Incorporation of telehealth into a multidisciplinary ALS Clinic: feasibility and acceptability

<p><i>Objective</i>: The practice of telehealth in the care of patients with ALS has received little attention, but has the potential to change the multidisciplinary care model. This study was carried out to assess the feasibility and acceptability of telehealth for ALS care via real-time videoconferencing from the clinic to patients’ homes. <i>Methods</i>: Patients and caregivers engaged in live telehealth videoconferencing from their homes with members of a multidisciplinary ALS care team who were located in an ALS clinic, in place of their usual in-person visit to the clinic. Participating patients, their caregivers, and health care providers (HCPs) completed surveys assessing satisfaction with the visit, quality of care, and confidence with the interface. Mixed methods analysis was used for survey responses. <i>Results</i>: Surveys from 11 patients, 12 caregivers, and 15 HCPs were completed. All patients and caregivers, and most HCPs, agreed that the system allowed for good communication, description of concerns, and provision of care recommendations. The most common sentiment conveyed by each group was that telehealth removed the burdens of travel, resulting in lower stress and more comfortable interactions. Caregivers and HCPs expressed more concerns than patients about the ways in which telehealth fell short of in-person care. <i>Conclusions</i>: Telehealth was generally viewed favourably by ALS patients, caregivers, and multidisciplinary team members. Improvements in technology and in methods to provide satisfactory remote care without person-to-person contact should be explored.</p

Pain in amyotrophic lateral sclerosis: Patient and physician perspectives and practices

<p>Our objective was to better understand the experience and impact of pain on ALS patients in the U.S., and to survey ALS physicians on their pain assessment and management practices. Individuals with ALS were invited to complete an online survey of pain in ALS. ALS specialist physicians were sent an e-mail survey about their experiences in evaluating and managing patients’ pain. Nearly 75% of patients with ALS reported significant pain, and most thought that ALS was the source of at least some of this pain. Pain intensity scores (mean 3.9/10) and pain interference scores (mean 4.3/10) were moderate on average, but nearly 80% of participants were using pain medication, including 22% using opioids. Nearly 25% of patients thought they needed stronger pain medication than they were receiving. Physicians generally assess and manage pain in ALS patients, but few use standardized assessment tools. Nearly two-thirds felt that there is a need for better pain management practices and more than one-third felt better training was needed. In conclusion, pain in patients with ALS is not always well controlled. Improvement in care may be facilitated by a more standardized approach to evaluation, and by additional education and training of ALS health care professionals.</p

Describing and characterising variability in ALS disease progression

Background, Objectives: Decrease in the revised ALS Functional Rating Scale (ALSFRS-R) score is currently the most widely used measure of disease progression. However, it does not sufficiently encompass the heterogeneity of ALS. We describe a measure of variability in ALSFRS-R scores and demonstrate its utility in disease characterization. Methods: We used 5030 ALS clinical trial patients from the Pooled Resource Open-Access ALS Clinical Trials database to calculate variability in disease progression employing a novel measure and correlated variability with disease span. We characterized the more and less variable populations and designed a machine learning model that used clinical, laboratory and demographic data to predict class of variability. The model was validated with a holdout clinical trial dataset of 84 ALS patients (NCT00818389). Results: Greater variability in disease progression was indicative of longer disease span on the patient-level. The machine learning model was able to predict class of variability with accuracy of 60.1–72.7% across different time periods and yielded a set of predictors based on clinical, laboratory and demographic data. A reduced set of 16 predictors and the holdout dataset yielded similar accuracy. Discussion: This measure of variability is a significant determinant of disease span for fast-progressing patients. The predictors identified may shed light on pathophysiology of variability, with greater variability in fast-progressing patients possibly indicative of greater compensatory reinnervation and longer disease span. Increasing variability alongside decreasing rate of disease progression could be a future aim of trials for faster-progressing patients.</p