Using Clinically Pertinent Neuromuscular Function Assessments to Predict Falls in Multiple Sclerosis

Laurits Taul Madsen, a PhD candidate at Aarhus University, discussed the use of lower extremity function assessments to characterize patients with MS at risk for future falls.

Laurits Taul Madsen, PhD-C

Laurits Taul Madsen, PhD-C

In recent years, assessments such as Fmax and the rate of force development (RFD) have been shown to accurately help discriminate fallers from non-fallers. Interestingly, RFD has been argued to be a superior measure to Fmax in predicting fallers. To investigator whether neuromuscular function can discriminate fallers from non-fallers in patients with multiple sclerosis (MS), a disease group that commonly experiences falls, lead investigator Laurits Taul Madsen, PhD-C, collected a 53-patient sample, classified as either non-fallers (n = 24), one-time fallers (n = 8), or recurrent fallers (n = 21).

Presented at the 2022 European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) Congress, October 26-28, in Amsterdam, Netherlands, knee extensor neuromuscular function—including Fmax, RFD within 50 ms (RFD50) and 200 ms (RFD200) relative for force onset. Compared with non-fallers, impaired neuromuscular function was observed in both one-time fallers (RFD50, –3.26 [95% CI, –7.08 to 0.57], Nm/s/kg, –36%; RFD200, –0.75 [95% CI, –2.66 to 1.16], Nm/s/kg, –13%; Fmax, –0.31 [95% CI, –0.79 to 0.18], Nm/s/kg, –15%) and recurrent fallers (RFD50, –5.65 [95% CI, –8.45 to –.85], Nm/s/kg, –62%; RFD200, –2.22 [95% CI, –3.62 to –0.83] Nm/s/kg, –37%; MVC -0.44 (CI -0.80;-0.08), -21%).

At ECTRIMS 2022, NeurologyLive® sat down with Taul Madsen, a PhD candidate at Aarhus University, to discuss his presentation in further detail. He provided background on the commonality and severity of falls in MS, the need to expand the research in this area, and why neuromuscular function can serve as a beneficial tool going forward.

NeurologyLive®: What was the idea behind this study?

Laurits Taul Madsen, PhD-C: The important aspect is to remember that it's not only neuromuscular function that's important for predicting falls or predicting potential fallers. What we're looking at is kind of a pixel of the whole picture. We've been looking at neuromuscular function as a potential risk factor, and it can be measured in different ways. You can look at the maximum muscle strength, or as we do, look at rate of force development. If you're a person that's about to fall, the important thing is not how strong you are, the important thing is how quickly you can produce enough force to counteract the balance perturbation that you're experiencing.

Does this research area not gain the attention it deserves?

I think this does, because this is the first study investigating greater force development. We know from other neurodegenerative populations that rate of force development is a risk factor for falls, so I think this deserves more attention. What we have done is only a cross sectional study of 53 participants, so this is a start to this topic. But yeah, we need to look at the longitudinal data and bigger studies.

What were your main findings?

We found that rate of force development is the best measure to predict potential fallers compared with the maximum muscle strength. In this particular study, we didn't look at balance, fear of walking, or cognitive impairment, which are known to be important risk factors. We only looked at the neuromuscular function, and there we saw the rate of force development at 50 milliseconds—or the very early force you have—is the most important aspect in discriminating potential false.

How often is neuromuscular function being assessed in clinic?

It's not being measured that much. When we do it in research, we measured it with gold standard equipment, which is quite expensive. But in clinical practice, it is possible to measure it quite easily. If you have a small device and the chair, you can measure it. It can be easily transduced into clinical practice. It’s very useful and very straightforward.

Is there anything else you’d like to see with this dataset?

The data set we have is cross sectional, and that's a big limiting factor. We need to look at the longitudinal part. We collected the falls on a retrospective manner, so based on people's memory. We need to do it in a longitudinal way so that we follow people over 1 to 5 years and look at how it develops with falls and the rate of force development. And even further, to try to improve the rate of force development, and see, do we then prevent or reduce the number of falls?

How common of an issue is falls in patients with MS? Are there patients who may be predisposed?

It is quite a big issue. I think 30% of the population with MS is above 65 years, and we know that even if you don't have MS, you have a high risk of falling when you grow. And then when you have MS, the increase is just rising. It’s a big problem that's not being taken enough into account.

What types of research is needed to expand on these findings?

In general, we could start by every time we have a randomized control trial, to collect data on falls, because it's quite easy. It's a simple question, “Have you fallen within the past 2 weeks, 4 weeks, or so?” Then we can start to look at, OK, if we improve their aerobic capacity, improve their strength, and the rate of force development, do we then see a picture of someone falling less. Again, bear in mind that that we are only looking at a small pixel of the whole picture. Because a thing like balance is probably very important, it’s just so multifactorial. Is it the strength? Is it a cognitive thing that causes you to have impaired balance?

Transcript edited for clarity. Click here for more coverage of ECTRIMS 2022.

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