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ALS is a neurodegenerative disease characterized by loss of motor neurons, resulting in progressive weakness and wasting of muscles. The average survival time is 2–5 years, mostly due to respiratory failure. Since current therapies can prolong survival time by only a few months, multidisciplinary care remains the cornerstone of the management of ALS. At the ALS Expert Centre of University Hospitals Leuven, a large proportion of Belgian ALS patients are seen for diagnosis and a significant number is also in follow-up with the multidisciplinary team. In this retrospective study, we compared the outcome of incident patients who were in follow-up at our site with patients who were not in follow-up. We included 659 patients of which 557 (84.5%) received specialized care at the ALS Expert Centre. After adjusting for clinically relevant prognostic parameters, multidisciplinary follow-up significantly prolonged survival (p = 0.004; HR = 0.683; CI 95% [0.528 − 0.884]). This increase in survival is mainly driven by patients with spinal onset (p = 0.035; HR = 0.746; CI 95% [0.568 − 0.980]), since no significant increased survival time was observed in patients with bulbar onset (p = 0.28; HR = 0.778; CI 95% [0.495 − 1.223]). These data confirm that multidisciplinary follow-up contributes to a better outcome of patients, emphasizing the importance of multidisciplinary specialized care in ALS. [ABSTRACT FROM AUTHOR]

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. 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. 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. The extent of motor and extramotor involvement in patients with ALS at disease presentation holds complementary prognostic information. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is characterized by progressive loss of upper and lower motor neurons. In 10% of patients, the disorder runs in the family. Our aim was to study the impact of ALS-causing gene mutations on cerebral glucose metabolism. Between October 2010 and October 2022, 538 patients underwent genetic testing for mutations with strong evidence of causality for ALS and 18F-2-fluoro-2-deoxy-D-glucose-PET (FDG PET), at University Hospitals Leuven. We identified 48 C9orf72-ALS and 22 SOD1-ALS patients. After propensity score matching, two cohorts of 48 and 21 matched sporadic ALS patients, as well as 20 healthy controls were included. FDG PET images were assessed using a voxel-based and volume-of-interest approach. We observed widespread frontotemporal involvement in all ALS groups, in comparison to healthy controls. The degree of relative glucose metabolism in SOD1-ALS in motor and extra-motor regions did not differ significantly from matched sporadic ALS patients. In C9orf72-ALS, we found more pronounced hypometabolism in the peri-rolandic region and thalamus, and hypermetabolism in the medulla extending to the pons, in comparison to matched sporadic ALS patients. Our study revealed C9orf72-dependent differences in glucose metabolism in the peri-rolandic region, thalamus, and brainstem (i.e., medulla, extending to the pons) in relation to matched sporadic ALS patients. [ABSTRACT FROM AUTHOR]

Purpose: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with on average a 1-year delay between symptom onset and diagnosis. Studies have demonstrated the value of [18F]-FDG PET as a sensitive diagnostic biomarker, but the discriminatory potential to differentiate ALS from patients with symptoms mimicking ALS has not been investigated. We investigated the combination of brain and spine [18F]-FDG PET-CT for differential diagnosis between ALS and ALS mimics in a real-life clinical diagnostic setting. Methods: Patients with a suspected diagnosis of ALS (n = 98; 64.8 ± 11 years; 61 M) underwent brain and spine [18F]-FDG PET-CT scans. In 62 patients, ALS diagnosis was confirmed (67.8 ± 10 years; 35 M) after longitudinal follow-up (average 18.1 ± 8.4 months). In 23 patients, another disease was diagnosed (ALS mimics, 60.9 ± 12.9 years; 17 M) and 13 had a variant motor neuron disease, primary lateral sclerosis (PLS; n = 4; 53.6 ± 2.5 years; 2 M) and progressive muscular atrophy (PMA; n = 9; 58.4 ± 7.3 years; 7 M). Spine metabolism was determined after manual and automated segmentation. VOI- and voxel-based comparisons were performed. Moreover, a support vector machine (SVM) approach was applied to investigate the discriminative power of regional brain metabolism, spine metabolism and the combination of both. Results: Brain metabolism was very similar between ALS mimics and ALS, whereas cervical and thoracic spine metabolism was significantly different (in standardised uptake values; cervical: ALS 2.1 ± 0.5, ALS mimics 1.9 ± 0.4; thoracic: ALS 1.8 ± 0.3, ALS mimics 1.5 ± 0.3). As both brain and spine metabolisms were very similar between ALS mimics and PLS/PMA, groups were pooled for accuracy analyses. Mean discrimination accuracy was 65.4%, 80.0% and 81.5%, using only brain metabolism, using spine metabolism and using both, respectively. Conclusion: The combination of brain and spine FDG PET-CT with SVM classification is useful as discriminative biomarker between ALS and ALS mimics in a real-life clinical setting. [ABSTRACT FROM AUTHOR]

Background: The Edinburgh cognitive and behavioral ALS screen (ECAS) was developed specifically to detect cognitive and behavioral changes in patients with amyotrophic lateral sclerosis (ALS). Differences with regard to normative data of different (language) versions of neuropsychological tests such as the ECAS exist. Objective: To derive norms for the Dutch version of the ECAS. Methods: Normative data were derived from a large sample of 690 control subjects and cognitive profiles were compared between a matched sample of 428 patients with ALS and 428 control subjects. Results: Age, level of education, and sex were significantly associated with performance on the ECAS in the normative sample. ECAS data were not normally distributed and therefore normative data were expressed as percentile ranks. The comparison of ECAS scores between patients and control subjects demonstrated that patients obtained significantly lower scores for language, executive function, verbal fluency, and memory, which is in line with the established cognitive profile of ALS. Conclusion: For an accurate interpretation of ECAS results, it is important to derive normative data in large samples with nonparametric methods. The present normative data provide healthcare professionals with an accurate estimate of how common or uncommon patients' ECAS scores are and provide a useful supplement to existing cut-off scores. [ABSTRACT FROM AUTHOR]

