Dereli Onur, Behringer Jochen, Berthele Achim, Hapfelmeier Alexander, Hemmer Bernhard, Gasperi Christiane
Department of Neurology, TUM School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.
AOK Bayern, Munich, Germany.
Eur J Neurol. 2025 Apr;32(4):e70124. doi: 10.1111/ene.70124.
Identifying predictors for disability progression is crucial for managing multiple sclerosis (MS). This study aims to explore levels of disability and informative factors for disability progression in people with MS (PwMS) using healthcare data without detailed clinical information.
We conducted a case-control/cohort study on data from Bavaria's largest health insurance organization. The dataset included records of assistive devices, nursing care, sick leaves, rehabilitation, drug therapies, and diagnoses for individuals with MS, Crohn's disease (CD), rheumatoid arthritis (RA), and controls (CTR) without these diseases. We used generalized linear models to compare healthcare service utilization between MS and other cohorts. A gradient-boosting algorithm identified informative healthcare-related factors associated with disability progression in PwMS, defined by increased nursing care utilization.
PwMS (N = 11,961) demonstrated higher healthcare utilization than CD (N = 21,884), RA (N = 105,450), and CTR (N = 82,677) groups, even at young ages. Besides expected risk factors like age, smoking, diabetes, and psychiatric disorders, the prediction algorithm revealed that PwMS with specific gynecological disorders, upper tract infections, asthma, and thyroiditis were less likely to need higher levels of nursing care.
Leveraging healthcare data allows for an objective assessment of disability in PwMS and can identify informative factors for disability progression. Our approach can be applied to studies on disease progression in large cohorts without detailed clinical data and can be adapted to other diseases, disability measures, and healthcare systems. Higher utilization of healthcare resources even at young ages revealed an unmet need for improved treatment and management strategies for young adults with MS.
识别残疾进展的预测因素对于多发性硬化症（MS）的管理至关重要。本研究旨在利用缺乏详细临床信息的医疗数据，探索MS患者（PwMS）的残疾水平及残疾进展的相关信息因素。
我们对巴伐利亚最大的健康保险机构的数据进行了病例对照/队列研究。数据集包括MS、克罗恩病（CD）、类风湿关节炎（RA）患者以及无这些疾病的对照人群（CTR）的辅助设备、护理、病假、康复、药物治疗和诊断记录。我们使用广义线性模型比较MS与其他队列之间的医疗服务利用情况。一种梯度提升算法确定了与PwMS残疾进展相关的信息性医疗相关因素，残疾进展由护理利用增加来定义。
即使在年轻时，PwMS组（N = 11,961）的医疗利用率也高于CD组（N = 21,884）、RA组（N = 105,450）和CTR组（N = 82,677）。除了年龄、吸烟、糖尿病和精神疾病等预期风险因素外，预测算法显示，患有特定妇科疾病、上呼吸道感染、哮喘和甲状腺炎的PwMS患者不太可能需要更高水平的护理。
利用医疗数据能够客观评估PwMS的残疾情况，并可识别残疾进展的信息因素。我们的方法可应用于无详细临床数据的大型队列疾病进展研究，并可适用于其他疾病、残疾测量和医疗系统。即使在年轻时医疗资源利用率较高，也表明患有MS的年轻人对改善治疗和管理策略的需求未得到满足。
