaplan Meier Analysis and Multistate Model for Panel Data.

RESULTS

The approach using Mayo Clinic data accurately estimated tollgate-passed states of patients given their itemized ALSFRS-R scores (AUCs > 0.90). The tollgate time trajectories derived from the augmented PRO-ACT dataset provide valuable insights; we predicted that the majority of the ALS patients would have modified arm function (67%) and require assistive devices for walking (53%) by the second year after ALS onset. By the third year, most (74%) ALS patients would occasionally use a wheelchair, while 48% of the ALS patients would be wheelchair-dependent by the fourth year. Assistive speech devices and feeding tubes were needed in 49% and 30% of the patients by the third year after ALS onset, respectively. The onset body region alters some tollgate passage time estimations by 1-2 years.

CONCLUSIONS

The estimated tollgate-based time trajectories inform patients and clinicians about prospective assistive device needs and life changes. More research is needed to personalize these estimations according to prognostic factors. Further, the approach can be leveraged in the progression of other diseases.

Serum creatinine levels in patients with amyotrophic lateral sclerosis: a systematic review and meta-analysis

BACKGROUND

Serum creatinine (Cr) is a biosynthetic product of creatine phosphate metabolism in muscles and is closely related to total muscle mass, but it is not easily affected by diet. Several studies have tried to explore the role of serum Cr levels in amyotrophic lateral sclerosis (ALS), but the results were inconsistent. Therefore, our study aims to explore the differences of serum Cr levels between ALS patients and controls and whether serum Cr at baseline is an independent predictor of survival. Methods: We searched all the related studies that probed into the association between Serum Cr levels and ALS based on PubMed, EMBASE and Cochrane library from October 1952 to February 2019. The quality of the included studies was evaluated by using Newcastle-Ottawa Scale (NOS), and all the statistical analysis of this meta-analysis was performed by Stata version 12.0. Results: Eight studies with a total of 11377 ALS patients and 937 controls were included. Among them, five studies indicated that ALS patients had lower serum Cr levels (SMD = -0.78, 95%CI [-0.97, -0.60]) compared to controls, and three studies showed that higher serum Cr levels in ALS patients were related to lower overall mortality (HR 0.89, 95%CI [0.80, 0.99]). Conclusion: The levels of serum Cr in ALS patients are significantly lower than those in controls, and they are inversely related to overall mortality in ALS patients. Therefore, the serum Cr, an easily accessible serological factor, may serve as a prognostic biomarker.

Novel probes for label-free detection of neurodegenerative GGGGCC repeats associated with amyotrophic lateral sclerosis

DNA repeat expansion sequences cause a myriad of neurological diseases when they expand beyond a critical threshold. Previous electrochemical approaches focused on the detection of trinucleotide repeats (CAG, CGG, and GAA) and relied on labeling of the probe and/or target strands or enzyme-linked assays. However, detection of expanded GC-rich sequences is challenging because they are prone to forming secondary structures such as cruciforms and quadruplexes. Here, we present label-free detection of hexanucleotide GGGGCC repeat sequences, which cause the leading genetic form of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). The approach relies on capturing targets by surface-bound oligonucleotide probes with a different number of complementary repeats, which proportionately translates the length of the target strands into charge transfer resistance (RCT) signal measured by electrochemical impedance spectroscopy. The probe carrying three tandem repeats transduces the number of repeats into RCT with a 3× higher calibration sensitivity and detection limit. Chronocoulometric measurements show a decrease in surface density with increasing repeat length, which is opposite of the impedance trend. This implies that the length of the target itself can contribute to amplification of the impedance signal independent of the surface density. Moreover, the probe can distinguish between a control and patient sequences while remaining insensitive to non-specific Huntington's disease (CAG) repeats in the presence of a complementary target. This label-free strategy might be applied to detect the length of other neurodegenerative repeat sequences using short probes with a few complementary repeats. Graphical abstract Short oligomeric probes with multiple complementary repeats detect long neurodegenerative targets with high sensitivity and transduce into higher impedance signal.

