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Model inputs included baseline clinical features (cognitive function assessments, APOE \u03b54 status, and demographics) and brain magnetic resonance imaging (MRI) measures.<\/jats:p><\/jats:sec><jats:sec><jats:title>RESULTS<\/jats:title><jats:p>The model using clinical features achieved <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> of 0.21 and 0.31 for predicting 2\u2010year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model\u2010based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%.<\/jats:p><\/jats:sec><jats:sec><jats:title>DISCUSSION<\/jats:title><jats:p>Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.<\/jats:p><\/jats:sec>","DOI":"10.1002\/alz.13565","type":"journal-article","created":{"date-parts":[[2023,12,13]],"date-time":"2023-12-13T03:16:19Z","timestamp":1702437379000},"page":"1725-1738","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Predicting clinical progression trajectories of early Alzheimer's disease patients"],"prefix":"10.1002","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2059-9252","authenticated-orcid":false,"given":"Viswanath","family":"Devanarayan","sequence":"first","affiliation":[{"name":"Clinical Evidence Generation Eisai Inc.  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