{"doi":"10.1101/2023.09.28.23296234","title":"A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression","abstract":"ABSTRACT Importance Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. Objective A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression. Design, Setting, and Participants We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024. Exposure DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos. Main Outcomes and Measures Annualized change in peak aortic valve velocity (AV-V max ) and late (>6 months) aortic valve replacement (AVR). Results A total of 12,599 participants were included in the echocardiographic study (YNHHS: n =8,798, median age of 71 [IQR (interquartile range):60-80] years, 4250 [48.3%] women, and CSMC: n =3,801, 67 [IQR:54-78] years, 1685 [44.3%] women). Higher baseline DASSi was associated with faster progression in AV-V max (per 0.1 DASSi increments: YNHHS: +0.033 m/s/year [95%CI:0.028-0.038], n=5,483, and CSMC: +0.082 m/s/year [0.053-0.111], n=1,292), with levels ≥ vs <0.2 linked to a 4-to-5-fold higher AVR risk (715 events in YNHHS; adj.HR 4.97 [95%CI: 2.71-5.82], 56 events in CSMC: 4.04 [0.92-17.7]), independent of age, sex, ethnicity/race, ejection fraction and AV-V max . This was reproduced across 45,474 participants (median age 65 [IQR:59-71] years, 23,559 [51.8%] women) undergoing CMR in the UK Biobank (adj.HR 11.4 [95%CI:2.56-50.60] for DASSi ≥vs<0.2). Saliency maps and phenome-wide association studies supported links with traditional cardiovascular risk factors and diastolic dysfunction. Conclusions and Relevance In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.","journal":"medRxiv","year":2023,"id":400970,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"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":0.9548,"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":988180,"name":"Gregory Holste","orcid":"0000-0002-5657-3081","position":1,"is_corresponding":false},{"id":218311,"name":"Neal Yuan","orcid":"0000-0001-5782-7437","position":2,"is_corresponding":false},{"id":614790,"name":"Andreas Coppi","orcid":"0000-0002-5243-552X","position":3,"is_corresponding":false},{"id":988181,"name":"Robert L. McNamara","orcid":"0000-0002-1364-7749","position":4,"is_corresponding":false},{"id":1177647,"name":"Norrisa Haynes","orcid":"0000-0001-8028-7133","position":5,"is_corresponding":false},{"id":1177648,"name":"Amit N. Vora","orcid":"0000-0003-1269-9114","position":6,"is_corresponding":false},{"id":271733,"name":"Eric J. Velazquez","orcid":"0000-0003-2245-7477","position":7,"is_corresponding":false},{"id":113688,"name":"Fan Li","orcid":"0000-0001-6183-1893","position":8,"is_corresponding":false},{"id":639104,"name":"Venu Menon","orcid":"0000-0003-4410-2677","position":9,"is_corresponding":false},{"id":277930,"name":"Samir Kapadia","orcid":"0000-0002-0026-3391","position":10,"is_corresponding":false},{"id":242022,"name":"Thomas M. Gill","orcid":"0000-0002-6450-0368","position":11,"is_corresponding":false},{"id":5026,"name":"Girish N. Nadkarni","orcid":"0000-0001-6319-4314","position":12,"is_corresponding":false},{"id":1077,"name":"Harlan M. Krumholz","orcid":"0000-0003-2046-127X","position":13,"is_corresponding":false},{"id":641080,"name":"Zhangyang Wang","orcid":"0000-0002-2050-5693","position":14,"is_corresponding":false},{"id":218308,"name":"David Ouyang","orcid":"0000-0002-3813-7518","position":15,"is_corresponding":false},{"id":74880,"name":"Rohan Khera","orcid":"0000-0001-9467-6199","position":16,"is_corresponding":false},{"id":89683,"name":"Evangelos K. Oikonomou","orcid":"0000-0003-4362-0720","position":0,"is_corresponding":true}],"reference_count":46,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:20:08.195423Z","pmid":"37808685","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":[]}