Sandra Cabrera1, Ermengol Vallès2, Begoña Benito1, Óscar Alcalde1, Jesús Jiménez1, Roger Fan3, Julio Martí-Almor1. 1. Electrophysiology Unit, Department of Cardiology, Hospital del Mar, Universitat Autònoma de Barcelona, Barcelona, Spain; Heart Diseases Biomedical Research Group, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. 2. Electrophysiology Unit, Department of Cardiology, Hospital del Mar, Universitat Autònoma de Barcelona, Barcelona, Spain; Heart Diseases Biomedical Research Group, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain. Electronic address: ermengolvalles@mac.com. 3. Stony Brook University School of Medicine Health Science Center, T16-80, Stony Brook, NY 11794, United States.
Abstract
BACKGROUND: Predicting atrial fibrillation is a tremendous challenge. Only few studies have included 24h-Holter monitoring characteristics to predict new onset AF (NOAF). OBJECTIVES: Our aim is to define simple predictors for NOAF. METHODS: The study population included 468 patients undergoing Holter for any cause. After excluding 169 patients for history of AF prior to or during the Holter monitoring period, 299 patients were assessed for incidence of NOAF. RESULTS: Age at inclusion was 62.5±18years (53.5% male). After a median follow up of 39.1 [IQI 36.6-40] months, the incidence of NOAF was 10.4%. With univariate analysis, age, hypertension, diabetes, renal impairment, heart failure/cardiomyopathy, left ventricle ejection fraction ≤50%, left atrium diameter ≥40mm, CHA2DS2 VASc ≥4, premature atrial complexes (PAC) ≥0.2%, and PR interval were associated with NOAF. With multivariate analysis, age (HR 1075; p=0.001 per year), presence of heart failure/cardiomyopathy (HR 6,16; p<0.001), PAC≥0.2% (HR 3,32; p=0.003) and PR interval (HR 1.011; p=0.006 per millisecond) were independent predictors for NOAF. Those predictors were used to create a risk calculator for NOAF, which was validated in an independent cohort of 200 consecutive patients with similar baseline characteristics. This new tool resulted in good discrimination capacity calculated by the C index for NOAF prediction: Area under curve (AUC) (95% CI) 0.794 (0.714-0.875) at 2years and 0.794 (0.713-0.875) at 3years. CONCLUSIONS: Simple clinical, ECG and Holter monitoring parameters are able to predict NOAF in a broad population and may help guide more rigorous monitoring for atrial fibrillation.
BACKGROUND: Predicting atrial fibrillation is a tremendous challenge. Only few studies have included 24h-Holter monitoring characteristics to predict new onset AF (NOAF). OBJECTIVES: Our aim is to define simple predictors for NOAF. METHODS: The study population included 468 patients undergoing Holter for any cause. After excluding 169 patients for history of AF prior to or during the Holter monitoring period, 299 patients were assessed for incidence of NOAF. RESULTS: Age at inclusion was 62.5±18years (53.5% male). After a median follow up of 39.1 [IQI 36.6-40] months, the incidence of NOAF was 10.4%. With univariate analysis, age, hypertension, diabetes, renal impairment, heart failure/cardiomyopathy, left ventricle ejection fraction ≤50%, left atrium diameter ≥40mm, CHA2DS2 VASc ≥4, premature atrial complexes (PAC) ≥0.2%, and PR interval were associated with NOAF. With multivariate analysis, age (HR 1075; p=0.001 per year), presence of heart failure/cardiomyopathy (HR 6,16; p<0.001), PAC≥0.2% (HR 3,32; p=0.003) and PR interval (HR 1.011; p=0.006 per millisecond) were independent predictors for NOAF. Those predictors were used to create a risk calculator for NOAF, which was validated in an independent cohort of 200 consecutive patients with similar baseline characteristics. This new tool resulted in good discrimination capacity calculated by the C index for NOAF prediction: Area under curve (AUC) (95% CI) 0.794 (0.714-0.875) at 2years and 0.794 (0.713-0.875) at 3years. CONCLUSIONS: Simple clinical, ECG and Holter monitoring parameters are able to predict NOAF in a broad population and may help guide more rigorous monitoring for atrial fibrillation.
Authors: Jin Kyung Hwang; Hye Bin Gwag; Seung-Jung Park; Young Keun On; June Soo Kim; Kyoung-Min Park Journal: Clin Cardiol Date: 2018-04-17 Impact factor: 2.882