{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T20:00:33Z","timestamp":1790625633946,"version":"4.1.0"},"reference-count":22,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100024149","name":"UCB Pharma Germany","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100024149","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100018748","name":"Argenx","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100018748","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["553539684"],"award-info":[{"award-number":["553539684"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ann Clin Transl Neurol"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Myasthenia gravis (MG) is a rare disorder characterized by fluctuating muscle weakness with potential life\u2010threatening 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this 12\u2010week randomized controlled study, 30 MG patients recorded symptoms using patient\u2010reported outcome measures (PROMs) and patient\u2010performed measures via a mobile app, alongside data from wearables. MG deterioration was defined as a \u2265\u20093\u2010point worsening in the Quantitative Myasthenia Gravis score, occurrence of MG\u2010related hospitalization or exacerbation. A machine learning linear classifier was trained to predict deterioration and cross\u2010validated. The area under the receiver operator characteristic curve (AUROC) was calculated, accepting 1\u20132 false alarms (FAs) per week.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved the best predictive performance when using all input signals (AUROC 0.85 (95% confidence interval 0.77\u20130.91)) and remained stable across look\u2010back windows of 4\u201310\u2009days. Model sensitivity was 0.65 (0.48\u20130.83) to 0.82 (0.60\u20131.00) (1 and 2 FAs per week, respectively). PROMs reflected worsening symptoms before deterioration; wearables alone showed higher AUROCs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>Multimodal self\u2010monitoring via MyaLink predicted MG deterioration with good performance at acceptable FA rates. This approach may enable earlier clinical interventions of MG worsening.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Trial Registration<\/jats:title>\n                    <jats:p>The study was registered under the German Clinical Trial Registry (DRKS00029907)<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acn3.70257","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T04:46:44Z","timestamp":1763614004000},"page":"714-723","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Prediction of Myasthenia Gravis Worsening: A Machine Learning Algorithm Using Wearables and Patient\u2010Reported Measures"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1514-8385","authenticated-orcid":false,"given":"Maike","family":"Stein","sequence":"first","affiliation":[{"name":"Department of Neurology With Experimental Neurology Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin  Berlin Germany"},{"name":"Neuroscience Clinical Research Center Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin  Berlin Germany"},{"name":"Berlin Institute of Health at Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin  Berlin Germany"},{"name":"Department of Neurology Beth Israel Deaconess Medical Center\/Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoqi","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Neurology Beth Israel Deaconess Medical Center\/Harvard Medical School  Boston Massachusetts 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Neurology With Experimental Neurology Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin  Berlin Germany"},{"name":"Neuroscience Clinical Research Center Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin  Berlin Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"M\u00f6nch","sequence":"additional","affiliation":[{"name":"Institute of Biometry and Clinical Epidemiology Charit\u00e9 \u2013 Universit\u00e4tsmedizin Berlin, Corporate Member of Freie Universit\u00e4t Berlin and Humboldt\u2010Universit\u00e4t zu Berlin  Berlin 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