Plasma creatinine and amyotrophic lateral sclerosis prognosis: a systematic review and meta-analysis

Background: Plasma creatinine has been described as a prognostic biomarker for Amyotrophic Lateral Sclerosis (ALS), but with conflicting results in the literature. We performed a systematic review followed by a meta-analysis to address this question. Methods: We performed a systematic review of Pubmed, Embase and Cochrane databases and retrieved 14 distinct cohorts (19 studies) reporting results regarding the relationship between plasma creatinine and a clinical marker for ALS progression, notably ALSFRS (ALS Functional Rating Scale) and survival. Results: For baseline plasma creatinine, mortality risk was 28% lower when creatinine was higher than 88.4 µmol/L (hazard ratio (HR): 0.72; 95% confidence interval (CI): 0.58 to 0.88; p = 0.0003) and was 25% lower if creatinine was above versus below the median (HR: 0.75; 95% CI: 0.63 to 0.89; p = 0.0008). We found a significant positive correlation between plasma creatinine at baseline and functional score, and between creatinine decline and functional score decline (p < 0.0001 for both); but a negative correlation between plasma creatinine and functional score decline (p = 0.033). The overall quality of the studies was low mainly due to potential attrition bias, and several studies did not report analyzable results raising concern regarding a potential reporting bias. Conclusions: Plasma creatinine seems to be a promising prognostic biomarker for ALS. However, new studies with sound methodology and standardized criteria for the evaluation of ALS progression should be conducted to validate plasma creatinine as a clinical biomarker for ALS prognosis.

Biomarkers in Motor Neuron Disease: A State of the Art Review

Motor neuron disease can be viewed as an umbrella term describing a heterogeneous group of conditions, all of which are relentlessly progressive and ultimately fatal. The average life expectancy is 2 years, but with a broad range of months to decades. Biomarker research deepens disease understanding through exploration of pathophysiological mechanisms which, in turn, highlights targets for novel therapies. It also allows differentiation of the disease population into sub-groups, which serves two general purposes: (a) provides clinicians with information to better guide their patients in terms of disease progression, and (b) guides clinical trial design so that an intervention may be shown to be effective if population variation is controlled for. Biomarkers also have the potential to provide monitoring during clinical trials to ensure target engagement. This review highlights biomarkers that have emerged from the fields of systemic measurements including biochemistry (blood, cerebrospinal fluid, and urine analysis); imaging and electrophysiology, and gives examples of how a combinatorial approach may yield the best results. We emphasize the importance of systematic sample collection and analysis, and the need to correlate biomarker findings with detailed phenotype and genotype data.

Body mass index and survival from amyotrophic lateral sclerosis: A meta-analysis

Background

Several studies have examined the relationship between body mass index (BMI) and survival from amyotrophic lateral sclerosis (ALS). Many indicate that low BMI at diagnosis or during follow-up may be associated with accelerated progression and shortened survival. This study systematically evaluated the relationship between BMI and survival in patients with ALS.

Methods

The PubMed database was searched to identify all available studies reporting time-to-event data. Eight studies with 6,098 patients fulfilled the eligibility criteria. BMI was considered a continuous and ordered variable. Interstudy heterogeneity was assessed by the Cochran Q test and quantified by the I² metric. Fixed- or random-effects odds ratios summarized pooled effects after taking interstudy variability into account. Significance was set at p < 0.05.

Results

The ALS survival hazard ratio (HR) decreased approximately by 3% (95% confidence interval [CI]: 2%-5%) for each additional BMI unit when BMI was considered a continuous variable. When BMI was considered a categorical variable, the HRs for "normal" BMI vs "overweight" BMI and "obese" BMI were estimated to be as high as 0.91 (95% CI: 0.79-1.04) and 0.78 (95% CI: 0.60-1.01), respectively. The HR for the comparison of the "normal" BMI vs "underweight" BMI was estimated to be as high as 1.94 (95% CI: 1.42-2.65).

Conclusions

BMI is significantly and inversely associated with ALS survival.

