Claims-based algorithm to estimate the Expanded Disability Status Scale for multiple sclerosis in a German health insurance fund: a validation study using patient medical records.

Background: The Expanded Disability Status Scale (EDSS) quantifies disability and measures disease progression in multiple sclerosis (MS), however is not available in administrative claims databases.
Objectives: To develop a claims-based algorithm for deriving EDSS and validate it against a clinical dataset capturing true EDSS values from medical records.
Methods: We built a unique linked dataset combining claims data from the German AOK PLUS sickness fund and medical records from the Multiple Sclerosis Management System 3D (MSDS ^3D ). Data were deterministically linked based on insurance numbers. We used 69 MS-related diagnostic indicators recorded with ICD-10-GM codes within 3 months before and after recorded true EDSS measures to estimate a claims-based EDSS proxy (pEDSS). Predictive performance of the pEDSS was assessed as an eight-fold (EDSS 1.0-7.0, ≥8.0), three-fold (EDSS 1.0-3.0, 4.0-5.0, ≥6.0), and binary classifier (EDSS <6.0, ≥6.0). For each classifier, predictive performance measures were determined, and overall performance was summarized using a macro F1-score. Finally, we implemented the algorithm to determine pEDSS among an overall cohort of patients with MS in AOK PLUS, who were alive and insured 12 months prior to and after index diagnosis.
Results: We recruited 100 people with MS insured by AOK PLUS who had ≥1 EDSS measure in MSDS ^3D between 01/10/2015 and 30/06/2019 (620 measurements overall). Patients had a mean rescaled EDSS of 3.2 and pEDSS of 3.0. The pEDSS deviated from the true EDSS by 1.2 points, resulting in a mean squared error of prediction of 2.6. For the eight-fold classifier, the macro F1-score of 0.25 indicated low overall predictive performance. Broader severity groupings were better performing, with the three-fold and binary classifiers for severe disability achieving a F1-score of 0.68 and 0.84, respectively. In the overall AOK PLUS cohort (3,756 patients, 71.9% female, mean 51.9 years), older patients, patients with progressive forms of MS and those with higher comorbidity burden showed higher pEDSS.
Conclusion: Generally, EDSS was underestimated by the algorithm as mild-to-moderate symptoms were poorly captured in claims across all functional systems. While the proxy-based approach using claims data may not allow for granular description of MS disability, broader severity groupings show good predictive performance.
Competing Interests: EM-L and LC were employees of F. Hoffmann La Roche Ltd. MG and TW were employees of IPAM. EZ was an employee of Cytel Inc. AD has received personal compensation and travel grants from Biogen, Celgene, Janssen, Roche and Sanofi for speaker activity. TZ has received consulting fees, grants, and research support from various pharmaceutical companies e.g., Almirall, Bayer, Biogen, Genzyme, Merck, Novartis, Roche, Sanofi, and Teva. TW has received honoraria from several pharmaceutical/consultancy firms e.g. Novo Nordisk, Roche, Abbvie, Merck, GSK, BMS, Bayer, and Boehringer Ingelheim. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that can be construed as a potential conflict of interest. The authors declare that this study received funding from F. Hoffmann La Roche Ltd. The funder had the following involvement in the study: conception of the study, design, interpretation of the results and writing and revision of the manuscript.
(Copyright © 2023 Muros-Le Rouzic, Ghiani, Zhuleku, Dillenseger, Maywald, Wilke, Ziemssen and Craveiro.)