2021 CMSC Annual Meeting

Identifying and Characterizing Fatigue in Patients Diagnosed with Multiple Sclerosis on Disease-Modifying Therapies Using Real World Evidence

SXM06

Background:
Fatigue is a common symptom in patients with Multiple Sclerosis (MS). Payor databases represent an important source of real world evidence (RWE) on fatigue in MS, although the literature on their use for this purpose is limited. We identified patients with likely MS-related fatigue in a claims analysis to help identify gaps in routine clinical practice.
Objectives:
To identify and characterize fatigue through specific codes for fatigue and proxies of fatigue using a large retrospective claims dataset in patients with the diagnosis of MS.
Methods:
Using the Optum non-affiliate multi-payer database (01JAN2015-31DEC2019), data from patients aged ?18 with ?2 diagnoses of MS, who received disease modifying therapies (DMTs) after MS diagnosis were analyzed. The date of the first prescription for a DMT is considered the index date. The baseline and follow-up periods were 12 months before and after the index date, respectively. Fatigue was analyzed using three definitions: (A) ICD9/10 codes for fatigue; and two proxies defined as (B) ICD9/10 or >2 prescriptions for stimulant drugs or a sleep study; or (C) ICD9/10 or >2 prescriptions for stimulant drugs or >2 prescriptions for sleep agents or a sleep study.
Results:
The claims database contained 4077 patients meeting the MS criterium. The mean age was 49 years, 75% were female, and the mean Charlson co-morbidity index (CCI) was 1.13. At baseline, fatigue were identified in 26%, 35%, and 48% of patients, using fatigue definitions A, B, and C, respectively. Using the fatigue definition C, 42% of patients who did not have fatigue at baseline developed fatigue during follow-up, and 87% of patients who had fatigue at baseline continue to have fatigue during follow-up. Patients with fatigue at baseline reported greater frequency of comorbidities compared to those without fatigue: depression (16% vs 3%), anxiety (26% vs 10%), cardiovascular diseases (30% vs 24%), gastrointestinal disorders (23% vs 14%), all which were statistically significant.
Conclusions:
Using ICD codes alone likely under-represents the true frequency of fatigue. Definition (C), which included provider interventions that indicated a concern for fatigue, was developed. Increased comorbidities and health care costs were reported in patients with likely fatigue. Thus, this type of identification may not only help clinicians to identify at risk patients for additional services, but may also present opportunities for improved case management.