Informal care in multiple sclerosis

International audienceBackgroundHome care for patients with multiple sclerosis relies largely on informal caregivers (ICs).MethodsWe assessed ICs objective burden (Informal Care Time, ICT) and ICs subjective burden (Zarit Burden Inventory, ZBI) and their explanatory factors.ResultsICs (n = 99) were spouses (70%), mean age 52 years, assisting disabled patients with a mean Expanded Disability Status Scale (EDSS) of 5.5, with executive dysfunction (mean DysExecutive Questionnaire [DEX] of 25) and a duration of MS ranging from 1 to 44 years. Objective burden was high (mean ICT = 6.5 hours/day), mostly consisting of supervision time. Subjective burden was moderate (mean ZBI = 27.3). Multivariate analyses showed that both burdens were positively correlated with higher levels of EDSS and DEX, whereas co-residency and IC's female gender correlated with objective burden only and IC's poor mental health status with subjective burden only. When considering MS aggressiveness, it appeared that both burdens were not correlated with a higher duration of MS but rather increased for patients with severe and early cognitive dysfunction and for patients classified as fast progressors according to the Multiple Sclerosis Severity Scale.ConclusionMS disability course and IC's personal situation must be evaluated to understand the burden process and to implement adequate interventions in MS

Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study

Background Lying on the floor for a long period of time has been described as a critical determinant of prognosis following a fall. In addition to fall-related injuries due to the trauma itself, prolonged immobilization on the floor results in a wide range of comorbidities and may double the risk of death in elderly. Thus, reducing the length of Time On the Ground (TOG) in fallers seems crucial in vulnerable individuals with cognitive disorders who cannot get up independently. Objective This study aimed to examine the effect of a new technology called SafelyYou Guardian (SYG) on early post-fall care including reduction of Time Until staff Assistance (TUA) and TOG. Methods SYG uses continuous video monitoring, artificial intelligence, secure networks, and customized computer applications to detect and notify caregivers about falls in real time while providing immediate access to video footage of falls. The present observational study was conducted in 6 California memory care facilities where SYG was installed in bedrooms of consenting residents and families. Fall events were video recorded over 10 months. During the baseline installation period (November 2017 to December 2017), SYG video captures of falls were not provided on a regular basis to facility staff review. During a second period (January 2018 to April 2018), video captures were delivered to facility staff on a regular weekly basis. During the third period (May 2018 to August 2018), real-time notification (RTN) of any fall was provided to facility staff. Two digital markers (TUA, TOG) were automatically measured and compared between the baseline period (first 2 months) and the RTN period (last 4 months). The total number of falls including those happening outside of the bedroom (such as common areas and bathrooms) was separately reported by facility staff. Results A total of 436 falls were recorded in 66 participants suffering from Alzheimer disease or related dementias (mean age 87 years; minimum 65, maximum 104 years). Over 80% of the falls happened in bedrooms, with two-thirds occurring overnight (8 PM to 8 AM). While only 8.1% (22/272) of falls were scored as moderate or severe, fallers were not able to stand up alone in 97.6% (247/253) of the cases. Reductions of 28.3 (CI 19.6-37.1) minutes in TUA and 29.6 (CI 20.3-38.9) minutes in TOG were observed between the baseline and RTN periods. The proportion of fallers with TOG &gt;1 hour fell from 31% (8/26; baseline) to zero events (RTN period). During the RTN period, 76.6% (108/141) of fallers received human staff assistance in less than 10 minutes, and 55.3% (78/141) of them spent less than 10 minutes on the ground. Conclusions SYG technology is capable of reducing TOG and TUA while efficiently covering the area (bedroom) and time zone (nighttime) that are at highest risk. After 6 months of SYG monitoring, TOG was reduced by a factor of 3. The drastic reduction of TOG is likely to decrease secondary comorbid complications, improve post-fall prognosis, and reduce health care costs. </jats:sec

Reduction of Time on the Ground Related to Real-Time Video Detection of Falls in Memory Care Facilities: Observational Study.

