Trial reportAnnals of clinical and translational neurology2026
Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient-Reported Measures.
Trial report in Annals of clinical and translational neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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.
Who cites it
5 citing papers in PubMed.
- Remote Monitoring in Myasthenia Gravis: Exploring Symptom Variability.Annals of clinical and translational neurology · 2026Trial
- Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient-Reported Measures.Annals of clinical and translational neurology · 2026Trial
- Telemedical communication patterns in myasthenia gravis in a remote monitoring study.Neurological research and practice · 2026Article
- Patient Communication Outside of Visits: Implications for Remote Monitoring and Digital Care Models in Myasthenia Gravis.Muscle & nerve · 2026Article
- Digital assessment in myasthenia gravis: evidence, validation, and a proposed framework for autoimmune heterogeneity, remote monitoring, and clinical endpoints.Frontiers in immunology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundMyasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life-threatening crises. Timely interventions may be delayed by limited access to care and fragmented documentation. Our objective was to develop predictive algorithms for MG deterioration using multimodal telemedicine data.
methodsIn this 12-week randomized controlled study, 30 MG patients recorded symptoms using patient-reported outcome measures (PROMs) and patient-performed measures via a mobile app, alongside data from wearables. MG deterioration was defined as a ≥ 3-point worsening in the Quantitative Myasthenia Gravis score, occurrence of MG-related hospitalization or exacerbation. A machine learning linear classifier was trained to predict deterioration and cross-validated. The area under the receiver operator characteristic curve (AUROC) was calculated, accepting 1-2 false alarms (FAs) per week.
resultsThe model achieved the best predictive performance when using all input signals (AUROC 0.85 (95% confidence interval 0.77-0.91)) and remained stable across look-back windows of 4-10 days. Model sensitivity was 0.65 (0.48-0.83) to 0.82 (0.60-1.00) (1 and 2 FAs per week, respectively). PROMs reflected worsening symptoms before deterioration; wearables alone showed higher AUROCs.
interpretationMultimodal self-monitoring via MyaLink predicted MG deterioration with good performance at acceptable FA rates. This approach may enable earlier clinical interventions of MG worsening.
trial registrationThe study was registered under the German Clinical Trial Registry (DRKS00029907).
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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.