The University of Texas at Austin, Austin, TX, USA.
The University of Texas Health Science Center at Houston, Houston, TX, USA.
Mult Scler Relat Disord. 2020 Nov;46:102539. doi: 10.1016/j.msard.2020.102539. Epub 2020 Sep 28.
BACKGROUND/OBJECTIVE: Patients with multiple sclerosis (MS) tend to have significantly lower health-related quality of life, increased mortality and morbidity, and increased healthcare costs. The lack of a claims-based algorithm to correctly identify disease severity makes targeted selection of the MS patients for specific interventions an important limitation in real-world MS research.
Using the Optum claims dataset (2016 -2018), 11,429 persons with MS and >= 24 months of eligibility were identified. A previously developed claims-based algorithm was employed to categorize MS disease severity (low, moderate, high), using MS symptoms and healthcare utilization. Linear regression analysis was used to determine the relationship between disease severity and total cost, a proxy for disease severity. Flexible parametric models were used to determine the risk of 12-month follow-up MS-related relapses and MS-related hospitalizations among the MS disease severity groups.
The risk of both MS-related relapses and MS-related hospitalizations increased as MS disease severity increased. The risk for MS-related relapses was significantly higher in the moderate (HR = 2.43, 95% CI 1.24 -2.64, P < 0.001) and high (HR = 5.97, 95% CI 5.19-6.83, P < 0.001) disease severity groups compared to the low disease severity group. The same trend is observed concerning MS-related hospitalization risk. Both the moderate (HR = 3.16, 95% CI 2.73-3.63, P> 0.001) and high disease severity (HR =12.70, 95% CI 10.57-15.09, P < 0.001) groups had significantly higher risk of MS-related hospitalization compared to the low disease severity group.
The claims-based disease severity algorithm performed well in explaining the total healthcare cost (excluding DMTs). The algorithm-determined disease severity categorization appears consistent with traditional measures of disease severity (MS relapse and hospitalizations). This claims-based algorithm may be a useful tool in determining MS disease severity in claims data.
背景/目的：多发性硬化症（MS）患者的健康相关生活质量往往显著降低，死亡率和发病率增加，医疗保健费用增加。由于缺乏基于索赔的算法来正确识别疾病严重程度，因此针对特定干预措施对 MS 患者进行有针对性的选择是真实世界 MS 研究中的一个重要限制。
使用 Optum 索赔数据集（2016-2018 年），确定了 11429 名符合条件的多发性硬化症患者（≥24 个月）。使用先前开发的基于索赔的算法，根据 MS 症状和医疗保健利用情况，将 MS 疾病严重程度（低、中、高）进行分类。线性回归分析用于确定疾病严重程度与总费用（疾病严重程度的替代指标）之间的关系。灵活参数模型用于确定 MS 疾病严重程度组中 12 个月随访期间 MS 相关复发和 MS 相关住院的风险。
随着 MS 疾病严重程度的增加，MS 相关复发和 MS 相关住院的风险均增加。中度（HR=2.43，95%CI 1.24-2.64，P<0.001）和高度（HR=5.97，95%CI 5.19-6.83，P<0.001）疾病严重程度组的 MS 相关复发风险显著高于低疾病严重程度组。关于 MS 相关住院风险，也观察到同样的趋势。中度（HR=3.16，95%CI 2.73-3.63，P>0.001）和高度疾病严重程度（HR=12.70，95%CI 10.57-15.09，P<0.001）组与低疾病严重程度组相比，MS 相关住院的风险显著增加。
基于索赔的疾病严重程度算法在解释总医疗保健费用（不包括 DMT）方面表现良好。该算法确定的疾病严重程度分类似乎与疾病严重程度的传统衡量标准（MS 复发和住院）一致。这种基于索赔的算法可能是确定索赔数据中 MS 疾病严重程度的有用工具。
