Applying the D50 disease progression model to gray and white matter pathology in amyotrophic lateral sclerosis

T1-weighted MRI texture analysis in amyotrophic lateral sclerosis patients stratified by the D50 progression model

Amyotrophic lateral sclerosis, a progressive neurodegenerative disease, presents challenges in predicting individual disease trajectories due to its heterogeneous nature. This study explores the application of texture analysis on T1-weighted MRI in patients with amyotrophic lateral sclerosis, stratified by the D50 disease progression model. The D50 model, which offers a more nuanced representation of disease progression than traditional linear metrics, calculates the sigmoidal curve of functional decline and provides independent quantifications of disease aggressiveness and accumulation. In this research, a representative cohort of 116 patients with amyotrophic lateral sclerosis was studied using the D50 model and texture analysis on MRI images. Texture analysis, a technique used for quantifying voxel intensity patterns in MRI images, was employed to discern alterations in brain tissue associated with amyotrophic lateral sclerosis. This study examined alterations of the texture feature autocorrelation across sub-groups of patients based on disease accumulation, aggressiveness and the first site of onset, as well as in direct regressions with accumulation/aggressiveness. The findings revealed distinct patterns of the texture-derived autocorrelation in grey and white matter, increase in bilateral corticospinal tract, right hippocampus and left temporal pole as well as widespread decrease within motor and extra-motor brain regions, of patients stratified based on their disease accumulation. Autocorrelation alterations in grey and white matter, in clusters within the left cingulate gyrus white matter, brainstem, left cerebellar tonsil grey matter and right inferior fronto-occipital fasciculus, were also negatively associated with disease accumulation in regression analysis. Otherwise, disease aggressiveness correlated with only two small clusters, within the right superior temporal gyrus and right posterior division of the cingulate gyrus white matter. The findings suggest that texture analysis could serve as a potential biomarker for disease stage in amyotrophic lateral sclerosis, with potential for quick assessment based on using T1-weighted images.

Cortical microstructural abnormalities in amyotrophic lateral sclerosis: a gray matter-based spatial statistics study

Background

Amyotrophic lateral sclerosis (ALS)-related white-matter microstructural abnormalities have received considerable attention; however, gray-matter structural abnormalities have not been fully elucidated. This study aimed to evaluate cortical microstructural abnormalities in ALS and determine their association with disease severity.

Methods

This study included 34 patients with ALS and 30 healthy controls. Diffusion-weighted data were used to estimate neurite orientation dispersion and density imaging (NODDI) parameters, including neurite density index (NDI) and orientation dispersion index (ODI). We performed gray matter-based spatial statistics (GBSS) in a voxel-wise manner to determine the cortical microstructure difference. We used the revised ALS Functional Rating Scale (ALSFRS-R) to assess disease severity and conducted a correlation analysis between NODDI parameters and ALSFRS-R.

Results

In patients with ALS, the NDI reduction involved several cortical regions [primarily the precentral gyrus, postcentral gyrus, temporal cortex, prefrontal cortex, occipital cortex, and posterior parietal cortex; family-wise error (FWE)-corrected P<0.05]. ODI decreased in relatively few cortical regions (including the precentral gyrus, postcentral gyrus, prefrontal cortex, and inferior parietal lobule; FWE-corrected P<0.05). The NDI value in the left precentral and postcentral gyrus was positively correlated with the ALS disease severity (FWE-corrected P<0.05).

Conclusions

The decreases in NDI and ODI involved both motor-related and extra-motor regions and indicated the presence of gray-matter microstructural impairment in ALS. NODDI parameters are potential imaging biomarkers for evaluating disease severity in vivo. Our results showed that GBSS is a feasible method for identifying abnormalities in the cortical microstructure of patients with ALS.

Novel approaches to assessing upper motor neuron dysfunction in motor neuron disease/amyotrophic lateral sclerosis: IFCN handbook chapter

