In patients with primary progressive multiplesclerosis, machine learning can predict progression with reasonable accuracy on the basis of the blood transcriptome. PPMS AAN2023 AANAM
for PPMS. Analyses were conducted only on blood samples from those randomized to placebo, who, like those in the active treatment arm, were evaluated at baseline and at 12-week intervals for more than 2 years.
The peripheral blood samples were subjected to RNA sequencing analysis using commercially available analysis techniques. The prediction model for the disability endpoint was based on data generated from the blood transcriptome of 135 patients of which 53 met the endpoint at 120 weeks. The prediction model for the change in brain morphology was based on the blood transcriptome from 94 patients of which 63 met the endpoint.
Typical of a PPMS trial population, the mean age of the patients was about 44 years. The mean disease duration was about 6 years. The majority of patients had an EDSS score below 5.5 at baseline. The baseline T2 lesion number was approximately 50. Although she did not hear the presentation by Dr. Gurevich, Dr. Farina is enthusiastic about the potential for machine learning to help manage MS through the analysis of the blood transcriptome."I do believe that transcriptomics in peripheral immune cells may become a useful tool for MS diagnosis and prognosis," she said.
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