BackgroundLying on the floor for a long period of time has been described as a critical determinant of prognosis following a fall. In addition to fall-related injuries due to the trauma itself, prolonged immobilization on the floor results in a wide range of comorbidities and may double the risk of death in elderly. Thus, reducing the length of Time On the Ground (TOG) in fallers seems crucial in vulnerable individuals with cognitive disorders who cannot get up independently.ObjectiveThis study aimed to examine the effect of a new technology called SafelyYou Guardian (SYG) on early post-fall care including reduction of Time Until staff Assistance (TUA) and TOG.MethodsSYG uses continuous video monitoring, artificial intelligence, secure networks, and customized computer applications to detect and notify caregivers about falls in real time while providing immediate access to video footage of falls. The present observational study was conducted in 6 California memory care facilities where SYG was installed in bedrooms of consenting residents and families. Fall events were video recorded over 10 months. During the baseline installation period (November 2017 to December 2017), SYG video captures of falls were not provided on a regular basis to facility staff review. During a second period (January 2018 to April 2018), video captures were delivered to facility staff on a regular weekly basis. During the third period (May 2018 to August 2018), real-time notification (RTN) of any fall was provided to facility staff. Two digital markers (TUA, TOG) were automatically measured and compared between the baseline period (first 2 months) and the RTN period (last 4 months). The total number of falls including those happening outside of the bedroom (such as common areas and bathrooms) was separately reported by facility staff.ResultsA total of 436 falls were recorded in 66 participants suffering from Alzheimer disease or related dementias (mean age 87 years; minimum 65, maximum 104 years). Over 80% of the falls happened in bedrooms, with two-thirds occurring overnight (8 PM to 8 AM). While only 8.1% (22/272) of falls were scored as moderate or severe, fallers were not able to stand up alone in 97.6% (247/253) of the cases. Reductions of 28.3 (CI 19.6-37.1) minutes in TUA and 29.6 (CI 20.3-38.9) minutes in TOG were observed between the baseline and RTN periods. The proportion of fallers with TOG &gt;1 hour fell from 31% (8/26; baseline) to zero events (RTN period). During the RTN period, 76.6% (108/141) of fallers received human staff assistance in less than 10 minutes, and 55.3% (78/141) of them spent less than 10 minutes on the ground.ConclusionsSYG technology is capable of reducing TOG and TUA while efficiently covering the area (bedroom) and time zone (nighttime) that are at highest risk. After 6 months of SYG monitoring, TOG was reduced by a factor of 3. The drastic reduction of TOG is likely to decrease secondary comorbid complications, improve post-fall prognosis, and reduce health care costs

Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis

BackgroundDementia is defined as a cognitive decline that affects functional status. Longitudinal ageing surveys often lack a clinical diagnosis of dementia though measure cognition and daily function over time. We used unsupervised machine learning and longitudinal data to identify transition to probable dementia.MethodsMultiple Factor Analysis was applied to longitudinal function and cognitive data of 15,278 baseline participants (aged 50&nbsp;years and more) from the Survey of Health, Ageing, and Retirement in Europe (SHARE) (waves 1, 2 and 4-7, between 2004 and 2017). Hierarchical Clustering on Principal Components discriminated three clusters at each wave. We estimated probable or "Likely Dementia" prevalence by sex and age, and assessed whether dementia risk factors increased the risk of being assigned probable dementia status using multistate models. Next, we compared the "Likely Dementia" cluster with self-reported dementia status and replicated our findings in the English Longitudinal Study of Ageing (ELSA) cohort (waves 1-9, between 2002 and 2019, 7840 participants at baseline).ResultsOur algorithm identified a higher number of probable dementia cases compared with self-reported cases and showed good discriminative power across all waves (AUC ranged from 0.754 [0.722-0.787] to 0.830 [0.800-0.861]). "Likely Dementia" status was more prevalent in older people, displayed a 2:1 female/male ratio, and was associated with nine factors that increased risk of transition to dementia: low education, hearing loss, hypertension, drinking, smoking, depression, social isolation, physical inactivity, diabetes, and obesity. Results were replicated in ELSA cohort with good accuracy.ConclusionsMachine learning clustering can be used to study dementia determinants and outcomes in longitudinal population ageing surveys in which dementia clinical diagnosis is lacking