Identifying upper motor neuron (UMN) dysfunction is fundamental to the diagnosis and understanding of disease pathogenesis in motor neuron disease (MND). The clinical assessment of UMN dysfunction may be difficult, particularly in the setting of severe muscle weakness. From a physiological perspective, transcranial magnetic stimulation (TMS) techniques provide objective biomarkers of UMN dysfunction in MND and may also be useful to interrogate cortical and network function. Single, paired- and triple pulse TMS techniques have yielded novel diagnostic and prognostic biomarkers in MND, and have provided important pathogenic insights, particularly pertaining to site of disease onset. Cortical hyperexcitability, as heralded by reduced short interval intracortical inhibition (SICI) and increased short interval intracortical facilitation, has been associated with the onset of lower motor neuron degeneration, along with patterns of disease spread, development of specific clinical features such as the split hand phenomenon, and may provide an indication about the rate of disease progression. Additionally, reduction of SICI has emerged as a potential diagnostic aid in MND. The triple stimulation technique (TST) was shown to enhance the diagnostic utility of conventional TMS measures in detecting UMN dysfunction in MND. Separately, sophisticated brain imaging techniques have uncovered novel biomarkers of neurodegeneration that have bene associated with progression. The present review will discuss the utility of TMS and brain neuroimaging derived biomarkers of UMN dysfunction in MND, focusing on recently developed TMS techniques and advanced neuroimaging modalities that interrogate structural and functional integrity of the corticomotoneuronal system, with an emphasis on pathogenic, diagnostic, and prognostic utility.

The ALSFRS-R Summit: a global call to action on the use of the ALSFRS-R in ALS clinical trials

The Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) was developed more than 25 years ago as an instrument to monitor functional change over time in patients with ALS. It has since been revised and extended to meet the needs of high data quality in ALS trials (ALSFRS-R), however a full re-validation of the scale was not completed. Despite this, the scale has remained a primary outcome measure in clinical trials. We convened a group of clinical trialists to discuss and explore opportunities to improve the scale and propose alternative measures. In this meeting report, we present a call to action on the use of the ALSFRS-Revised scale in clinical trials, focusing on the need for (1) harmonization of the ALSFRS-R administration globally, (2) alignment on a set of recommendations for clinical trial design and statistical analysis plans (SAPs), and (3) use of additional outcome measures.

Trajectories and contributing factors of neural compensation in healthy and pathological aging

Neural degeneration is a hallmark of healthy aging and can be associated with specific cognitive impairments. However, neural degeneration per se is not matched by unremitting declines in cognitive abilities. Instead, middle-aged and older adults typically maintain surprisingly high levels of cognitive functioning, suggesting that the human brain can adapt to structural degeneration by neural compensation. Here, we summarize prevailing theories and recent empirical studies on neural compensation with a focus on often neglected contributing factors, such as lifestyle, metabolism and neural plasticity. We suggest that these factors moderate the relationship between structural integrity and neural compensation, maintaining psychological well-being and behavioral functioning. Finally, we discuss that a breakdown in neural compensation may pose a tipping point that distinguishes the trajectories of healthy vs pathological aging, but conjoint support from psychology and cognitive neuroscience for this alluring view is still scarce. Therefore, future experiments that target the concomitant processes of neural compensation and associated behavior will foster a comprehensive understanding of both healthy and pathological aging.

Creatine Kinase MB Isoenzyme Is a Complementary Biomarker in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is an invariably fatal neurodegenerative disease with limited therapeutic options. There is an urgent need for novel biomarkers to be used as surrogates for new therapeutic trials and disease monitoring. In this study, we sought to systematically study creatine kinase isoenzyme MB (CK-MB) in a real-world cohort of ALS patients, assess the diagnostic performance, and evaluate its association with other laboratory and clinical parameters. We reviewed data from 194 consecutive patients that included 130 ALS patients and 64 disease control patients (primary lateral sclerosis [PLS], benign fasciculations syndrome [BFS], Huntington's disease [HD] and Alzheimer's disease [AD]). CK-MB was elevated in the sera of more than half of all patients with ALS. In patients with spinal-onset ALS, CK-MB levels were significantly higher than in patients with other neurodegenerative diseases. Patients with slower rates of functional decline had a significantly higher baseline CK-MB. Furthermore, CK-MB elevations correlated with cardiac troponin T (cTnT) and with revised ALS Functional Rating Scale (ALSFRS-R) bulbar subcategory. We posit that measuring CK-MB in ALS patients in a complimentary fashion could potentially aid in the diagnostic workup of ALS and help discriminate the disease from some ALS mimics and other neurodegenerative diseases. CK-MB levels also may provide valuable prognostic information regarding disease aggressiveness as well as correlations with specific phenotypic presentations.

