Barts-MS rose-tinted-odometer: ★
Mrs P came for her annual follow-up appointment. She has had MS for 12 years, was NEDA on dimethyl fumarate and had an EDSS of 4.0. She had fatiguable foot drop on the right; after walking for about 20 minutes her right leg would start dragging and her foot would catch on uneven surfaces. Does this symptom sound familiar?
Fatiguable foot drop in MS is very common and indicates that the pyramidal nerve fibre tract or motor pathway, in Mrs P case to the right leg, has lost reserve and is vulnerable to slow degeneration of the sort that is associated with worsening MS or secondary progressive MS.
Towards the end of the consultation, almost in passing, Mrs P told me that during the summer, whilst on holiday she had tripped and twisted her right ankle and had fractured her fibula (one of the long bones in the lower leg that helps support the ankle). Fortunately, the fracture was mild and not unstable and she was managed with a soft foot splint. Although the fracture had healed her foot was still swollen and stiff. Interestingly, she had not been referred to physiotherapy for an exercise programme to advise her on a sensible rehabilitation programme. I said it was never too late to start rehabilitation.
As a reader of this blog, you must be aware that pwMS are at increased risk of falls and fractures, which is one of the most common causes of unscheduled or emergency hospital admissions for pwMS. A fractured neck of femur or femur is one of the reasons why pwMS end-up in a wheelchair and never mobilise again.
In a Barts-MS audit Dr Ruh several years ago we showed that the best predictor of falls was the need or potential need for a walking aid, i.e. a foot splint, a foot-up, FES device (functional electric nerve stimulator), walking stick or sticks, walking frame etc.
Another issue that is closely related to falls is bone health. PwMS are more likely to have thin bones (osteopaenia) and osteoporosis for multiple reasons, which also increases the risk of fractures, which is why we recommend bone density or DEXA scans in all of our patients at risk of falls. In addition, to a bone health screen, we try and get these vulnerable patients into a falls prevention clinic. The latter doesn’t always work as there is a shortage of physiotherapists in the NHS and the wait for falls clinics can be many months.
We tried to address this problem by setting up a group falls prevention clinic a few years ago, but because of a lack of funding and a shortage of physiotherapy time within our NHS trust, we couldn’t make the clinic sustainable. This is a great pity as every year several patients under my care, such as Mrs P, fall and have fractures, which impacts on their quality of life and their MS. I often ask how many of these predictable fractures could have been prevented?
The study below shows that you can use technology, i.e. sensors to detect falls. A system like this could be embedded into a well-designed self-management or self-prevention application to tackle falls prevention and bone health at a population level. This is yet another example of technology showing great potential to improve preventive medicine, but as usual, there is no clear path on how to incorporate this type of innovation into routine clinical care. This is why my recent post on rethinking healthcare is so timely. As a MS HCP, I want an easy and well-oiled or frictionless system for testing these type of innovations in the NHS. Is that too much to ask for?
Do you use, or potentially need, a walking aid? Have you had any falls or near falls (trips)? If yes, you need to have the state of your bone health assessed and referred to a falls prevention clinic. Believe me when I say bone fractures are unpleasant; they are. I am typing this post supine with a painful fractured pelvis and a fractured cervical spine and a foggy head from the analgesics I am on to manage my pain. Although fractures heal they can leave behind residual deficits that could impact on your quality of life.
If any of you are having falls and have been on a falls prevention programme please feel free to share your experience.
Mosquera-Lopez et al. Automated Detection of Real-World Falls: Modeled from People with Multiple Sclerosis. J Biomed Health Inform. 2020 Nov 27;PP. doi: 10.1109/JBHI.2020.3041035.
Falls are a major health problem with one in three people over the age of 65 falling each year, oftentimes causing hip fractures, disability, reduced mobility, hospitalization and death. A major limitation in fall detection algorithm development is an absence of real-world falls data. Fall detection algorithms are typically trained on simulated fall data that contain a well-balanced number of examples of falls and activities of daily living. However, real-world falls occur infrequently, making them difficult to capture and causing severe data imbalance. People with multiple sclerosis (MS) fall frequently, and their risk of falling increases with disease progression. Because of their high fall incidence, people with MS provide an ideal model for studying falls. This paper describes the development of a context-aware fall detection system based on inertial sensors and time of flight sensors that is robust to imbalance, which is trained and evaluated on real-world falls in people with MS. The algorithm uses an auto-encoder that detects fall candidates using reconstruction error of accelerometer signals followed by a hyper-ensemble of balanced random forests trained using both acceleration and movement features. On a clinical dataset obtained from 25 people with MS monitored over eight weeks during free-living conditions, 54 falls were observed and our system achieved a sensitivity of 92.14%, and false-positive rate of 0.65 false alarms per day.
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