ArticleMultiple sclerosis (Houndmills, Basingstoke, England)2021
Accurate classification of secondary progression in multiple sclerosis using a decision tree.
Article in Multiple sclerosis (Houndmills, Basingstoke, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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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.
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Who cites it
13 citing papers in PubMed, 26 citations in OpenAlex.
- Artificial neural network-based prediction of multiple sclerosis using blood-based metabolomics data.Multiple sclerosis and related disorders · 2024Article
- Validation of a Set of Clinical Criteria for the Diagnosis of Secondary Progressive Multiple Sclerosis.Brain sciences · 2024Article
- A Clinical Care Algorithm for Detecting Progression in Multiple Sclerosis: RetratEMos Project.Cureus · 2024Article
- The Faces of "Too Late"-A Surprisingly Progressive Cohort of "Stable" Relapsing Remitting Multiple Sclerosis Patients.Medicina (Kaunas, Lithuania) · 2024Article
- Patterns and predictors of multiple sclerosis phenotype transition.Brain communications · 2024Article
- The risk of secondary progressive multiple sclerosis is geographically determined but modifiable.Brain : a journal of neurology · 2023Article
- The impact of healthcare systems on the clinical diagnosis and disease-modifying treatment usage in relapse-onset multiple sclerosis: a real-world perspective in five registries across Europe.Therapeutic advances in neurological disorders · 2023Article
- Ensemble machine learning identifies genetic loci associated with future worsening of disability in people with multiple sclerosis.Scientific reports · 2022Article
- Inhibition of colony stimulating factor-1 receptor (CSF-1R) as a potential therapeutic strategy for neurodegenerative diseases: opportunities and challenges.Cellular and molecular life sciences : CMLS · 2022Review
- Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.Frontiers in public health · 2022Article
- Charting a global research strategy for progressive MS-An international progressive MS Alliance proposal.Multiple sclerosis (Houndmills, Basingstoke, England) · 2022Review
- Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity.Multiple sclerosis journal - experimental, translational and clinicalArticle
- Proportion and characteristics of secondary progressive multiple sclerosis in five European registries using objective classifiers.Multiple sclerosis journal - experimental, translational and clinicalArticle
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
Abstract
backgroundThe absence of reliable imaging or biological markers of phenotype transition in multiple sclerosis (MS) makes assignment of current phenotype status difficult.
objectiveThe authors sought to determine whether clinical information can be used to accurately assign current disease phenotypes.
methodsData from the clinical visits of 14,387 MS patients in Sweden were collected. Classifying algorithms based on several demographic and clinical factors were examined. Results obtained from the best classifier when predicting neurologist recorded disease classification were replicated in an independent cohort from British Columbia and were compared to a previously published algorithm and clinical judgment of three neurologists.
resultsA decision tree (the classifier) containing only most recently available expanded disability scale status score and age obtained 89.3% (95% confidence intervals (CIs): 88.8-89.8) classification accuracy, defined as concordance with the latest reported status. Validation in the independent cohort resulted in 82.0% (95% CI: 81.0-83.1) accuracy. A previously published classification algorithm with slight modifications achieved 77.8% (95% CI: 77.1-78.4) accuracy. With complete patient history of 100 patients, three neurologists obtained 84.3% accuracy compared with 85% for the classifier using the same data.
conclusionThe classifier can be used to standardize definitions of disease phenotype across different cohorts. Clinically, this model could assist neurologists by providing additional information.
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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 OpenQuestion graph.