Beyond standard rehabilitation programs : working with people with MS for adequate goal setting and rehabilitation treatment evaluation

Shared decision-making occurs when the decision is ‘preference sensitive’. It consists of identifying the different treatment options (choice talk), considering the advantages and disadvantages of each option (option talk), and then supporting making the decision in the light of an individual’s experiences and values (decision talk). It is most effective when working with an ‘activated patient’, that is, one who is prepared for the shared decision-making role. In rehabilitation, many decisions are preference sensitive. These decisions may be framed as ‘goal setting’. Skilled clinicians can support patients to learn goal setting skills until the person has the skills to maintain health supporting behaviours most of the time, only seeing a clinical team at times of change or crisis. The steps in goal setting can be summarised as building empathy, creating a contract, identifying priorities, summarising the conversation, articulating the goal, defining actions, building coping plans, and then reviewing progress. Working with people with MS can extend beyond working with individuals to a consideration of what people with MS want from services. This can result in the co-production and co-design of services, as well as the identification of research priorities as exemplified by the James Lind Alliance

What is 'Early intervention' for work related difficulties for people with multiple sclerosis?: a case study report

Background: Employment matters and at diagnosis most people with multiple sclerosis are in full time work or education. 75% of people with multiple sclerosis report the condition has impacted on this employment or career opportunities. Early intervention to support people in work is advocated for in the literature. This paper starts the journey of exploring what is meant by early. Methods: A randomized control trial was undertaken offering either occupational therapy led early intervention or usual care to people diagnosed with multiple sclerosis within one year. Two cases were purposively selected from the treatment group and used to illustrate the importance as well as the nature of early intervention. Results: Both participants received occupational therapy led support which included fatigue management, advice about legal rights, support accessing services such as Access to Work, and support with disclosure in the workplace. Conclusions: Neither of the participants had reported any work problems at the point of referral. However the clinical intervention led to the identification of small concerns and worries. The education and support offered to these two participants alleviated these worries. Early support and education to enable people with multiple sclerosis to manage their condition in the work place can have a positive impact. This may equip them better for the journey ahead

Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population

Background Information: Falls are associated with high direct and indirect costs, and significant morbidity and mortality for patients. Pathological falls are usually a result of a compromised motor system, and/or cognition. Very little research has been conducted on predicting falls based on this premise. Aims: To demonstrate that cognitive and motor tests can be used to create a robust predictive tool for falls. Methods: Three tests of attention and executive function (Stroop, Trail Making, and Semantic Fluency), a measure of physical function (Walk-12), a series of questions (concerning recent falls, surgery and physical function) and demographic information were collected from a cohort of 323 patients at a tertiary neurological center. The principal outcome was a fall during the in-patient stay (n = 54). Data-driven, predictive modelling was employed to identify the statistical modelling strategies which are most accurate in predicting falls, and which yield the most parsimonious models of clinical relevance. Results: The Trail test was identified as the best predictor of falls. Moreover, addition of any others variables, to the results of the Trail test did not improve the prediction (Wilcoxon signed-rank p < .001). The best statistical strategy for predicting falls was the random forest (Wilcoxon signed-rank p < .001), based solely on results of the Trail test. Tuning of the model results in the following optimized values: 68% (+- 7.7) sensitivity, 90% (+- 2.3) specificity, with a positive predictive value of 60%, when the relevant data is available. Conclusion: Predictive modelling has identified a simple yet powerful machine learning prediction strategy based on a single clinical test, the Trail test. Predictive evaluation shows this strategy to be robust, suggesting predictive modelling and machine learning as the standard for future predictive tools

Quality-of-life measures for use within care homes:A systematic review of their measurement properties

