{"doi":"10.1016/j.jacadv.2025.102060","title":"External Validation of the Veterans Affairs Women Cardiovascular Disease Risk Score to Nonveteran Women","abstract":"BACKGROUND: The Veterans Affairs (VA) women cardiovascular disease (CVD) risk score is an internally validated tool to assess the ten-year atherosclerosis CVD (ASCVD) risk in women veterans and has been successfully applied to assess the CVD risk for women veterans. OBJECTIVES: This study externally validated the VA women CVD risk score to assess the ASCVD risk in civilian women and young active-duty women military service members. METHODS: This study employed linear calibration models applied to the Cox model stratified by race and ethnicity group, non-Hispanic (N-H) White, N-H Black and Hispanic, and log-likelihood ratio tests in externally validating the VA women CVD risk score for 1,383 civilians (Dallas Heart Study) and 154,168 young active-duty military service members (Department of Defense and Veterans Affairs Infrastructure for Clinical Intelligence Direct Care). RESULTS: The VA women CVD risk score not only met the good discrimination criterion (C-statistics ≥0.7) but also showed good accuracy in predicting ASCVD events for external populations-Dallas Heart Study civilian women and Department of Defense and Veterans Affairs Infrastructure for Clinical Intelligence Direct Care young active-duty women military service members-across all 3 race and ethnic groups. Calibration in-the-large, a simple update of ASCVD event-free survival, preserved accuracy of the VA women CVD risk score for all races for civilian and young women military service members, except Hispanic civilian women who needed recalibration. CONCLUSIONS: The calibrated VA women CVD risk score can serve as a validated clinical decision-making tool to screen and assess the CVD risk of nonveteran women-civilian and younger active-duty military service members.","journal":"JACC Advances","year":2025,"id":546015,"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":0.9516,"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":1437253,"name":"Xiaofei Chen","orcid":"0000-0001-8086-4746","position":1,"is_corresponding":false},{"id":917320,"name":"Erum Z. Whyne","orcid":"0000-0003-4950-4405","position":2,"is_corresponding":false},{"id":506345,"name":"Shirling Tsai","orcid":"0000-0003-0269-2605","position":3,"is_corresponding":false},{"id":1437764,"name":"Monica R. Barbosa","orcid":null,"position":4,"is_corresponding":false},{"id":525521,"name":"Bala Ramanan","orcid":"0000-0003-1319-5903","position":5,"is_corresponding":false},{"id":631601,"name":"Sujata Bhushan","orcid":null,"position":6,"is_corresponding":false},{"id":663719,"name":"Dian J. Cao","orcid":"0000-0002-2457-8291","position":7,"is_corresponding":false},{"id":1001168,"name":"Haekyung Jeon‐Slaughter","orcid":"0000-0002-5753-2935","position":0,"is_corresponding":true}],"reference_count":35,"raw_metadata":null,"created_at":"2026-07-19T02:53:27.751050Z","pmid":"40803289","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":[]}