Demographic changes in a large motor neuron disease cohort in Portugal: a 27 year experience

Objective: Motor Neuron Diseases (MND) have a large clinical spectrum, being the most common amyotrophic lateral sclerosis (ALS) but there is significant clinical heterogeneity. Our goal was to investigate this heterogeneity and any potential changes during a long period. Methods: We performed a retrospective cohort study among a large Portuguese cohort of MND patients (n = 1550) and investigated changing patterns in clinical and demographic characteristics over the 27-year period of our database. With that aim, patients were divided into three 9-year groups according to the date of their first visit to our unit: P1, 1994-2002; P2, 2003-2011; P3, 2012-2020. Results: The overall cohort's clinical and demographic characteristics are consistent with clinical experience, but our findings point to gradual changes over time. Time pattern analysis revealed statistically significant differences in the distribution of clinical phenotypes, the average age of onset, diagnostic delay, the proportin of patients using respiratory support with noninvasive ventilation (NIV), time to NIV, and survival. Across time, in the overall cohort, we found an increasing age at onset (p = 0.029), a decrease of two months in diagnostic delay (p < 0.001) and a higher relative frequency of progressive muscular atrophy patients. For ALS patients with spinal onset, from P1 to P2, there was a more widespread (54.8% vs 69.4%, p = 0.005) and earlier (36.9 vs 27.2 months, p = 0.05) use of NIV and a noteworthy 13-month increase in median survival (p = 0.041). Conclusions: Our results probably reflect better comprehensive care, and they are relevant for future studies exploring the impact of new treatments on ALS patients.

Amyotrophic lateral sclerosis regional progression intervals change according to time of involvement of different body regions

BACKGROUND AND PURPOSE

The prediction of disease course is one of the main targets of amyotrophic lateral sclerosis (ALS) research, particularly considering its wide phenotypic heterogeneity. Despite many attempts to classify patients into prognostic categories according to the different spreading patterns at diagnosis, a precise regional progression rate and the time of involvement of each region has yet to be clarified. The aim of our study was to evaluate the functional decline in different body regions according to their time of involvement during disease course.

METHODS

In a population-based dataset of ALS patients, we analysed the functional decline in different body regions according to time and order of regional involvement. We calculated the regional progression intervals (RPIs) between initial involvement and severe functional impairment using the ALS Functional Rating Scale revised (ALSFRS-r) subscores for the bulbar, upper limb, lower limb and respiratory/thoracic regions. Time-to-event analyses, adjusted for age, sex, ALSFRS-r pre-slope (ΔALSFRS-R), cognitive status, and mutational status were performed.

RESULTS

The duration of RPI differed significantly among ALS phenotypes, with the RPI of the first region involved being significantly longer than the RPIs of regions involved later. Cox proportional hazard models showed that in fact a longer time between disease onset and initial regional involvement was related to a reduced duration of the RPI duration in each different body region (bulbar region: hazard ratio [HR] 1.11, 95% confidence interval [CI] 1.06-1.16, p < 0.001; upper limb region: HR 1.16, 95% CI 1.06-1.28, p = 0.002; lower limb region: HR 1.11, 95% CI 1.03-1.19, p = 0.009; respiratory/thoracic region: HR 1.10, 95% CI 1.06-1.14, p = 0.005).

CONCLUSIONS

We found that the progression of functional decline accelerates in regions involved later during disease course. Our findings can be useful in patient management and prognosis prediction.

Motor unit number index (MUNIX) loss of 50% occurs in half the time of 50% functional loss according to the D50 disease progression model of ALS

Capturing disease progression in amyotrophic lateral sclerosis (ALS) is challenging and refinement of progression markers is urgently needed. This study introduces new motor unit number index (MUNIX), motor unit size index (MUSIX) and compound muscle action potential (CMAP) parameters called M50, MUSIX200 and CMAP50. M50 and CMAP50 indicate the time in months from symptom onset an ALS patient needs to lose 50% of MUNIX or CMAP in relation to the mean values of controls. MUSIX200 represents the time in months until doubling of the mean MUSIX of controls. We used MUNIX parameters of Musculi abductor pollicis brevis (APB), abductor digiti minimi (ADM) and tibialis anterior (TA) of 222 ALS patients. Embedded in the D50 disease progression model, disease aggressiveness and accumulation were analyzed separately. M50, CMAP50 and MUSIX200 significantly differed among disease aggressiveness subgroups (p < 0.001) regardless of disease accumulation. ALS patients with a low M50 had a significantly shorter survival compared to high M50 (median 32 versus 74 months). M50 preceded the loss of global function (median of about 14 months). M50, CMAP50 and MUSIX200 characterize the disease course in ALS in a new way and may be applied as early measures of disease progression.