Objective: the aims of this review were (i) to identify quality-of-life (QoL) measures which have had their measurement properties validated in people residing in care homes or nursing homes, and to critically compare and summarise these instruments and (ii) to make recommendations for measurement instruments. Methods: bibliographic databases PsycINFO, PubMed, Cochrane, CINAHL and Embase were searched for articles evaluating measurement properties of QoL instruments in people residing in care homes. Methodological quality of studies was assessed using the consensus-based standards for the selection of health measurement instruments checklist. Measurement properties of instruments were appraised using a systematic checklist. Results: the search strategy resulted in 3252 unique citations, of which 15 articles were included in this review. These articles assessed 13 instruments, 8 of which were dementia or Alzheimer specific instruments. The QUALIDEM, a dementia-specific observational instrument, had the widest array of information available on its measurement properties, which were mostly satisfactory. Most measurement instruments lacked information on hypotheses testing and content validity. Information on responsiveness and measurement error was not available for any instrument. Conclusions: for people with dementia living in care homes, the QUALIDEM is recommended for measuring QoL. For residents without dementia, we recommend Kane et al.'s Psychosocial Quality of Life Domains questionnaire. Studies of higher methodological quality, assessing a wider range of measurement properties are needed to allow a more fully informed choice of QoL instrument

Developing vocational rehabilitation services for people with long-term neurological conditions : identifying facilitators and barriers to service provision

Purpose: This study aimed to understand existing vocational rehabilitation (VR) service provision in one locality in London (population 3.74 million), identify any gaps and explore reasons for this, to support service development. Method: Using Soft Systems Methodology to guide the research process, semi - structured interviews were completed with nine participants, who were clinicians and managers providing VR within NHS services. Data were analysed thematically to build a ‘rich picture’ and develop a conceptual model of VR service delivery. Findings were then ratified with participants at an engagement event. Results: The findings indicate a spectrum of VR service provision for long - term neurological conditions with differing levels of funding in place. VR often takes place ‘under the radar’ and therefore the true VR needs of this population, and the extent of service provision is not known. There is inconsistency of understanding across the services as to what constitutes VR and outcomes are not routinely measured. Conclusion: For VR services to develop they require appropriate funding, driven by Government policy to commissioners. Clear definitions of VR, collecting and sharing outcome data and effective communication across services are needed at a local level. This is expressed in a conceptual model of VR service delivery

Minimum data set to measure rehabilitation needs and health outcome after major trauma : application of an international framework

BACKGROUND: Measurement of long term health outcome after trauma remains non-standardized and ambiguous which limits national and international comparison of burden of injuries. The World Health Organization (WHO) has recommended the application of the International Classification of Function, Disability and Health (ICF) to measure rehabilitation and health outcome worldwide. No previous poly-trauma studies have applied the ICF comprehensively to evaluate outcome after injury. AIM: To apply the ICF categorization in patients with traumatic injuries to identify a minimum data set of important rehabilitation and health outcomes to enable national and international comparison of outcome data. DESIGN: A mixed methods design of patient interviews and an on-line survey. SETTING: An ethnically diverse urban major trauma center in London. POPULATION: Adult patients with major traumatic injuries (poly-trauma) and international health care professionals (HCPs) working in acute and post-acute major trauma settings. METHODS: Mixed methods investigated patients and health care professionals (HCPs) perspectives of important rehabilitation and health outcomes. Qualitative patient data and quantitative HCP data were linked to ICF categories. Combined data were refined to identify a minimum data set of important rehabilitation and health outcome categories. RESULTS: Transcribed patient interview data (N.=32) were linked to 234 (64%) second level ICF categories. Two hundred and fourteen HCPs identified 121 from a possible 140 second level ICF categories (86%) as relevant and important. Patients and HCPs strongly agreed on ICF body structures and body functions categories which include temperament, energy and drive, memory, emotions, pain and repair function of the skin. Conversely, patients prioritised domestic tasks, recreation and work compared to HCP priorities of self-care and mobility. Twenty six environmental factors were identified. Patient and HCP data were refined to recommend a 109 possible ICF categories for a minimum data set. CONCLUSIONS: The comprehensive measurement of health outcomes after trauma is important for patients, health professionals and trauma systems. An internationally applied ICF minimum data set will standardize the language used and concepts measured after major trauma to enable national and international comparison of outcome data

Systematic review of health-related work outcome measures and quality criteria-based evaluations of their psychometric properties