Objective: 18F-Fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) single-center studies using support vector machine (SVM) approach to differentiate amyotrophic lateral sclerosis (ALS) from controls have shown high overall accuracy on an individual patient basis using local a priori defined classifiers. The aim of the study was to validate the SVM accuracy on a multicentric level. Methods: A previously defined Belgian (BE) group of 175 ALS patients (61.9 ± 12.2 years, 120M/55F) and 20 screened healthy controls (62.4 ± 6.4 years, 12M/8F) was used to classify another large dataset from Italy (IT), consisting of 195 patients (63.2 ± 11.6 years, 117M/78F) and 40 controls (62 ± 14.4 years; 29M/11F) free of any neurological and psychiatric disorder who underwent whole-body 18F-FDG PET-CT for lung cancer without any evidence of paraneoplastic symptoms. 18F-FDG within-center group comparisons based on statistical parametric mapping (SPM) were performed and SVM classifiers based on the local training sets were applied to differentiate ALS from controls from the other centers. Results: SPM group analysis showed only minor differences between both ALS groups, indicating pattern consistency. SVM using BE data set as training, classified 183/193 ALS-IT correctly (accuracy of 94.8%). However, 35/40 CON-IT were misclassified as ALS (accuracy 12.5%). Furthermore, using IT data as training, ALS-BE could not be distinguished from CON-BE. Within-center SPM group analysis confirmed prefrontal hypometabolism in CON-IT versus CON-BE, indicating subclinical brain changes in patients undergoing oncological scanning. Conclusion: This multicenter study confirms that the 18F-FDG ALS pattern is stable across centers. Furthermore, it highlights the importance of carefully selected controls, as subclinical frontal changes might be present in patients in an oncological setting. [ABSTRACT FROM AUTHOR]

Background: 18F-FDG brain PET measures metabolic changes in neurodegenerative disorders and may discriminate between different diseases even at an early stage. The objective of this study was to classify patients with amyotrophic lateral sclerosis (ALS) and Parkinson plus syndromes (PP). To this end, different approaches were evaluated using generalized linear models and corresponding glucose metabolic brain patterns. Besides direct classification, healthy controls were also included to generate disease-specific metabolic brain patterns and to perform a classification using disease expression scores.Methods: ALS patients (n = 70) and PP patients (n = 33: 20 PSP, 3 CBD, and 10 MSA) were available from an existing database of patients with neuromuscular and movement disorders while age-matched healthy controls (n = 29) were selected from a prospective study. To generate both disease-discriminative (direct classification) and disease-specific (classification versus controls) metabolic brain patterns, data were spatially normalized and a principal component analysis (PCA) was performed prior to classification using either logistic regression (PCA-LR) or a support vector machine (PCA-SVM). Furthermore, a direct SVM approach was considered. To compare the three different approaches, Pearson correlations (r) between pattern expression scores and metabolic brain patterns were evaluated, while pairs of ALS- and PP-specific pattern expression scores were compared using the RV coefficient.Results: Classification between ALS and PP resulted in a sensitivity and specificity ≥ 0.82 for both direct classification and classification according to disease-specific pattern expression scores. PCA-LR, PCA-SVM, and SVM generated very similar metabolic brain patterns with voxelwise correlations ≥ 0.66, while all patterns allowed straightforward identification of ALS- and PP-specific brain regions of hyper- and hypometabolism. Moreover, pattern expression scores were highly correlated among different classifiers with a mean r of 0.94 while a RV coefficient ≥ 0.91 was found between pairs of ALS- and PP-specific pattern expression scores.Conclusion: We demonstrated that a classification between ALS and PP using expression scores of an ALS and PP metabolic brain pattern leads to a similar and high prediction accuracy as direct classification between ALS and PP. Classification performance and disease-specific metabolic patterns, which could support visual reading and improve insight in brain pathology, were very related for different classifiers. [ABSTRACT FROM AUTHOR]

Background: Prevalence estimates of suicidal thoughts and behaviours (STB) among clinically active healthcare professionals during the first wave of COVID19 pandemic are non-existing. The main aim of this study was to investigate the 30-day prevalence of STB and associated risk factors.Methods: As part of the Recovering Emotionally from COVID study (RECOVID), 30-day STB among healthcare professionals (N = 6,409) was assessed in an e-survey in healthcare settings in Belgium. The prevalence of STB and associated risk factors were estimated in multivariable models with individual-level and society-level measures of association. We used post-stratification weights to make the data representative for the entire clinical workforce in Belgium.Results: Prevalence was 3.6% death wish, 1.5% suicide ideation, 1.0% suicide plan, and 0.0% suicide attempt. Thirty-day STB was (a) increased among respondents with lifetime and current mental disorders (mostly depression) and those hospitalized for COVID19 infection, (b) decreased among respondents with social support, and (c) unrelated to work environment.Limitations: This is an explorative cross-sectional study using multivariate models that generates specific hypotheses on the prevalence of and risk factors for STB during the COVID19 pandemic rather than testing specific pathways that lead to STB onset.Conclusions: Across age, gender, professional discipline, and exposure to COVID, lifetime and current mental disorders were highly associated with STB. These factors could guide governments and healthcare organizations in taking up responsibilities in preventing emotional problems and developing resilience among healthcare professionals during, but probably beyond, the current COVID19 pandemic. [ABSTRACT FROM AUTHOR]