{"doi":"10.1007/s10620-023-08043-8","title":"Predictive Algorithm for Hepatic Steatosis Detection Using Elastography Data in the Veterans Affairs Electronic Health Records","abstract":"BACKGROUND AND AIMS: Nonalcoholic fatty liver disease (NAFLD) has reached pandemic proportions. Early detection can identify at-risk patients who can be linked to hepatology care. The vibration-controlled transient elastography (VCTE) controlled attenuation parameter (CAP) is biopsy validated to diagnose hepatic steatosis (HS). We aimed to develop a novel clinical predictive algorithm for HS using the CAP score at a Veterans' Affairs hospital. METHODS: We identified 403 patients in the Greater Los Angeles VA Healthcare System with valid VCTEs during 1/2018-6/2020. Patients with alcohol-associated liver disease, genotype 3 hepatitis C, any malignancies, or liver transplantation were excluded. Linear regression was used to identify predictors of NAFLD. To identify a CAP threshold for HS detection, receiver operating characteristic analysis was applied using liver biopsy, MRI, and ultrasound as the gold standards. RESULTS: The cohort was racially/ethnically diverse (26% Black/African American; 20% Hispanic). Significant positive predictors of elevated CAP score included diabetes, cholesterol, triglycerides, BMI, and self-identifying as Hispanic. Our predictions of CAP scores using this model strongly correlated (r = 0.61, p < 0.001) with actual CAP scores. The NAFLD model was validated in an independent Veteran cohort and yielded a sensitivity of 82% and specificity 83% (p < 0.001, 95% CI 0.46-0.81%). The estimated optimal CAP for our population cut-off was 273.5 dB/m, resulting in AUC = 75.5% (95% CI 70.7-80.3%). CONCLUSION: Our HS predictive algorithm can identify at-risk Veterans for NAFLD to further risk stratify them by non-invasive tests and link them to sub-specialty care. Given the biased referral pattern for VCTEs, future work will need to address its applicability in non-specialty clinics. Proposed clinical algorithm to identify patients at-risk for NAFLD prior to fibrosis staging in Veteran.","journal":"Digestive Diseases and Sciences","year":2023,"id":385509,"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":0.9595,"is_data_producer":false,"deposit_databanks":null,"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":662722,"name":"Ram Sundaresh","orcid":null,"position":1,"is_corresponding":false},{"id":1153524,"name":"Anna H. Lee","orcid":"0000-0002-8330-0599","position":2,"is_corresponding":false},{"id":441512,"name":"Nicole Prause","orcid":"0000-0002-1420-9177","position":3,"is_corresponding":false},{"id":1153525,"name":"Frank Hao","orcid":"0000-0003-1462-612X","position":4,"is_corresponding":false},{"id":301052,"name":"Tien S. Dong","orcid":"0000-0003-0105-8063","position":5,"is_corresponding":false},{"id":651125,"name":"Monica A. Tincopa","orcid":"0000-0002-9954-8548","position":6,"is_corresponding":false},{"id":243161,"name":"George Cholankeril","orcid":"0000-0001-5335-8426","position":7,"is_corresponding":false},{"id":233438,"name":"Nicole E. Rich","orcid":"0000-0003-2740-8818","position":8,"is_corresponding":false},{"id":1153956,"name":"Jenna Kawamoto","orcid":null,"position":9,"is_corresponding":false},{"id":442329,"name":"Debika Bhattacharya","orcid":"0000-0002-2136-7763","position":10,"is_corresponding":false},{"id":851823,"name":"Steven B. Han","orcid":null,"position":11,"is_corresponding":false},{"id":225413,"name":"Arpan Patel","orcid":"0000-0002-6548-4531","position":12,"is_corresponding":false},{"id":401919,"name":"Magda Shaheen","orcid":"0000-0001-9077-5798","position":13,"is_corresponding":false},{"id":278348,"name":"Jihane N. Benhammou","orcid":"0000-0003-2442-5145","position":14,"is_corresponding":false},{"id":27726,"name":"Saroja Bangaru","orcid":null,"position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-19T01:17:52.636565Z","pmid":"37864738","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":[]}