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More Guidance, Less Variability: Digital EDSS Givers Raters a More Precise, More User-Friendly Tool for MS Assessments

CNS researchers have been using the EDSS (Expanded Disability Status Scale) for years to quantify the severity of disability in patients with multiple sclerosis and other neurological conditions. However, with more than 130 data points, this scale can be cumbersome to complete on paper, leaving it vulnerable to inaccuracies, including interrater and intrarater variability.1

To address this, WCG collaborated with Neurostatus-UHB Ltd., to develop a digital version, the electronic EDSS (eEDSS). This digital scale incorporates proprietary scoring algorithms and edit checks, offering real-time feedback on data inconsistencies. This reduces inaccuracies, avoiding the cost and hassle of additional expert reviews.

Because the eEDSS closely resembles the paper-based form, raters can make a seamless and user-friendly transition. Help icons and other tools are easily accessible to assist raters.

A View From the Trenches

To understand how eEDSS works in the hands of raters, we asked Mark Skeen, M.D, to share his experiences. His responses have been edited for space.

Q: What are the current challenges to capturing data in EDSS?

A: Most, if not all, of our outcome assessments in multiple sclerosis are incomplete, or they only capture certain elements.

Because of that, any variability that we have in each individual outcome measure contributes to the noise in the signal of trying to capture the performance of patients with MS and how that relates in the setting of a clinical trial.

So if we look at the EDSS, our standard clinical trial outcome measure, there can be a lot of noise in the signal created by the nuanced differences in how different raters score.

Whenever we can cause the scoring to be done exactly the same way each visit by each evaluator, we can reduce variability in outcome assessments.

Q. How does WCG’s new eEDSS solution overcome some of these challenges?

A: There are many advantages to the electronic capture of outcome assessments. If we look at the EDSS specifically, it’s basically a complicated scoring system for the neurologic examination that all neurologists do. But in the process, there are nuances in how responses are recorded and stored. As someone who’s been an EDSS rater, I would frequently have to go back to the key to, for instance, look up the difference between a 1 and a 2 in a particular subscore assessment. In eEDSS, those keys are provided right within the software. I can click a button and see the difference between a 1 and a 2. I don’t have to carry a booklet around with me.

It also collects what I call “epidata.” For example, the eEDSS captures exactly how fast an evaluator conducted various parts of the evaluation, allowing us to compare evaluators with each other. As a result, we can collect quite a bit of data on how things are done at different sites — how trials are conducted and how outcomes are being assessed. This provides valuable insight into evaluator performance and illuminates differences between raters, trials and sites.

Q. How does eEDSS streamline data collection?

A. One of the greatest advantages is the ability to provide immediate feedback on apparent conflicts in scores.

Here’s an example: In the subscore for motor, I may indicate somebody has significant weakness in their right leg, but then in the evaluation of walking I say there’s no problem. Now, there’s conceivably a reason it might be true, but it’s an apparent discrepancy.

In the past, resolving such conflicts could take days. By the time they were brought to our attention, it was hard to remember which patient was involved. It was suboptimal.  With eEDSS, we get  immediate feedback. If it’s an error, the rater can correct it immediately.

More Accuracy, Less Burden

The eEDSS offers a more efficient and accurate method of capturing clinical trial data, resulting in improved data quality, greater consistency, less variability and reduced administrative burden.

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  1. Weinstock-Guttman B, Sormani MP, Repovic P. Predicting Long-term Disability in Multiple Sclerosis: A Narrative Review of Current Evidence and Future Directions. International Journal of MS Care. Published online October 5, 2021. doi:

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