Prognostic value of clinical and electrodiagnostic parameters at time of diagnosis in patients with amyotrophic lateral sclerosis

Prognostic value of motor and extramotor involvement in ALS

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder resulting in upper and lower motor neuron loss. ALS often has a focal onset of weakness, which subsequently spreads to other body regions. Survival is limited to two to five years after disease onset, often due to respiratory failure. Cognitive impairment is present in approximately 30% to 50% of patients and in 10%-15% of patients, the clinical criteria of frontotemporal dementia (FTD) are met.

METHODS

In this retrospective single-center ALS cohort study, we examined the occurrence of cognitive and behavioral impairment in relation to motor impairment at disease presentation and studied its impact on survival.

RESULTS

The degree of lower motor neuron involvement was associated with a worse survival, but there was no effect for upper motor neuron involvement. Patients who were cognitively normal had a significantly better survival compared to patients with cognitive or behavioral impairment and to patients with comorbid FTD. There was no significant difference regarding survival between patients with FTD and patients with cognitive or behavioral impairment.

CONCLUSIONS

The extent of motor and extramotor involvement in patients with ALS at disease presentation holds complementary prognostic information.

The impact of upper motor neuron involvement on clinical features, disease progression and prognosis in amyotrophic lateral sclerosis

Objectives

In amyotrophic lateral sclerosis (ALS) both upper (UMNs) and lower motor neurons (LMNs) are involved in the process of neurodegeneration, accounting for the great disease heterogeneity. We evaluated the associations of the burden of UMN impairment, assessed through the Penn Upper Motor Neuron Score (PUMNS), with demographic and clinical features of ALS patients to define the independent role of UMN involvement in generating disease heterogeneity, predicting disease progression and prognosis.

Methods

We collected the following clinical parameters on a cohort of 875 ALS patients: age and site of onset, survival, MRC scale, lower motor neuron score (LMNS), PUMNS, ALSFRS-R, change in ALSFRS-R over time (DFS), MITOS and King’s staging systems (KSS). Transcranial magnetic stimulation was performed on a subgroup of patients and central motor conduction time (CMCT) and cortical silent period (CSP) were calculated.

Results

We observed that patients with an earlier age at onset and bulbar onset had higher PUMNS values. Higher values were also associated to lower ALSFRS-R and to higher DFS scores, as well as to higher MITOS and KSS, indicating that a greater UMN burden correlates with disease severity. Conversely, we did not appreciate any association between UMN involvement and survival or markers of LMN impairment. Moreover, PUMNS values showed a positive association with CMCT and a negative one with CSP values.

Interpretation

Our results suggest that the burden of UMN pathology, assessed through PUMNS, has an important independent role in defining clinical characteristics, functional disability, disease progression and prognosis in ALS patients. We also support the role of TMS in defining severity of UMN involvement.

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.

Genetic factors for survival in amyotrophic lateral sclerosis: an integrated approach combining a systematic review, pairwise and network meta-analysis

BACKGROUND

The time of survival in patients with amyotrophic lateral sclerosis (ALS) varies greatly, and the genetic factors that contribute to the survival of ALS are not well studied. There is a lack of a comprehensive study to elucidate the role of genetic factors in the survival of ALS.

METHODS

The published studies were systematically searched and obtained from PubMed, EMBASE, and the Cochrane Library without any language restrictions from inception to Oct 27, 2021. A network meta-analysis for ALS causative/risk genes and a systematic review and pairwise meta-analysis for other genetic modifiers were conducted. The PROSPERO registration number: CRD42022311646.

RESULTS

A total of 29,764 potentially relevant references were identified, and 71 papers were eligible for analysis based on pre-decided criteria, including 35 articles in network meta-analysis for 9 ALS causative/risk genes, 17 articles in pairwise meta-analysis for four genetic modifiers, and 19 articles described in the systematic review. Variants in three genes, including ATXN2 (HR: 3.6), C9orf72 (HR: 1.6), and FUS (HR:1.8), were associated with short survival of ALS, but such association was not identified in SOD1, TARDBP, TBK1, NEK1, UBQLN2, and CCNF. In addition, UNC13A rs12608932 CC genotype and ZNF521B rs2275294 C allele also caused a shorter survival of ALS; however, APOE ε4 allele and KIFAP3 rs1541160 did not be found to have any effect on the survival of ALS.

CONCLUSIONS

Our study summarized and contrasted evidence for prognostic genetic factors in ALS and would help to understand ALS pathogenesis and guide clinical trials and drug development.

