{"doi":"10.1101/2024.08.13.24311944","title":"Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation","abstract":"Background: AF risk estimation is feasible using clinical factors, inherited predisposition, and artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis. Objective: To test whether integrating these distinct risk signals improves AF risk estimation. Methods: In the UK Biobank prospective cohort study, we estimated AF risk using three models derived from external populations: the well-validated Cohorts for Aging in Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) clinical score, a 1,113,667-variant AF polygenic risk score (PRS), and a published AI-enabled ECG-based AF risk model (ECG-AI). We estimated discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic (AUROC) and average precision (AP). Results: Among 49,293 individuals (mean age 65±8 years, 52% women), 825 (2.4%) developed AF within 5 years. Using single models, discrimination of 5-year incident AF was higher using ECG-AI (AUROC 0.705 [95%CI 0.686-0.724]; AP 0.085 [0.071-0.11]) and CHARGE-AF (AUROC 0.785 [0.769-0.801]; AP 0.053 [0.048-0.061]) versus the PRS (AUROC 0.618, [0.598-0.639]; AP 0.038 [0.028-0.045]). The inclusion of all components (\"Predict-AF3\") was the best performing model (AUROC 0.817 [0.802-0.832]; AP 0.11 [0.091-0.15], p<0.01 vs CHARGE-AF+ECG-AI), followed by the two component model of CHARGE-AF+ECG-AI (AUROC 0.802 [0.786-0.818]; AP 0.098 [0.081-0.13]). Using Predict-AF3, individuals at high AF risk (i.e., 5-year predicted AF risk >2.5%) had a 5-year cumulative incidence of AF of 5.83% (5.33-6.32). At the same threshold, the 5-year cumulative incidence of AF was progressively higher according to the number of models predicting high risk (zero: 0.67% [0.51-0.84], one: 1.48% [1.28-1.69], two: 4.48% [3.99-4.98]; three: 11.06% [9.48-12.61]), and Predict-AF3 achieved favorable net reclassification improvement compared to both CHARGE-AF+ECG-AI (0.039 [0.015-0.066]) and CHARGE-AF+PRS (0.033 [0.0082-0.059]). Conclusions: Integration of clinical, genetic, and AI-derived risk signals improves discrimination of 5-year AF risk over individual components. Models such as Predict-AF3 have substantial potential to improve prioritization of individuals for AF screening and preventive interventions.","journal":"medRxiv","year":2024,"id":487262,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9517,"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":886,"name":"Joel Rämö","orcid":"0000-0002-6429-5149","position":1,"is_corresponding":false},{"id":236909,"name":"Sam Friedman","orcid":"0000-0002-0688-2169","position":2,"is_corresponding":false},{"id":38117,"name":"Lu‐Chen Weng","orcid":"0000-0003-1475-4930","position":3,"is_corresponding":false},{"id":24864,"name":"Carolina Roselli","orcid":"0000-0001-5267-6756","position":4,"is_corresponding":false},{"id":37462,"name":"Min Seo Kim","orcid":"0000-0003-2115-7835","position":5,"is_corresponding":false},{"id":1331227,"name":"Akl C. Fahed","orcid":"0000-0002-3564-4944","position":6,"is_corresponding":false},{"id":1083,"name":"Steven A. Lubitz","orcid":"0000-0002-9599-4866","position":7,"is_corresponding":false},{"id":464218,"name":"Mahnaz Maddah","orcid":"0000-0002-9837-6000","position":8,"is_corresponding":false},{"id":896,"name":"Patrick T. Ellinor","orcid":"0000-0002-2067-0533","position":9,"is_corresponding":false},{"id":552483,"name":"Shaan Khurshid","orcid":"0000-0002-2840-4539","position":10,"is_corresponding":false},{"id":885,"name":"Shinwan Kany","orcid":"0000-0001-8113-733X","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:08:06.013846Z","pmid":"39185529","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":[]}