{"doi":"10.22489/cinc.2023.348","title":"A Grid Search of Fibrosis Thresholds for Uncertainty Quantification in Atrial Flutter Simulations","abstract":"Atypical atrial flutter (AAF) is a cardiac arrhythmia commonly developed following catheter ablation for atrial fibrillation. Patient-specific computational simulations of propagation have shown promise in prospectively predicting AAF reentrant circuits and providing useful insight to guide successful ablation procedures. These patient-specific models require a large number of inputs, each with an unknown amount of uncertainty. Uncertainty quantification (UQ) is a technique to assess how variability in a set of input parameters can affect the output of a model. However, modern UQ techniques, such as polynomial chaos expansion, require a well-defined output to map to the inputs. In this study, we aimed to explore the sensitivity of simulated reentry to the selection of fibrosis threshold in patient-specific AAF models. We utilized the image intensity ratio (IIR) method to set the fibrosis threshold in the LGE-MRI from a single patient with prior ablation. We found that the majority of changes to the duration of reentry occurred within an IIR range of 1.01 to 1.39, and that there was a large amount of variability in the resulting arrhythmia. This study serves as a starting point for future UQ studies to investigate the nonlinear relationship between fibrosis threshold and the resulting arrhythmia in AAF models.","journal":"Computing in cardiology","year":2023,"id":415496,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9514,"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":557657,"name":"Jake Bergquist","orcid":"0000-0002-4586-6911","position":1,"is_corresponding":false},{"id":1174196,"name":"Eric Paccione","orcid":"0009-0002-8546-6397","position":2,"is_corresponding":false},{"id":530019,"name":"Matthias Lange","orcid":"0000-0001-5635-3444","position":3,"is_corresponding":false},{"id":353923,"name":"Eugene Kwan","orcid":"0000-0001-7581-6096","position":4,"is_corresponding":false},{"id":1174197,"name":"Bram Hunt","orcid":"0000-0002-8457-7290","position":5,"is_corresponding":false},{"id":353927,"name":"Rob MacLeod","orcid":"0000-0002-0000-0356","position":6,"is_corresponding":false},{"id":557660,"name":"Akil Narayan","orcid":"0000-0002-5914-4207","position":7,"is_corresponding":false},{"id":237803,"name":"Ravi Ranjan","orcid":"0000-0002-3321-2435","position":8,"is_corresponding":false},{"id":1179783,"name":"Benjamin A Orkild","orcid":null,"position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T01:22:12.933438Z","pmid":"41049023","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":[]}