{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:29:06Z","timestamp":1779294546617,"version":"3.51.4"},"reference-count":48,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,7,28]],"date-time":"2025-07-28T00:00:00Z","timestamp":1753660800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Muscle and Nerve"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Predicting mortality in Amyotrophic Lateral Sclerosis (ALS) guides personalized care and clinical trial optimization. Existing statistical and machine learning models often rely on baseline or diagnosis visit data, assume fixed predictor\u2010survival relationships, lack validation in non\u2010Western populations, and depend on features like genetic tests and imaging not routinely available. This study developed ALS mortality prediction models that address these limitations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We trained Royston\u2010Parmar and eXtreme Gradient Boosting models on the PRO\u2010ACT database for 6\u2010 and 12\u2010month mortality predictions. Each visit was labeled positive (for death) if death occurred within 6 or 12\u2009months, negative if survival was confirmed beyond that, and excluded if follow\u2010up was insufficient, assuming patients were alive up to their last recorded visit. Models were validated on independent datasets from the North American Celecoxib trial and a Singapore ALS clinic population. Feature importance and the impact of reducing predictors on performance were evaluated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Models predicted mortality from any clinical visit with area under the curve (AUC) of 0.768\u20130.819, rising to 0.865 for 12\u2010month prediction using 3\u2010month windows. Albumin was the top predictor, reflecting nutritional and inflammatory status. Other key predictors included ALS Functional Rating Scale\u2010Revised slope, limb onset, absolute basophil count, forced vital capacity, bicarbonate, body mass index, and respiratory rate. Models maintained robust performance on the independent datasets and after reducing inputs to seven key predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>These visit\u2010agnostic models, validated across diverse populations, identify key prognostic features and demonstrate the potential of predictive modeling to enhance ALS care and trial design.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mus.28487","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T05:35:36Z","timestamp":1753767336000},"page":"653-661","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Amyotrophic Lateral Sclerosis Mortality With Machine Learning in Diverse Patient Databases"],"prefix":"10.1002","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8394-4338","authenticated-orcid":false,"given":"Ling","family":"Guo","sequence":"first","affiliation":[{"name":"Institute for Infocomm Research (I<sup>2<\/sup>R) Agency for Science, Technology and Research (A*STAR)  Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6838-4661","authenticated-orcid":false,"given":"Ian Qian","family":"Xu","sequence":"additional","affiliation":[{"name":"Duke\u2010NUS Medical School  Singapore"},{"name":"National Neuroscience Institute  Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sonakshi","family":"Nag","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I<sup>2<\/sup>R) Agency for Science, Technology and Research (A*STAR)  Singapore"},{"name":"National Neuroscience Institute  Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"Centre for Quantitative Medicine, Duke\u2010NUS  Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josiah","family":"Chai","sequence":"additional","affiliation":[{"name":"National Neuroscience Institute  Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zachary","family":"Simmons","sequence":"additional","affiliation":[{"name":"Department of Neurology Pennsylvania State University  Hershey Pennsylvania USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Savitha","family":"Ramasamy","sequence":"additional","affiliation":[{"name":"Institute for Infocomm Research (I<sup>2<\/sup>R) Agency for Science, Technology and Research (A*STAR)  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