Objective To examine the state of psychometric validation in the health-related work outcome literature. Data Sources We searched PubMed, PubMed Central, CINAHL, Embase (plus Embase Classic), and PsycINFO from inception to January 2016 using the following search terms: stroke, multiple sclerosis, epilepsy, spinal cord injury, brain injury, musculoskeletal disease, work, absenteeism, presenteeism, occupation, employment, job, outcome measure, assessment, work capacity evaluation, scale, and questionnaire. Study Selection From the 22,676 retrieved abstracts, 597 outcome measures were identified. Inclusion was based on content analysis. There were 95 health-related work outcome measures retained; of these, 2 were treated as outliers and therefore are discussed separately. All 6 authors individually organized the 93 remaining scales based on their content. Data Extraction A follow-up search using the same sources, and time period, with the name of the outcome measures and the terms psychometric, reliability, validity, and responsiveness, identified 263 unique classical test theory psychometric property datasets for the 93 tools. An assessment criterion for psychometric properties was applied to each article, and where consensus was not achieved, the rating delivered by most of the assessors was reported. Data Synthesis Of the articles reported, 18 reporting psychometric data were not accessible and therefore could not be assessed. There were 39 that scored 80%. The 3 outcome measures associated with the highest scoring datasets were the Sheehan Disability Scale, the Fear Avoidance Beliefs Questionnaire, and the assessment of the Subjective Handicap of Epilepsy. Finally, only 2 psychometric validation datasets reported the complete set of baseline psychometric properties. Conclusions This systematic review highlights the current limitations of the health-related work outcome measure literature, including the limited number of robust tools available

Managing the long term effects of covid-19 : summary of NICE, SIGN, and RCGP rapid guideline

For a proportion of people covid-19 leads to long term effects that can have a significant impact on quality of life. According to the Office for National Statistics, around one in five people testing positive for covid-19 exhibit symptoms for a period of five weeks or more.1 This presents challenges for determining best-practice standards of care. As yet, no commonly agreed clinical definition of long term covid-19 exists, nor a clear definition of treatment pathway. To assist clinicians, the National Institute for Health and Care Excellence (NICE), the Scottish Intercollegiate Guidelines Network (SIGN), and the Royal College of General Practitioners (RCGP) have developed the “COVID-19 rapid guideline: managing the long term effects of COVID-19.”2 It covers care for people with signs and symptoms that continue for more than four weeks, and which developed during or after an infection consistent with covid-19, and which are not explained by alternative diagnoses

The Trail Making test : a study of its ability to predict falls in the acute neurological in-patient population

Objective: To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. Design: Prospective cohort study. Setting: Tertiary neurological and neurosurgical center. Subjects: In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. Main Measures: Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). Results: The principal outcome was a fall during the in-patient stay (n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. Conclusion: This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test

Functional near infrared spectroscopy as a probe of brain function in people with prolonged disorders of consciousness

Near infrared spectroscopy (NIRS) is a non-invasive technique which measures changes in brain tissue oxygenation. NIRS has been used for continuous monitoring of brain oxygenation during medical procedures carrying high risk of iatrogenic brain ischemia and also has been adopted by cognitive neuroscience for studies on executive and cognitive functions. Until now, NIRS has not been used to detect residual cognitive functions in patients with prolonged disorders of consciousness (pDOC). In this study we aimed to evaluate the brain function of patients with pDOC by using a motor imagery task while recording NIRS. We also collected data from a group of age and gender matched healthy controls while they carried out both real and imagined motor movements to command. We studied 16 pDOC patients in total, split into two groups: five had a diagnosis of Vegetative state/Unresponsive Wakefulness State, and eleven had a diagnosis of Minimally Conscious State. In the control subjects we found a greater oxy-haemoglobin (oxyHb) response during real movement compared with imagined movement. For the between group comparison, we found a main effect of hemisphere, with greater depression of oxyHb signal in the right > left hemisphere compared with rest period for all three groups. A post-hoc analysis including only the two pDOC patient groups was also significant suggesting that this effect was not just being driven by the control subjects. This study demonstrates for the first time the feasibility of using NIRS for the assessment of brain function in pDOC patients using a motor imagery task