{"doi":"10.1093/gerona/glaf119","title":"External validation of an AI-based preoperative frailty index using real-world data","abstract":"BACKGROUND: Preoperative frailty assessment is crucial for surgical risk stratification in older adults. Traditional frailty measurements are often too time-consuming and resource-intensive in preoperative settings. This study aimed to externally validate an artificial intelligence (AI)-based frailty index developed using electronic health records (EHR). METHODS: We externally validated an AI-based frailty index, previously developed by our team, on a cohort of 1 52 364 surgical patients aged 65+ years from the OneFlorida+ Clinical Research Consortium. We examined the association between the predicted frailty and three postoperative outcomes: 30-day mortality, length of hospital stay, and discharge disposition. We also compared the predictive performance of general and service-specific frailty indices (the latter developed using data from patients undergoing specific surgeries) in predicting postoperative outcomes. RESULTS: The AI-based frailty index demonstrated a strong and stepwise association with adverse postoperative outcomes. Patients in the highest frailty level (top 20%) had significantly higher odds of 30-day mortality (OR 4.33, 95% CI 3.91-4.80), longer hospital stays (2.53 times longer, 95% CI 2.47-2.60), and a higher likelihood of unfavorable discharge dispositions compared to the lowest frailty level, after adjusting for demographics and comorbidities. The general frailty index performed comparably to or slightly better than service-specific indices across surgical specialties. CONCLUSION: The developed preoperative frailty index effectively predicts postoperative outcomes in a large and diverse external cohort. The index's efficiency and predictive performance in stratifying surgical risk can potentially improve surgical care and outcomes.","journal":"The Journals of Gerontology Series A","year":2025,"id":536256,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7963,"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":232201,"name":"Feifei Xiao","orcid":"0000-0002-1597-4719","position":1,"is_corresponding":false},{"id":537813,"name":"Mohammad Al‐Ani","orcid":"0000-0002-7220-1114","position":2,"is_corresponding":false},{"id":352830,"name":"Catherine C. Price","orcid":"0000-0001-5994-0644","position":3,"is_corresponding":false},{"id":109416,"name":"Todd M. Manini","orcid":"0000-0002-5970-4462","position":4,"is_corresponding":false},{"id":492367,"name":"Mamoun Mardini","orcid":"0000-0002-5345-8811","position":5,"is_corresponding":false},{"id":682098,"name":"Chen Bai","orcid":"0000-0003-0961-1927","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":null,"created_at":"2026-07-19T02:52:05.227140Z","pmid":"40489638","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":[]}