Motor unit number index (MUNIX) in the D50 disease progression model reflects disease accumulation independently of disease aggressiveness in ALS

The neurophysiological technique motor unit number index (MUNIX) is increasingly used in clinical trials to measure loss of motor units. However, the heterogeneous disease course in amyotrophic lateral sclerosis (ALS) obfuscates robust correlations between clinical status and electrophysiological assessments. To address this heterogeneity, MUNIX was applied in the D50 disease progression model by analyzing disease aggressiveness (D50) and accumulation (rD50 phase) in ALS separately. 237 ALS patients, 45 controls and 22 ALS-Mimics received MUNIX of abductor pollicis brevis (APB), abductor digiti minimi (ADM) and tibialis anterior (TA) muscles. MUNIX significantly differed between controls and ALS patients and between ALS-Mimics and controls. Within the ALS cohort, significant differences between Phase I and II revealed in MUNIX, compound muscle action potential (CMAP) and motor unit size index (MUSIX) of APB as well as in MUNIX and CMAP of TA. For the ADM, significant differences occurred later in CMAP and MUNIX between Phase II and III/IV. In contrast, there was no significant association between disease aggressiveness and MUNIX. In application of the D50 disease progression model, MUNIX can demonstrate disease accumulation already in early Phase I and evaluate effects of therapeutic interventions in future therapeutic trials independent of individual disease aggressiveness.

Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data

The clinical presentation of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, varies widely across patients, making it challenging to determine if potential therapeutics slow progression. We sought to determine whether there were common patterns of disease progression that could aid in the design and analysis of clinical trials. We developed an approach based on a mixture of Gaussian processes to identify clusters of patients sharing similar disease progression patterns, modeling their average trajectories and the variability in each cluster. We show that ALS progression is frequently nonlinear, with periods of stable disease preceded or followed by rapid decline. We also show that our approach can be extended to Alzheimer's and Parkinson's diseases. Our results advance the characterization of disease progression of ALS and provide a flexible modeling approach that can be applied to other progressive diseases.

Cortical and subcortical grey matter atrophy in Amyotrophic Lateral Sclerosis correlates with measures of disease accumulation independent of disease aggressiveness

There is a growing demand for reliable biomarkers to monitor disease progression in Amyotrophic Lateral Sclerosis (ALS) that also take the heterogeneity of ALS into account. In this study, we explored the association between Magnetic Resonance Imaging (MRI)-derived measures of cortical thickness (CT) and subcortical grey matter (GM) volume with D50 model parameters. T1-weighted MRI images of 72 Healthy Controls (HC) and 100 patients with ALS were analyzed using Surface-based Morphometry for cortical structures and Voxel-based Morphometry for subcortical Region-Of-Interest analyses using the Computational Anatomy Toolbox (CAT12). In Inter-group contrasts, these parameters were compared between patients and HC. Further, the D50 model was used to conduct subgroup-analyses, dividing patients by a) Phase of disease covered at the time of MRI-scan and b) individual overall disease aggressiveness. Finally, correlations between GM and D50 model-derived parameters were examined. Inter-group analyses revealed ALS-related cortical thinning compared to HC located mainly in frontotemporal regions and a decrease in GM volume in the left hippocampus and amygdala. A comparison of patients in different phases showed further cortical and subcortical GM atrophy along with disease progression. Correspondingly, regression analyses identified negative correlations between cortical thickness and individual disease covered. However, there were no differences in CT and subcortical GM between patients with low and high disease aggressiveness. By application of the D50 model, we identified correlations between cortical and subcortical GM atrophy and ALS-related functional disability, but not with disease aggressiveness. This qualifies CT and subcortical GM volume as biomarkers representing individual disease covered to monitor therapeutic interventions in ALS.

Classification of amyotrophic lateral sclerosis by brain volume, connectivity, and network dynamics

Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional connectivity between brain regions by linear methods highlight the role of brain function. These studies have yet to be combined with brain structure and nonlinear functional features. We investigate the role of linear and nonlinear functional brain features, and the benefit of combining brain structure and function for ALS classification. ALS patients (N = 97) and healthy controls (N = 59) underwent structural and functional resting state magnetic resonance imaging. Based on key hubs of resting state networks, we defined three feature sets comprising brain volume, resting state functional connectivity (rsFC), as well as (nonlinear) resting state dynamics assessed via recurrent neural networks. Unimodal and multimodal random forest classifiers were built to classify ALS. Out-of-sample prediction errors were assessed via five-fold cross-validation. Unimodal classifiers achieved a classification accuracy of 56.35-61.66%. Multimodal classifiers outperformed unimodal classifiers achieving accuracies of 62.85-66.82%. Evaluating the ranking of individual features' importance scores across all classifiers revealed that rsFC features were most dominant in classification. While univariate analyses revealed reduced rsFC in ALS patients, functional features more generally indicated deficits in information integration across resting state brain networks in ALS. The present work undermines that combining brain structure and function provides an additional benefit to diagnostic classification, as indicated by multimodal classifiers, while emphasizing the importance of capturing both linear and nonlinear functional brain properties to identify discriminative biomarkers of ALS.

