{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["26(11)"],"submitter":["Shen A"],"pubmed_abstract":["<h4>Background</h4>Atrial fibrillation (AF) is a major complication of hypertrophic cardiomyopathy (HCM) with significant prognostic implications. Current risk prediction models lack the integration of comprehensive cardiac magnetic resonance (CMR) metrics and subtype-specific analyses.<h4>Methods</h4>A retrospective study of 405 HCM patients (86 with AF) was performed from 2019 to 2024. After excluding highly correlated variables (|r| > 0.8), the cohort was split into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression analyses were used to identify predictors, with model performance assessed via receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. S"],"journal":["Reviews in cardiovascular medicine"],"pagination":["45267"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12680985"],"repository":["biostudies-literature"],"pubmed_title":["Development, Validation, and Subtype Analysis of a Predictive Model for Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy."],"pmcid":["PMC12680985"],"pubmed_authors":["Liu Y","Shen A","Xue Q","Liang J","Mu D","Yue X","Xu J","Yu H"],"additional_accession":[]},"is_claimable":false,"name":"Development, Validation, and Subtype Analysis of a Predictive Model for Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy.","description":"<h4>Background</h4>Atrial fibrillation (AF) is a major complication of hypertrophic cardiomyopathy (HCM) with significant prognostic implications. Current risk prediction models lack the integration of comprehensive cardiac magnetic resonance (CMR) metrics and subtype-specific analyses.<h4>Methods</h4>A retrospective study of 405 HCM patients (86 with AF) was performed from 2019 to 2024. After excluding highly correlated variables (|r| > 0.8), the cohort was split into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression analyses were used to identify predictors, with model performance assessed via receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. S","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-05T23:27:46.8Z","creation":"2026-05-23T03:13:18.762Z"},"accession":"S-EPMC12680985","cross_references":{"pubmed":["41356296"],"doi":["10.31083/RCM45267"]}}