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Combining clinical and magnetic resonance imaging markers enhances prediction of 12-year employment status in multiple sclerosis patients

Publication at Faculty of Physical Education and Sport, First Faculty of Medicine |
2018

Abstract

Background: Multiple sclerosis (MS) is frequently diagnosed in the most productive years of adulthood and is often associated with worsening employment status. However, reliable predictors of employment status change are lacking.

Objective: To identify early clinical and brain magnetic resonance imaging (MRI) markers of employment status worsening in MS patients at 12-year follow-up. Methods: A total of 145 patients with early relapsing-remitting MS from the original Avonex-Steroids-Azathioprine (ASA) study were included in this prospective, longitudinal, observational cohort study.

Cox models were conducted to identify MRI and clinical predictors (at baseline and during the first 12 months) of worsening employment status (patients either (1) working full-time or part-time with no limitations due to MS and retaining this status during the course of the study, or (2) patients working full-time or part-time with no limitations due to MS and switching to being unemployed or working part-time due to MS). Results: In univariate analysis, brain parenchymal fraction, T1 and T2 lesion volume were the best MRI predictors of worsening employment status over the 12-year follow-up period.

MS duration at baseline (hazard ratio (HR) = 1.10, 95% confidence interval (CI) 1.03-1.18; p = 0.040) was the only significant clinical predictor. Having one extra milliliter of T1 lesion volume was associated with a 53% greater risk of worsening employment status (HR = 1.53, 95% CI 1.16-2.02; p = 0.018).

A brain parenchymal fraction decrease of 1% increased the risk of worsening employment status by 22% (HR = 0.78, 95% CI 0.65-0.95; p = 0.034). Conclusion: Brain atrophy and lesion load were significant predictors of worsening employment status in MS patients.

Using a combination of clinical and MRI markers may improve the early prediction of an employment status change over long-term follow-up.