Susan Mathers’s research while affiliated with Calvary Health Care and other places

Background and purpose Given the accepted multistep process of disease causation in amyotrophic lateral sclerosis (ALS), the present study was undertaken to determine the number of steps required for disease onset across each of the ALS phenotypes. Methods Clinical and demographic data were prospectively accumulated using the Australian Motor Neurone Disease Registry (2005–2016), and age‐specific incidence rates were calculated. Poisson regression was utilized to assess the relationship between log age‐specific incidence and log age of onset, with McFadden's R² used to assess the goodness of fit of the model. Results In total, 2647 ALS patients were included, with mean disease‐onset age being 62.2 ± 12.1 years. A linear relationship between log incidence and log age was established across ALS phenotypes, with variable slope estimates: bulbar 5.1 (95% confidence interval [CI] 4.6–5.6); cervical 2.7 (95% CI 2.3–3.0); lumbar 3.5 (95% CI 3.2–3.9); flail arm 4.7 (95% CI 3.9–5.5); flail leg 3.6 (95% CI 2.6–4.5); primary lateral sclerosis 2.7 (95% CI 1.8–3.7). Slope estimates were significantly higher in the bulbar compared to the cervical, lumbar and primary lateral sclerosis phenotypes. McFadden's R² values were >0.4 for all phenotypes indicating excellent model fit. Discussion A multistep process has been established across all ALS phenotypes with variable slope estimates, suggesting that the number of steps to develop disease is different across clinical presentations. Identification of mechanisms underlying slope estimate variability could exert pathophysiological significance.

Background Interactions within multidisciplinary teams can underpin improved care integration and outcomes for people living with Huntington’s disease (HD). Social network analysis, based on graph theory, provides a tool to measure interaction and influence within these inter-professional networks. Aim: To measure interaction and influence among health professionals within a HD clinic using social network analysis. Methods This study was conducted at a publicly funded multidisciplinary HD clinic in Victoria, Australia. Re-development in 2016-2017 led to transition from a neurologist-led to a more collaborative service delivery. We adopted a retrospective before (Year 2014-2015; n=339) and after (Year 2018-2019; n=346) cohort design. Patient-related interaction data was manually extracted from medical records, then exported for analysis in R. The network analysis included interaction correlations, Walktrap community detection (based on the idea that short random walks tend to stay in the same community), and local (degree, closeness, betweenness) and global (PageRank) centrality. Results There were more of stronger positive interaction correlations in the After cohort (between physiotherapist and occupational therapist (OT) (r=0.57); speech pathologist (SP) and dietician (r=0.47); SP and OT (r=0.45); physiotherapist and SP (r=0.41); OT and dietician (r=0.41)) compared from the Before cohort (SP and dietician (r=0.53); neurologist and clinical liaison (r=0.38)). Walktrap algorithm detected three unconnected communities in the Before cohort, while the After cohort had two communities (one larger-sized) connected through the neurologist (Figure 1). The most influential professionals in the After cohort were neurologist (highest degree=7 and betweenness=22) [most social connections and control over information flow, respectively], clinical liaison (lowest closeness=0.27) [closest to other professionals], and dietician (highest PageRank=0.15) [most in-links from important professionals]. Conclusion This study showed a social network approach to measure interaction and influence among health professionals within a HD clinic. • Download figure • Open in new tab • Download powerpoint Abstract H022 Figure 1 Walktrap community detection: before and after cohorts

Background Predicting 12-month mortality is important to optimise end-of-life care for people with Huntington’s disease (HD). Machine learning (ML) may outperform traditional statistical methods when modelling complex high-dimensional, censored survival data. We compared several tree-based ensemble ML methods for predicting 12-month mortality among patients with HD. Methods We conducted a retrospective review of electronic records of adults with genetically-confirmed HD at an Australian centre (1 January 2018 - 30 June 2023). Three tree-based ML methods (random survival forest (rfsrc), ranger, Bayesian Additive Regression Trees (BART)) were compared to Cox proportional-hazards (CoxPH) model using area under the receiver operating characteristics curve (AUC). The covariates included patient demographics, functional status, symptoms, complications, comorbidities. Prior to model development, we employed the standard approach of splitting the dataset into training (70%) and validation (30%) sets. We used dimensionless Shapley value from cooperative Game Theory to determine the covariates making the largest contributions to death. Results Among 343 patients, 71 (20.7%) died. The AUCs for training and validation sets, respectively, were 0.96 and 0.79 (rfsrc), 0.99 and 0.92 (ranger), 0.94 and 0.90 (BART), 0.93 and 0.75 (CoxPH). A smaller difference between training and validation AUC for BART model suggests less overfitting and more accurate predictions compared to other models. Shapley values for all ML models showed poor functional status (UHDRS-TFC<1) contributed the most to death within 12 months. For BART model, depression, male sex, dysphagia, dementia were additional important predictors. Conclusion BART can accurately predict 12-month mortality in HD as compared to traditional Cox method.

