According to a claims-based study published in Neurology and Therapy, Principal Component Analysis (PCA) may be exploited as a methodology for the estimation of multiple sclerosis severity Researchers undertook this study to investigate the possibility of utilizing PCA for estimating multiple sclerosis severity utilizing information from a Japanese claims database.

Utilizing principal component analysis, the multiple sclerosis severity score was developed. From the claims database, extraction of factors linked with functional systems for Expanded Disability Status Scale (EDSS) and greater disease severity (77 procedures, 74 diagnoses, 68 drug prescriptions) was done.

For each patient-year, the score (first principal component [PC1] score) was developed each year from the first diagnosis (excluding year of first diagnosis), on the basis of the first principal component of the incorporated factors. The patient-years were categorized into quartiles on the basis of the PC1 score. Assessment of demographic information and medical status was done.

Overall, 7067 people with multiple sclerosis were incorporated in the database. The highest score group exhibited a higher mean age, reduced percentage of females, and longer mean disease duration from the first diagnosis in comparison with the lowest score group, as shown in Table 1:

Furthermore, the PC1 score of each participant was positively associated with disease duration from diagnosis. The score-linked alterations in demographic features showed consistency with those reported by prior investigation. Taking into consideration the consistency of these results with those of prior research, the score developed by PCA might represent multiple sclerosis severity to some extent. Additional investigation is needed for validating PC1 score with clinical measurement of disability like EDSS.