Health
Can computers tell patients how their multiple sclerosis is progressing?
Machine learning models can reliably inform clinicians about disability progression in multiple sclerosis, according to a new study published this week in an open access journal. PLOS Digital Health by Edward de Brouwer of the Catholic University of Leuven in Belgium and his colleagues.
Multiple sclerosis (MS) is a chronic progressive autoimmune disease that follows a complex pattern of progression, recovery, and relapse, causing severe disability over time. Over the past decade, the global prevalence of MS has increased by more than 30%. However, few tools are available to predict MS progression and help clinicians and patients make life-planning and treatment decisions.
In the new study, De Brouwer and colleagues used data from 15,240 adults with at least 3 years of MS history who were treated at 146 MS centers in 40 countries. Two years of data from each patient's disease progression were used to train state-of-the-art machine learning models that predicted the likelihood of disease progression in the following months and years. The models were trained and validated using strict clinical guidelines to facilitate the application of the models in clinical practice. Although the performance of individual models varied across patient subgroups, the average area under the receiver operating characteristic curve (ROC-AUC) of the models was 0.71 ± 0.01. The study found that a history of disability progression was a better predictor of future disability progression than treatment history or relapse history.
The authors conclude that the model developed in this study has the potential to significantly enhance planning for people with MS and could be evaluated in clinical impact studies.
De Brouwer added: “Using the clinical histories of over 15,000 patients with multiple sclerosis, we have trained a machine learning model that can reliably predict the likelihood of disability progression over the next two years. The model has broad applicability because it only uses routinely collected clinical variables. Our rigorous benchmarking and external validation confirm the great potential of machine learning models to help patients plan their lives and clinicians optimize treatment strategies.”
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