Feature selection from magnetic resonance imaging data in ALS: a systematic review

Background:

With the advances in neuroimaging in amyotrophic lateral sclerosis (ALS), it has been speculated that multiparametric magnetic resonance imaging (MRI) is capable to contribute to early diagnosis. Machine learning (ML) can be regarded as the missing piece that allows for the useful integration of multiparametric MRI data into a diagnostic classifier. The major challenges in developing ML classifiers for ALS are limited data quantity and a suboptimal sample to feature ratio which can be addressed by sound feature selection.

Methods:

We conducted a systematic review to collect MRI biomarkers that could be used as features by searching the online database PubMed for entries in the recent 4 years that contained cross-sectional neuroimaging data of subjects with ALS and an adequate control group. In addition to the qualitative synthesis, a semi-quantitative analysis was conducted for each MRI modality that indicated which brain regions were most commonly reported.

Results:

Our search resulted in 151 studies with a total of 221 datasets. In summary, our findings highly resembled generally accepted neuropathological patterns of ALS, with degeneration of the motor cortex and the corticospinal tract, but also in frontal, temporal, and subcortical structures, consistent with the neuropathological four-stage model of the propagation of pTDP-43 in ALS.

Conclusions:

These insights are discussed with respect to their potential for MRI feature selection for future ML-based neuroimaging classifiers in ALS. The integration of multiparametric MRI including DTI, volumetric, and texture data using ML may be the best approach to generate a diagnostic neuroimaging tool for ALS.

Monocyte-Derived Macrophages Contribute to Chitinase Dysregulation in Amyotrophic Lateral Sclerosis: A Pilot Study

Neuroinflammation significantly contributes to Amyotrophic Lateral Sclerosis (ALS) pathology. In lieu of this, reports of elevated chitinase levels in ALS are interesting, as they are established surrogate markers of a chronic inflammatory response. While post-mortem studies have indicated glial expression, the cellular sources for these moieties remain to be fully understood. Therefore, the objective of this pilot study was to examine whether the peripheral immune system also contributes to chitinase dysregulation in ALS. The temporal expression of CHIT1, CHI3L1, and CHI3L2 in non-polarized monocyte-derived macrophages (MoMas) from ALS patients and healthy controls (HCs) was examined. We demonstrate that while CHIT1 and CHI3L1 display similar temporal expression dynamics in both groups, profound between-group differences were noted for these targets at later time-points i.e., when cells were fully differentiated. CHIT1 and CHI3L1 expression were significantly higher in MoMas from ALS patients at both the transcriptomic and protein level, with CHI3L1 levels also being influenced by age. Conversely, CHI3L2 expression was not influenced by disease state, culture duration, or age. Here, we demonstrate for the first time, that in ALS, circulating immune cells have an intrinsically augmented potential for chitinase production that may propagate chronic neuroinflammation, and how the ageing immune system itself contributes to neurodegeneration.

Patterns of grey and white matter changes differ between bulbar and limb onset amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that is characterized by a high heterogeneity in patients' disease course. Patients with bulbar onset of symptoms (b-ALS) have a poorer prognosis than patients with limb onset (l-ALS). However, neuroimaging correlates of the assumed biological difference between b-ALS and l-ALS may have been obfuscated by patients' diversity in the disease course. We conducted Voxel-Based-Morphometry (VBM) and Tract-Based-Spatial-Statistics (TBSS) in a group of 76 ALS patients without clinically relevant cognitive deficits. The subgroups of 26 b-ALS and 52 l-ALS patients did not differ in terms of disease Phase or disease aggressiveness according to the D50 progression model. VBM analyses showed widespread ALS-related changes in grey and white matter, that were more pronounced for b-ALS. TBSS analyses revealed that b-ALS was predominantly characterized by frontal fractional anisotropy decreases. This demonstrates a higher degree of neurodegenerative burden for the group of b-ALS patients in comparison to l-ALS. Correspondingly, higher bulbar symptom burden was associated with right-temporal and inferior-frontal grey matter density decreases as well as fractional anisotropy decreases in inter-hemispheric and long association tracts. Contrasts between patients in Phase I and Phase II further revealed that b-ALS was characterized by an early cortical pathology and showed a spread only outside primary motor regions to frontal and temporal areas. In contrast, l-ALS showed ongoing structural integrity loss within primary motor-regions until Phase II. We therefore provide a strong rationale to treat both onset types of disease separately in ALS studies.

