{"doi":"10.1002/dad2.70136","title":"Automated identification of older adults at risk for cognitive decline","abstract":"INTRODUCTION: Automated models that predict cognitive risk in older adults can aid decisions about which patients to screen in busy primary care settings. METHODS: In this retrospective prediction model development study, we conducted formal cognitive testing on 337 older primary care patients to establish cognitive status. We used up to 5 years of prior discrete-field electronic health record (EHR) data to develop a multivariable prediction model that differentiates patients with impaired versus intact cognition. RESULTS: The final model included seven easily extractable variables with known associations to cognitive decline: age, race, pulse, systolic blood pressure, non-steroidal anti-inflammatory use, history of mood disorder, and family history of neurological disease. The model demonstrated good discrimination of cognitive status (concordance statistic = 0.72). DISCUSSION: The cognitive risk model may be useful clinically to prompt for objective cognitive screening in high-risk patients. The use of common, discrete variables ensures relative ease of implementation in EHRs. Highlights: 337 older primary care patients completed full neuropsychological assessment.Risk modeling used data available in a typical primary care record.The model successfully differentiated patients with/without cognitive impairment.This EHR model offers a passive workflow to identify patients at cognitive risk.","journal":"Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring","year":2025,"id":568451,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9391,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":436288,"name":"Olivia Hogue","orcid":"0000-0002-3985-3831","position":1,"is_corresponding":false},{"id":1472707,"name":"Saket Saxena","orcid":"0000-0003-4084-7954","position":2,"is_corresponding":false},{"id":974637,"name":"Anita D. Misra‐Hebert","orcid":"0000-0003-3079-7025","position":3,"is_corresponding":false},{"id":244801,"name":"Alex Milinovich","orcid":"0000-0003-0585-1480","position":4,"is_corresponding":false},{"id":717965,"name":"Michael B. Rothberg","orcid":"0000-0002-2063-1876","position":5,"is_corresponding":false},{"id":717964,"name":"Elizabeth R. Pfoh","orcid":"0000-0003-3485-260X","position":6,"is_corresponding":false},{"id":295664,"name":"Robyn M. Busch","orcid":"0000-0002-5442-4912","position":7,"is_corresponding":false},{"id":1319555,"name":"Kamini Krishnan","orcid":"0000-0001-6741-2905","position":8,"is_corresponding":false},{"id":314588,"name":"Robert J. Fox","orcid":"0000-0002-4263-3717","position":9,"is_corresponding":false},{"id":45522,"name":"Michael W. Kattan","orcid":"0000-0002-3840-4161","position":10,"is_corresponding":false},{"id":734656,"name":"Darlene Floden","orcid":"0000-0002-3131-0058","position":0,"is_corresponding":true}],"reference_count":43,"raw_metadata":null,"created_at":"2026-07-19T02:56:52.212268Z","pmid":"40520422","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":[]}