ArticleCPT: pharmacometrics & systems pharmacology2022
Predicting disease activity in patients with multiple sclerosis: An explainable machine-learning approach in the Mavenclad trials.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 30 citations in OpenAlex.
- Employing deep mutational scanning in theProceedings of the National Academy of Sciences of the United States of America · 2025Article
- AI-Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference.Clinical and translational science · 2025Review
- The role of trustworthy and reliable AI for multiple sclerosis.Frontiers in digital health · 2025Article
- The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases.Pharmaceutics · 2024Review
- Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.Clinical and translational science · 2024Review
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Explainable machine learning prediction of edema adverse events in patients treated with tepotinib.Clinical and translational science · 2024Article
- Artificial Intelligence and Multiple Sclerosis.Current neurology and neuroscience reports · 2024Review
- Observational studies of treatment effectiveness in neurology.Brain : a journal of neurology · 2023Review
- An introduction to causal inference for pharmacometricians.CPT: pharmacometrics & systems pharmacology · 2023Article
- Predicting disease activity in patients with multiple sclerosis: An explainable machine-learning approach in the Mavenclad trials.CPT: pharmacometrics & systems pharmacology · 2022Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple sclerosis (MS) is among the most common autoimmune disabling neurological conditions of young adults and affects more than 2.3 million people worldwide. Predicting future disease activity in patients with MS based on their pathophysiology and current treatment is pivotal to orientate future treatment. In this respect, we used machine learning to predict disease activity status in patients with MS and identify the most predictive covariates of this activity. The analysis is conducted on a pooled population of 1935 patients enrolled in three cladribine tablets clinical trials with different outcomes: relapsing-remitting MS (from CLARITY and CLARITY-Extension trials) and patients experiencing a first demyelinating event (from the ORACLE-MS trial). We applied gradient-boosting (from XgBoost library) and Shapley Additive Explanations (SHAP) methods to identify patients' covariates that predict disease activity 3 and 6 months before their clinical observation, including patient baseline characteristics, longitudinal magnetic resonance imaging readouts, and neurological and laboratory measures. The most predictive covariates for early identification of disease activity in patients were found to be treatment duration, higher number of new combined unique active lesion count, higher number of new T1 hypointense black holes, and higher age-related MS severity score. The outcome of this analysis improves our understanding of the mechanism of onset of disease activity in patients with MS by allowing their early identification in clinical settings and prompting preventive measures, therapeutic interventions, or more frequent patient monitoring.
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