Identification of high likelihood of dementia in population-based surveys using unsupervised clustering: a longitudinal analysis

Abstract Background Dementia is defined as a cognitive decline that affects functional status. Longitudinal ageing surveys often lack a clinical diagnosis of dementia though measure cognition and daily function over time. We used unsupervised machine learning and longitudinal data to identify transition to probable dementia. Methods Multiple Factor Analysis was applied to longitudinal function and cognitive data of 15,278 baseline participants (aged 50 years and more) from the Survey of Health, Ageing, and Retirement in Europe (SHARE) (waves 1, 2 and 4–7, between 2004 and 2017). Hierarchical Clustering on Principal Components discriminated three clusters at each wave. We estimated probable or “Likely Dementia” prevalence by sex and age, and assessed whether dementia risk factors increased the risk of being assigned probable dementia status using multistate models. Next, we compared the “Likely Dementia” cluster with self-reported dementia status and replicated our findings in the English Longitudinal Study of Ageing (ELSA) cohort (waves 1–9, between 2002 and 2019, 7840 participants at baseline). Results Our algorithm identified a higher number of probable dementia cases compared with self-reported cases and showed good discriminative power across all waves (AUC ranged from 0.754 [0.722–0.787] to 0.830 [0.800–0.861]). “Likely Dementia” status was more prevalent in older people, displayed a 2:1 female/male ratio, and was associated with nine factors that increased risk of transition to dementia: low education, hearing loss, hypertension, drinking, smoking, depression, social isolation, physical inactivity, diabetes, and obesity. Results were replicated in ELSA cohort with good accuracy. Conclusions Machine learning clustering can be used to study dementia determinants and outcomes in longitudinal population ageing surveys in which dementia clinical diagnosis is lacking

Relationships between neuropsychological impairments and functional outcome eight years after severe traumatic brain injury: Results from the PariS-TBI study

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A Detailed Overview of Long-Term Outcomes in Severe Traumatic Brain Injury Eight Years Post-injury

International audienceBackground and aims: Severe traumatic brain injury is a leading cause of acquired persistent disabilities, and represents an important health and economic burden. However, the determinants of long-term outcome have rarely been systematically studied in a prospective longitudinal study of a homogeneous group of patients suffering exclusively from severe TBIMethods: Prospective observational study of an inception cohort of adult patients with severe traumatic brain injury in the Parisian area (PariS-TBI). Outcome was assessed with face-to-face interview 8 years after Traumatic Brain Injury, focusing on impairments, activity limitations, and participation restriction.Results: Five hundred and four patients were included between 2005 and 2007. At 8-year follow-up, 261 patients were deceased, 128 were lost to follow-up, 22 refused to participate, and 86 were finally evaluated. Age, gender, initial injury severity did not significantly differ between evaluated patients and lost to follow-up, but the latter were more frequently students or unemployed. Mean age was 41.9 (SD 13.6), 79% were male, median initial Glasgow Coma Scale Score was 6. The most frequent somatic complaints concerned balance (47.5%), motricity (31%), and headaches (36%), but these were less frequent than cognitive complaints (Memory 71%, Slowness 68%, Concentration 67%). According to the Hospital Anxiety and Depression Scale (HADS), 25 % had a score >8 for anxiety and 23.7% for depression. According to the Extended Glasgow Outcome Scale, 19.8% remained severely disabled, 46.5% moderately disabled, 33.7% had a good recovery. Older age, longer education duration, lower functional status upon intensive care discharge, and more severe 8-year dysexecutive problems were significantly associated with a lower Extended Glasgow Outcome Scale score in multivariable analysis. At 8 years, 48.7% of patients were employed in a productive job. Of those, 38% declared a salary loss since traumatic brain injury. Unemployment was significantly associated with lower 1-year GOSE score and more severe 8-year dysexecutive problems.Conclusions: These results from an inception cohort study highlight the fact that long-term outcome after severe TBI is determined by a complex combination of injury-related, demographic and neuropsychological factors. Long after the injury, persisting impairments still interfere with social integration, and participation

