Using the multiple sclerosis resiliency scale to identify psychological distress in persons with multiple sclerosis

Background: The Multiple Sclerosis Resiliency Scale (MSRS) is the first resilience measure that is specific to multiple sclerosis (MS)-related challenges. In order for the MSRS to be a valuable tool for clinicians, it is important to identify what is a meaningful score. As such, this study aimed to examine the MSRS' ability to identify persons with MS experiencing depression or anxiety symptoms, as determined using clinically significant scores on the Hospital Anxiety and Depression Scale (HADS). Methods: Participants (n = 884) were persons with MS who were recruited electronically primarily through the North American Research Committee on MS (NARCOMS). In addition to the MSRS, participants completed the HADS, which was used to categorize them into possible depression and anxiety groups using two criteria from the literature: >8 and >11. Receiver-operating-characteristic (ROC) curves were run to determine the MSRS total and subscale scores' classification accuracies, with optimal scores for detecting possible depression and anxiety cases determined using the Youden index. Results: The MSRS total score's classification accuracy ranged between 86.2% and 92.2% for depression, with scores of 70 and 68 for the >8 and >11 criteria, respectively. For anxiety, the MSRS total score's classification accuracy ranged between 78.1% and 82.8%, with scores of 72 and 71 for the >8 and >11 criteria, respectively. The Emotional and Cognitive Strategies subscale had the strongest classification accuracy of all the subscales. Conclusions: The MSRS can be used to identify persons with MS experiencing mental health difficulties with relatively good classification accuracy, which may help clinicians to triage who needs additional assistance or support.

Automated summary

This study found that the MSRS can relatively accurately identify individuals with MS experiencing mental health difficulties, particularly those who may have symptoms of depression or anxiety, with good classification accuracy. This can assist clinicians in triaging patients who need additional help or support.