{"doi":"10.1186/s12877-024-05567-0","title":"Developing a prediction model for cognitive impairment in older adults following critical illness","abstract":"BACKGROUND: New or worsening cognitive impairment or dementia is common in older adults following an episode of critical illness, and screening post-discharge is recommended for those at increased risk. There is a need for prediction models of post-ICU cognitive impairment to guide delivery of screening and support resources to those in greatest need. We sought to develop and internally validate a machine learning model for new cognitive impairment or dementia in older adults after critical illness using electronic health record (EHR) data. METHODS: Our cohort included patients > 60 years of age admitted to a large academic health system ICU in North Carolina between 2015 and 2021. Patients were included in the cohort if they were admitted to the ICU for ≥ 48 h with ≥ 2 ambulatory visits prior to hospitalization and at least one visit in the post-discharge year. We used a machine learning model, oblique random survival forests (ORSF), to examine the multivariable association of 54 structured data elements available by 3 months after discharge with incident diagnoses of cognitive impairment or dementia over 1-year. RESULTS: In this cohort of 8,299 adults, 22% died and 4.9% were diagnosed with dementia or cognitive impairment within one year. The ORSF model showed reasonable discrimination (c-statistic = 0.83) and stability with little difference in the model's c-statistic across time. CONCLUSION: Machine learning using readily available EHR data can predict new cognitive impairment or dementia at 1-year post-ICU discharge in older adults with acceptable accuracy. Further studies are needed to understand how this tool may impact screening for cognitive impairment in the post-discharge period.","journal":"BMC Geriatrics","year":2024,"id":462501,"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":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9489,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1008304,"name":"Lauren Witek","orcid":"0000-0001-9604-0629","position":1,"is_corresponding":false},{"id":295974,"name":"Nicholas M. Pajewski","orcid":"0000-0002-4447-6196","position":2,"is_corresponding":false},{"id":376956,"name":"Stephanie Parks Taylor","orcid":"0000-0001-6163-9666","position":3,"is_corresponding":false},{"id":788881,"name":"Richa Bundy","orcid":"0000-0002-2506-5682","position":4,"is_corresponding":false},{"id":295973,"name":"Jeff D. Williamson","orcid":"0000-0002-0185-5823","position":5,"is_corresponding":false},{"id":329407,"name":"Byron C. Jaeger","orcid":"0000-0001-7399-2299","position":6,"is_corresponding":false},{"id":654752,"name":"Jessica A. Palakshappa","orcid":"0000-0002-6333-365X","position":7,"is_corresponding":false},{"id":1292827,"name":"Ashley E. Eisner","orcid":null,"position":0,"is_corresponding":true}],"reference_count":37,"raw_metadata":null,"created_at":"2026-07-19T02:04:24.680035Z","pmid":"39614152","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":[]}