Learning dynamic Bayesian networks from time-dependent and time-independent data: Unraveling disease progression in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease causing patients to quickly lose motor neurons. The disease is characterized by a fast functional impairment and ventilatory decline, leading most patients to die from respiratory failure. To estimate when patients should get ventilatory support, it is helpful to adequately profile the disease progression. For this purpose, we use dynamic Bayesian networks (DBNs), a machine learning model, that graphically represents the conditional dependencies among variables. However, the standard DBN framework only includes dynamic (time-dependent) variables, while most ALS datasets have dynamic and static (time-independent) observations. Therefore, we propose the sdtDBN framework, which learns optimal DBNs with static and dynamic variables. Besides learning DBNs from data, with polynomial-time complexity in the number of variables, the proposed framework enables the user to insert prior knowledge and to make inference in the learned DBNs. We use sdtDBNs to study the progression of 1214 patients from a Portuguese ALS dataset. First, we predict the values of every functional indicator in the patients' consultations, achieving results competitive with state-of-the-art studies. Then, we determine the influence of each variable in patients' decline before and after getting ventilatory support. This insightful information can lead clinicians to pay particular attention to specific variables when evaluating the patients, thus improving prognosis. The case study with ALS shows that sdtDBNs are a promising predictive and descriptive tool, which can also be applied to assess the progression of other diseases, given time-dependent and time-independent clinical observations.

Prognostic value of time to generalization in 71 Chinese patients with sporadic amyotrophic lateral sclerosis

Background:

It is important to determine prognostic factors for the outcome of amyotrophic lateral sclerosis (ALS) at an early stage. The time taken for symptoms to spread from spinal or bulbar regions to both (time to generalization; TTG) is considered a strong predictor of survival; however, this has rarely been studied in Asian populations. The aim of this retrospective study was to evaluate potential factors affecting prognosis in Chinese patients with sporadic ALS, with a focus on the association between TTG and overall survival.

Methods:

Seventy-one patients with sporadic ALS who were hospitalized at Chinese PLA General Hospital from 2009 to 2016 were followed up until December 2017. Survival analysis was performed using univariate Kaplan-Meier log-rank and multivariate Cox proportional hazards models. The clinical data of the patients were recorded and analyzed. Variables studied were age at symptom onset, sex, site of symptom onset, diagnostic latency, TTG, diagnostic category, ALS Functional Rating Scale-revised score, percent predicted forced vital capacity (FVC%), and disease progression rate (DPR) at diagnosis.

Results:

The mean age at onset was 54 (SD = 10.2) years, and the median survival time from symptom onset was 41 months (95% confidence interval: 34–47). By univariate analysis, factors independently affecting survival were age at symptom onset (Log rank = 15.652, P < 0.0001), TTG (Log rank = 14.728, P < 0.0001), diagnostic latency (Log rank = 11.997, P = 0.001), and DPR (Log rank = 6.50, P = 0.011). In the Cox multivariate model, TTG had the strongest impact on survival time (hazard ratio = 0.926, P = 0.01).

Conclusions:

TTG can be used as an effective indicator of prognosis in patients with sporadic ALS.

Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems. Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs. Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated. Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.

Serum neurofilament light chain levels as a marker of upper motor neuron degeneration in patients with Amyotrophic Lateral Sclerosis

AIMS

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron degeneration disease with a diagnostic delay of about 1 year after symptoms onset. In ALS, blood neurofilament light chain (NfL) levels are elevated, but it is not entirely clear what drives this increase and what the diagnostic performance of serum NfL is in terms of predictive values and likelihood ratios. The aims of this study were to further explore the prognostic and diagnostic performances of serum NfL to discriminate between patients with ALS and ALS mimics, and to investigate the relationship between serum NfL with motor neuron degeneration.

METHODS

The diagnostic performances of serum NfL were based on a cohort of 149 serum samples of patients with ALS, 19 serum samples of patients with a disease mimicking ALS and 82 serum samples of disease control patients. The serum NfL levels were correlated with the number of regions (thoracic, bulbar, upper limb and lower limb) displaying upper and/or lower motor neuron degeneration. The prognostic performances of serum NfL were investigated based on a Cox regression analysis.

RESULTS

The associated predictive values and likelihood ratio to discriminate patients with ALS and ALS mimics were established. Serum NfL was associated with motor neuron degeneration driven by upper motor neuron (UMN) degeneration and was independently associated with survival in patients with ALS.

CONCLUSIONS

Altogether, these findings suggest that elevated serum NfL levels in ALS are driven by UMN degeneration and the disease progression rate and are independently associated with survival at time of diagnosis.

Amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is characterized by the degeneration of both upper and lower motor neurons, which leads to muscle weakness and eventual paralysis. Until recently, ALS was classified primarily within the neuromuscular domain, although new imaging and neuropathological data have indicated the involvement of the non-motor neuraxis in disease pathology. In most patients, the mechanisms underlying the development of ALS are poorly understood, although a subset of patients have familial disease and harbour mutations in genes that have various roles in neuronal function. Two possible disease-modifying therapies that can slow disease progression are available for ALS, but patient management is largely mediated by symptomatic therapies, such as the use of muscle relaxants for spasticity and speech therapy for dysarthria.