Phosphorylation of Threonine 175 Tau in the Induction of Tau Pathology in Amyotrophic Lateral Sclerosis-Frontotemporal Spectrum Disorder (ALS-FTSD). A Review

Approximately 50–60% of all patients with amyotrophic lateral sclerosis (ALS) will develop a deficit of frontotemporal function, ranging from frontotemporal dementia (FTD) to one or more deficits of neuropsychological, speech or language function which are collectively known as the frontotemporal spectrum disorders of ALS (ALS-FTSD). While the neuropathology underlying these disorders is most consistent with a widespread alteration in the metabolism of transactive response DNA-binding protein 43 (TDP-43), in both ALS with cognitive impairment (ALSci) and ALS with FTD (ALS-FTD; also known as MND-FTD) there is evidence for alterations in the metabolism of the microtubule associated protein tau. This alteration in tau metabolism is characterized by pathological phosphorylation at residue Thr¹⁷⁵ (pThr¹⁷⁵ tau) which in vitro is associated with activation of GSK3β (pTyr²¹⁶GSK3β), phosphorylation of Thr²³¹tau, and the formation of cytoplasmic inclusions with increased rates of cell death. This putative pathway of pThr¹⁷⁵ induction of pThr²³¹ and the formation of pathogenic tau inclusions has been recently shown to span a broad range of tauopathies, including chronic traumatic encephalopathy (CTE) and CTE in association with ALS (CTE-ALS). This pathway can be experimentally triggered through a moderate traumatic brain injury, suggesting that it is a primary neuropathological event and not secondary to a more widespread neuronal dysfunction. In this review, we discuss the neuropathological underpinnings of the postulate that ALS is associated with a tauopathy which manifests as a FTSD, and examine possible mechanisms by which phosphorylation at Thr¹⁷⁵tau is induced. We hypothesize that this might lead to an unfolding of the hairpin structure of tau, activation of GSK3β and pathological tau fibril formation through the induction of cis-Thr²³¹ tau conformers. A potential role of TDP-43 acting synergistically with pathological tau metabolism is proposed.

Monitoring disease progression with plasma creatinine in amyotrophic lateral sclerosis clinical trials

OBJECTIVES

Plasma creatinine is a predictor of survival in amyotrophic lateral sclerosis (ALS). It remains, however, to be established whether it can monitor disease progression and serve as surrogate endpoint in clinical trials.

METHODS

We used clinical trial data from three cohorts of clinical trial participants in the LITRA, EMPOWER and PROACT studies. Longitudinal associations between functional decline, muscle strength and survival with plasma creatinine were assessed. Results were translated to trial design in terms of sample size and power.

RESULTS

A total of 13 564 measurements were obtained for 1241 patients. The variability between patients in rate of decline was lower in plasma creatinine than in ALS functional rating scale-Revised (ALSFRS-R; p<0.001). The average rate of decline was faster in the ALSFRS-R, with less between-patient variability at baseline (p<0.001). Plasma creatinine had strong longitudinal correlations with the ALSFRS-R (0.43 (0.39-0.46), p<0.001), muscle strength (0.55 (0.51-0.58), p<0.001) and overall mortality (HR 0.88 (0.86-0.91, p<0.001)). Using plasma creatinine as outcome could reduce the sample size in trials by 21.5% at 18 months. For trials up to 10 months, the ALSFRS-R required a lower sample size.

CONCLUSIONS

Plasma creatinine is an inexpensive and easily accessible biomarker that exhibits less variability between patients with ALS over time and is predictive for the patient's functional status, muscle strength and mortality risk. Plasma creatinine may, therefore, increase the power to detect treatment effects and could be incorporated in future ALS clinical trials as potential surrogate outcome.

Serum C-Reactive Protein as a Prognostic Biomarker in Amyotrophic Lateral Sclerosis

Importance

Various factors have been proposed as possible candidates associated with the prognosis of amyotrophic lateral sclerosis (ALS); however, there is still no consensus on which biomarkers are reliable prognostic factors. C-reactive protein (CRP) is a biomarker of the inflammatory response that shows significant prognostic value for several diseases.

