Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient‐Reported Measures

Background Myasthenia 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. Methods In 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. Results The 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. Interpretation Multimodal 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 Registration The study was registered under the German Clinical Trial Registry (DRKS00029907)