Motor neurone disease/amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder with no known cure, where death is usually secondary to progressive respiratory failure. Assisting people with ALS through their disease journey is complex and supported by clinics that provide comprehensive multidisciplinary care (MDC). This review aims to apply both a respiratory and a complexity lens to the key roles and areas of practice within the MDC model in ALS. Models of noninvasive ventilation care, and considerations in the provision of palliative therapy, respiratory support, and speech and language therapy are discussed. The impact on people living with ALS of both inequitable funding models and the complexity of clinical care decisions are illustrated using case vignettes. Considerations of the impact of emerging antisense and gene modifying therapies on MDC challenges are also highlighted. The review seeks to illustrate how MDC members contribute to collective decision-making in ALS, how the sum of the parts is greater than any individual care component or health professional, and that the MDC per se adds value to the person living with ALS. Through this approach we hope to support clinicians to navigate the space between what are minimum, guideline-driven, standards of care and what excellent, person-centred ALS care that fully embraces complexity could be. Educational aims To highlight the complexities surrounding respiratory care in ALS. To alert clinicians to the risk that complexity of ALS care may modify the effectiveness of any specific, evidence-based therapy for ALS. To describe the importance of person-centred care and shared decision-making in optimising care in ALS.

Background Motor neurone disease is a rare but debilitating illness with incomplete evidence regarding patients’ symptom burden. Palliative care and generalist clinicians are often in-experienced in caring for these patients and assessing their needs. Aim To identify the symptom prevalence and severity experienced by patients with motor neurone disease. Secondary objectives were to examine differences in symptom burden and clusters according to phenotype, functional status, palliative care provision and those in their last months of life. Design A point prevalence study assessing patient-reported symptoms using a modified IPOS-Neuro assessment tool, incorporating 41 symptom items. Setting/participants Patients with motor neurone disease attending the State-wide Progressive Neurological Disease Service or inpatient unit at Calvary Health Care Bethlehem, Melbourne Australia, from March to December 2021. Results A total of 102 patients participated, the majority diagnosed with lumber-onset (30.4%), bulbar-onset (28.4%) and cervical-onset (25.5%) phenotypes. Patients experienced a median of 17 symptoms (range 2–32) with a median of 3 symptoms rated as severe/overwhelming (range 0–13). Motor and functional symptoms predominated, with differences in symptom clusters present according to phenotype. Patients had a higher number of severe/overwhelming symptoms if they were accessing palliative care services (p = 0.005), in their last 6 months of life (p = 0.003) and experiencing moderate or severe functional impairment (p < 0.001). Conclusions Patients with motor neurone disease report high symptom burden. A validated motor neurone disease-specific symptom assessment tool is needed to accurately assess patients, including important variations in symptom clusters according to phenotype. Further research must focus on evidence-based treatment guidelines for symptoms experienced commonly and severely.

Background Neuropalliative care is a newly-defined subspeciality bringing specific aspects of fields of neurology and palliative care together to better meet the complex care needs of people with progressive neurological diseases. Examining these needs would help provide guidance about developing relevant models of care and identify gaps in research knowledge. Aim To identify current models and approaches to neuropalliative care for people with progressive neurological diseases and the priorities for future research work. Design A scoping literature review following the methods described by the Joanna Briggs Institute. Data sources An electronic search of the literature was undertaken from six sources including MEDLINE (Ovid), EMCARE, PsycINFO and CINAHL covering the years January 2011 to September 2021. Results Twenty-eight studies were found examining neuropalliative care from the perspectives of 4795 PND patients, 774 informal carers and 138 health professionals. All studies held themes of integrative care, with most studies employing outpatient models of multidisciplinary care. Topics discussed included: overcoming local system-issues, providing education for professionals, patients and carers, early referral and capturing outcome measures for quality-assurance and future research work. Conclusions Most models of neuropalliative care described in the international literature are predominantly outpatient, multidisciplinary and integrative. Clinicians typically utilise existing neurology and palliative care infrastructure to provide care. More high-quality research and outcome tools are needed to guide the design of evidence-based palliative care for people with progressive neurological diseases.

