{"doi":"10.1002/alz.13565","title":"Predicting clinical progression trajectories of early Alzheimer's disease patients","abstract":"BACKGROUND: Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring. METHODS: Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures. RESULTS: to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%. DISCUSSION: Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.","journal":"Alzheimer s & Dementia","year":2023,"id":326003,"datarank":0.8064619623190679,"base_score":3.4339872044851463,"endowment":3.4339872044851463,"self_citation_contribution":0.515098080672772,"citation_network_contribution":0.29136388164629595,"self_endowment_contribution":0.515098080672772,"citer_contribution":0.29136388164629595,"corpus_percentile":null,"corpus_rank":null,"citation_count":30,"citer_count":22,"citers_with_citation_signal":10,"citers_with_endowment":10,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9021,"is_data_producer":true,"deposit_databanks":{"ClinicalTrials.gov":["NCT02956486","NCT03036280","NCT01767311"]},"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":416713,"name":"Yuanqing Ye","orcid":"0000-0001-5708-8961","position":1,"is_corresponding":false},{"id":1044402,"name":"Arnaud Charil","orcid":"0000-0002-3437-1710","position":2,"is_corresponding":false},{"id":1044858,"name":"Erica Andreozzi","orcid":null,"position":3,"is_corresponding":false},{"id":66316,"name":"Pallavi Sachdev","orcid":null,"position":4,"is_corresponding":false},{"id":417937,"name":"Daniel A. Llano","orcid":"0000-0003-0933-1837","position":5,"is_corresponding":false},{"id":51246,"name":"Lü Tian","orcid":"0000-0002-5893-0169","position":6,"is_corresponding":false},{"id":1044403,"name":"Liang Zhu","orcid":"0000-0002-1970-6914","position":7,"is_corresponding":false},{"id":282617,"name":"Harald Hampel","orcid":"0000-0003-0894-8982","position":8,"is_corresponding":false},{"id":842885,"name":"Lynn D. Kramer","orcid":null,"position":9,"is_corresponding":false},{"id":67374,"name":"Shobha Dhadda","orcid":null,"position":10,"is_corresponding":false},{"id":67390,"name":"Michael C. Irizarry","orcid":null,"position":11,"is_corresponding":false},{"id":872123,"name":"for the Alzheimer's Disease Neuroimaging Initiative (ADNI)","orcid":null,"position":12,"is_corresponding":false},{"id":36366,"name":"Viswanath Devanarayan","orcid":"0000-0003-2059-9252","position":0,"is_corresponding":true}],"reference_count":70,"raw_metadata":null,"created_at":"2026-07-19T01:08:28.266998Z","pmid":"38087949","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}