Evidence map›Paper›PMID 40159595›Full record

ArticleEuropean journal of neurology2025

Insights Into Disability and Disability Progression in People With Multiple Sclerosis Using Large-Scale Healthcare Data.

Onur Dereli, Jochen Behringer, Achim Berthele, Alexander Hapfelmeier, Bernhard Hemmer, Christiane Gasperi

Abstract read
In one paragraph

Article in European journal of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Onur DereliDepartment of Neurology, TUM School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.ORCID 0000-0002-2759-2793
Jochen BehringerAOK Bayern, Munich, Germany.
Achim BertheleDepartment of Neurology, TUM School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.
Alexander HapfelmeierInstitute of AI and Informatics in Medicine, TUM School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.
Bernhard HemmerDepartment of Neurology, TUM School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.ORCID 0000-0001-5985-6784
Christiane GasperiDepartment of Neurology, TUM School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.ORCID 0000-0002-0524-6005

Funding

Bundesministerium für Bildung und Forschung 161L0216
6 · The paper itself

Abstract

backgroundIdentifying 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.

methodsWe 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.

resultsPwMS (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.

conclusionsLeveraging 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.

Indexed as

Disease ProgressionMultiple SclerosisPatient Acceptance of Health CarePersons with DisabilitiesAdultAgedArthritis, RheumatoidCase-Control StudiesCohort StudiesCrohn DiseaseDisability EvaluationFemaleHumansMaleMiddle AgedYoung Adultdisability progressionhealthcare datamachine learningmultiple sclerosisprediction

Identifiers

PMID40159595
PMCPMC11955418

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.