Long-term follow-up of neurodegenerative phenomenon in severe traumatic brain injury using MRI

International audienceBackground: . Traumatic brain injury (TBI) lesions are known to evolve over time, but the duration and consequences of cerebral remodelling are unclear. Degenerative mechanisms occurring in the chronic phase after TBI could constitute “tertiary” lesions related to the neurological outcome.Objective: . The objective of this prospective study of severe TBI was to longitudinally evaluate the volume of white and grey matter structures and white matter integrity with 2 time-point multimodal MRI.Methods: . Longitudinal MRI follow-up was obtained for 11 healthy controls (HCs) and 22 individuals with TBI (mean [SD] 60 [15] months after injury) along with neuropsychological assessments. TBI individuals were classified in the “favourable” recovery group (Glasgow Outcome Scale Extended [GOSE] 6–8) and “unfavourable” recovery group (GOSE 3–5) at 5 years. Variation in brain volumes (3D T1-weighted image) and white matter integrity (diffusion tensor imaging [DTI]) were quantitatively assessed over time and used to predict neurological outcome.Results:. TBI individuals showed a marked decrease in volumes of whole white matter (median -11.4% [interquartile range -5.8; -14.6]; p < 0.001) and deep grey nuclear structures (-17.1% [-10.6; -20.5]; p < 0.001). HCs did not show any significant change over the same time period. Median volumetric loss in several brain regions was higher with GOSE 3–5 than 6–8. These lesions were associated with lower fractional anisotropy and higher mean diffusivity at baseline. Volumetric variations were positively correlated with normalized fractional anisotropy and negatively with normalized mean diffusivity at baseline and follow-up. A computed predictive model with baseline DTI showed good accuracy to predict neurological outcome (area under the receiver operating characteristic curve 0.82 [95% confidence interval 0.81–0.83])Conclusions: . We characterised the striking atrophy of deep brain structures after severe TBI. DTI imaging in the subacute phase can predict the occurrence and localization of these tertiary lesions as well as long-term neurological outcome

Disability and health-related quality-of-life 4 years after a severe traumatic brain injury: A structural equation modelling analysis

<p><i>Objectives</i>: To assess predictors and indicators of disability and quality-of-life 4 years after severe traumatic brain injury (TBI), using structural equation modelling (SEM).</p> <p><i>Methods</i>: The PariS-TBI study is a longitudinal multi-centre inception cohort study of 504 patients with severe TBI. Among 245 survivors, 147 patients were evaluated upon 4-year follow-up, and 85 completed the full assessment. Two outcome measures were analysed separately using SEM: the Glasgow Outcome Scale-extended (GOS-E), to measure disability, and the QOLIBRI, to assess quality-of-life. Four groups of variables were entered in the model: demographics; injury severity; mood and cognitive impairments; somatic impairments.</p> <p><i>Results</i>: The GOS-E was directly significantly related to mood and cognition, injury severity, and somatic impairments. Age and education had an indirect effect, mediated by mood/cognition or somatic deficiencies. In contrast, the only direct predictor of QOLIBRI was mood and cognition. Age and somatic impairments had an indirect influence on the QOLIBRI.</p> <p><i>Conclusion</i>: Although this study should be considered as explorative, it suggests that disability and quality-of-life were directly influenced by different factors. While disability appeared to result from an interaction of a wide range of factors, quality-of-life was solely directly related to psycho-cognitive factors.</p