Importance: Brain-computer interface (BCI) implants have previously required craniotomy to deliver penetrating or surface electrodes to the brain. Whether a minimally invasive endovascular technique to deliver recording electrodes through the jugular vein to superior sagittal sinus is safe and feasible is unknown. Objective: To assess the safety of an endovascular BCI and feasibility of using the system to control a computer by thought. Design, setting, and participants: The Stentrode With Thought-Controlled Digital Switch (SWITCH) study, a single-center, prospective, first in-human study, evaluated 5 patients with severe bilateral upper-limb paralysis, with a follow-up of 12 months. From a referred sample, 4 patients with amyotrophic lateral sclerosis and 1 with primary lateral sclerosis met inclusion criteria and were enrolled in the study. Surgical procedures and follow-up visits were performed at the Royal Melbourne Hospital, Parkville, Australia. Training sessions were performed at patients' homes and at a university clinic. The study start date was May 27, 2019, and final follow-up was completed January 9, 2022. Interventions: Recording devices were delivered via catheter and connected to subcutaneous electronic units. Devices communicated wirelessly to an external device for personal computer control. Main outcomes and measures: The primary safety end point was device-related serious adverse events resulting in death or permanent increased disability. Secondary end points were blood vessel occlusion and device migration. Exploratory end points were signal fidelity and stability over 12 months, number of distinct commands created by neuronal activity, and use of system for digital device control. Results: Of 4 patients included in analyses, all were male, and the mean (SD) age was 61 (17) years. Patients with preserved motor cortex activity and suitable venous anatomy were implanted. Each completed 12-month follow-up with no serious adverse events and no vessel occlusion or device migration. Mean (SD) signal bandwidth was 233 (16) Hz and was stable throughout study in all 4 patients (SD range across all sessions, 7-32 Hz). At least 5 attempted movement types were decoded offline, and each patient successfully controlled a computer with the BCI. Conclusions and relevance: Endovascular access to the sensorimotor cortex is an alternative to placing BCI electrodes in or on the dura by open-brain surgery. These final safety and feasibility data from the first in-human SWITCH study indicate that it is possible to record neural signals from a blood vessel. The favorable safety profile could promote wider and more rapid translation of BCI to people with paralysis. Trial registration: ClinicalTrials.gov Identifier: NCT03834857.

Introduction/aims: Rate of disease progression (ΔFS), measured as change in the amyotrophic lateral sclerosis functional rating scale (ALSFRS-R) and body mass index (BMI), are predictors of survival in amyotrophic lateral sclerosis (ALS). The aim of this study was to assess the utility of these clinical biomarkers, along with neurophysiological measures such as the split hand index (SI), in monitoring disease progression. Methods: Clinical trial data was collected from 107 patients recruited into the Tecfidera in ALS trial. The prognostic utility of clinical and neurophysiological measures, including ΔFS, BMI, SI and neurophysiological index (NPI) were assessed cross-sectionally and longitudinally (40-weeks). The outcome measures of disease severity and progression included: (i) ALSFRS-R; (ii) Medical Research Council (MRC) scores; and (iii) forced vital capacity and sniff nasal inspiratory pressure. Results: Fast progressor ALS patients (ΔFS≥1.1) exhibited significantly lower ALSFRS-R and total MRC scores at baseline. A baseline ΔFS score ≥1.1 was associated with a greater reduction in ALSFRS-R (P=0.002) and MRC scores (P=0.002) over 40 weeks. Baseline BMI <25 was also associated with faster reduction of ALSFRS-R and MRC scores. SI and NPI were associated with disease severity at baseline but not with subsequent rate of disease progression. Discussion: Implementation of the assessed clinical and neurophysiological biomarkers may assist in patient management and stratification into clinical trials.

Objective Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. Methods We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks. Results The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. Interpretation Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.

The growing body of information-seeking and decision-making literature in motor neu-rone disease (MND) has not yet explored the impact of health literacy. Health literacy relates to the skills people have to access, understand, and use health information and is influenced by motivation to engage with healthcare. We aimed to better understand how people affected by MND engage in healthcare by examining longitudinal interview data using the construct of health literacy. Semi-structured interviews were conducted with 19 persons living with MND and 15 carers recruited from a specialist MND clinic using maximum variation sampling. Transcripts were deductively coded using a framework of health literacy behaviours. The analysis used a matrix-based approach for thematic analysis of longitudinal data. People living with MND and carers sought nuanced information dependent on their priorities and attitudes. Information uptake was influenced by perceived relevancy and changed over time. Time allowed opportunity to reflect on and understand the significance of information provided. The findings indicate that persons living with MND and carers benefit when information and consultations are adapted to meet their communication needs. The results highlight the potential benefits of gaining an early understanding of and accommodating the communication needs, personal preferences, and emotional readiness for information for persons living with MND and their carers.