Disease aggressiveness signatures of amyotrophic lateral sclerosis in white matter tracts revealed by the D50 disease progression model

Numerous neuroimaging studies in amyotrophic lateral sclerosis (ALS) have reported links between structural changes and clinical data; however phenotypic and disease course heterogeneity have occluded robust associations. The present study used the novel D50 model, which distinguishes between disease accumulation and aggressiveness, to probe correlations with measures of diffusion tensor imaging (DTI). DTI scans of 145 ALS patients and 69 controls were analyzed using tract-based-spatial-statistics of fractional anisotropy (FA), mean- (MD), radial (RD), and axial diffusivity (AD) maps. Intergroup contrasts were calculated between patients and controls, and between ALS subgroups: based on (a) the individual disease covered (Phase I vs. II) or b) patients' disease aggressiveness (D50 value). Regression analyses were used to probe correlations with model-derived parameters. Case-control comparisons revealed widespread ALS-related white matter pathology with decreased FA and increased MD/RD. These affected pathways showed also correlations with the accumulated disease for increased MD/RD, driven by the subgroup of Phase I patients. No significant differences were noted between patients in Phase I and II for any of the contrasts. Patients with high disease aggressiveness (D50 < 30 months) displayed increased AD/MD in bifrontal and biparietal pathways, which was corroborated by significant voxel-wise regressions with D50. Application of the D50 model revealed associations between DTI measures and ALS pathology in Phase I, representing individual disease accumulation early in disease. Patients' overall disease aggressiveness correlated robustly with the extent of DTI changes. We recommend the D50 model for studies developing/validating neuroimaging or other biomarkers for ALS.

Triage of Amyotrophic Lateral Sclerosis Patients during the COVID-19 Pandemic: An Application of the D50 Model

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, the management of which requires the continuous provision of multidisciplinary therapies. Owing to the novel coronavirus disease (COVID-19) pandemic, regular contact with ALS patients at our center was severely restricted and patient care was at risk by delay of supportive therapies. We established a triage system based on the D50 disease progression model and were thus able to identify a prospective cohort with high disease aggressiveness (D50 < 30). Thirty-seven patients with highly aggressive disease were actively offered follow-up, either via telephone or on-site, depending on their disease-specific needs and abilities. We describe here the procedures, obstacles, and results of these prescient efforts during the restrictions caused by COVID-19 in the period between March and June 2020. In conclusion, four patients with highly aggressive disease were initiated with non-invasive ventilation and two received a gastrostomy. We could show that a comparable amount of advanced care was induced in a retrospective cohort within a similar time period one year prior to the COVID-19 outbreak. Our workflow to identify high-risk patients via D50 model metrics can be easily implemented and integrated within existing centers. It helped to maintain a high quality of advanced care planning for our ALS patients.

Modelling disease course in amyotrophic lateral Sclerosis: pseudo-longitudinal insights from cross-sectional health-related quality of life data

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disorder with limited robust disease-modifying therapies presently available. While several treatments are aimed at improving health-related quality of life (HRQoL), longitudinal data on how QoL changes across the disease course are rare.

OBJECTIVES

To explore longitudinal changes in emotional well-being and HRQoL in ALS.

METHODS

Of the 161 subjects initially recruited, 39 received 2 subsequent follow-up assessments at 6 and 12 months after baseline. The ALS Functional Rating Scale-Revised (ALSFRS-R) was used to assess physical impairment. HRQoL was assessed using the ALS Assessment Questionnaire (ALSAQ-40). The D50 disease progression model was applied to explore longitudinal changes in HRQoL.

RESULTS

Patients were primarily in the early semi-stable and early progressive model-derived disease phases. Non-linear correlation analyses showed that the ALSAQ-40 summary index and emotional well-being subdomain behaved differently across disease phases, indicating that the response shift occurs early in disease. Both the ALSFRS-R and ALSAQ-40 significantly declined at 6- and 12-monthly follow-ups.

CONCLUSION

ALSAQ-40 summary index and emotional well-being change comparably over both actual time and model-derived phases, indicating that the D50 model enables pseudo-longitudinal interpretations of cross-sectional data in ALS.