Objective

To examine the prognostic significance of CRP in ALS.

Design, Setting, and Participants

Patients' serum CRP levels were evaluated from January 1, 2009, to June 30, 2015, in a large cohort of patients with ALS observed by an Italian tertiary multidisciplinary center. Results were replicated in an independent cohort obtained from a population-based registry of patients with ALS. A post hoc analysis was performed of the phase 2 trial of NP001 to determine whether stratification by levels of CRP improves differentiation of responders and nonresponders to the drug.

Main Outcomes and Measures

Serum CRP levels from the first examination were recorded to assess their effect on disease progression and survival.

Results

A total of 394 patients with ALS (168 women and 226 men; mean [SD] age at diagnosis, 60.18 [13.60] years) were observed in a tertiary multidisciplinary center, and the analysis was replicated in an independent cohort of 116 patients with ALS (50 women and 66 men; mean [SD] age at diagnosis, 67.00 [10.74] years) identified through a regional population-based registry. Serum CRP levels in the 394 patients with ALS correlated with severity of functional impairment, as measured by total score on the ALS Functional Rating Scale-Revised, at first evaluation (r = -0.14818; P = .004), and with patient survival (hazard ratio, 1.129; 95% CI, 1.033-1.234; P = .007). Similar results were found in the independent cohort (hazard ratio, 1.044; 95% CI, 1.016-1.056; P ≤ .001). Moreover, a post hoc analysis of the phase 2 trial of NP001 using the same CRP threshold showed that patients with elevated baseline CRP levels receiving the higher dose of NP001 had significantly less functional impairment after the treatment period compared with patients with normal baseline CRP, regardless of whether patients with normal CRP levels received NP001 or placebo (3.00 [3.62] vs -7.31 [6.23]; P = .04).

Conclusions and Relevance

These findings suggest that patients with ALS and elevated serum CRP levels progress more rapidly than do those with lower CRP levels and that this elevation may reflect a neuroinflammatory state potentially responsive to the immune regulators such as NP001.

Predicting functional decline and survival in amyotrophic lateral sclerosis

BACKGROUND

Better predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database.

METHODS

In this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors.

RESULTS

A model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class.

CONCLUSIONS

Using combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.

Open Access Platforms in Spinal Cord Injury: Existing Clinical Trial Data to Predict and Improve Outcomes

Recovery from acute spinal cord injury (SCI) is characterized by extensive heterogeneity, resulting in uncertain prognosis. Reliable prediction of recovery in the acute phase benefits patients and their families directly, as well as improves the likelihood of detecting efficacy in clinical trials. This issue of heterogeneity is not unique to SCI. In fields such as traumatic brain injury, Parkinson's disease, and amyotrophic lateral sclerosis, one approach to understand variability in recovery has been to make clinical trial data widely available to the greater research community. We contend that the SCI community should adopt a similar approach in providing open access clinical trial data.

Assessment of Motor Units in Neuromuscular Disease

The motor unit comprises the anterior horn cell, its axon, and the muscle fibers that it innervates. Although the true number of motor units is unknown, the number of motor units appears to vary greatly between different muscles and between different individuals. Assessment of the number and function of motor units is needed in diseases of the anterior horn cell and other motor nerve disorders. Amyotrophic lateral sclerosis is the most important disease of anterior horn cells. The need for an effective biomarker for assessing disease progression and for use in clinical trials in amyotrophic lateral sclerosis has stimulated the study of methods to measure the number of motor units. Since 1970 a number of different methods, including the incremental, F-wave, multipoint, and statistical methods, have been developed but none has achieved widespread applicability. Two methods (MUNIX and the multipoint incremental method) are in current use across multiple centres and are discussed in detail in this review, together with other recently published methods. Imaging with magnetic resonance and ultrasound is increasingly being applied to this area. Motor unit number estimates have also been applied to other neuromuscular diseases such as spinal muscular atrophy, compression neuropathies, and prior poliomyelitis. The need for an objective measure for the assessment of motor units remains tantalizingly close but unfulfilled in 2016.