Background:

Early detection of atrial fibrillation (AF) is highly relevant to prevent cardio-embolic stroke and thus disability and death. Unfortunately, clinically occult AF is frequently associated with delayed diagnosis. Thus, there is a debate to extend rhythm monitoring in patients after stroke even beyond the 72 hours, as recommended by the European Society of Cardiology guidelines. However, extended ECG monitoring leads to increased costs, personnel workload, and effort for patients. Therefore, risk evaluation of AF and a step-by-step diagnostic assessment of AF seem to be indispensable. Onset and maintenance of AF are associated with left atrial (LA) remodeling, dysfunction and fibrosis. Echocardiographic parameters representing impaired left atrial (LA) function and remodeling are of high value to predict atrial fibrillation (AF). This study aimed to develop a prediction model for AF easily to apply in clinical routine containing echocardiographic parameters associated with LA remodeling and - function.

Methods and Results:

This monocentric, semi-blinded, controlled analysis included 235 patients to derive a prediction model.

This prediction model was tested in a validation cohort encompassing 290 cardiovascular inpatients. The derivation and validation cohort included 54 (23%) and 66 (23%) patients with AF, respectively. Transthoracic echocardiography, comprising parameters indicating left atrial remodeling (septal/lateral total atrial conduction time (s/l PA-TDI)) and left atrial volume indexed to a` (LAVI/a`) was performed in each patient. Based on multivariable regressions analysis, four variables were enclosed into the EAHsy (Echocardiography, Age, Hypertension)-AF-Score for AF prediction: Hypertension, Age, LAVI/a` and septal PA-TDI. To account for individual predictive weight in the AF-Score, one score point was assigned per regression coefficient (β) in the multivariable model to identify the maximal point range. The prediction of AF was positively correlated with the sum of the total individual score. Risk categories were defined: low risk (0-2 points), intermediate risk (3-5 points), high risk (6-8 points). In the validation cohort discrimination was strong (C-statistic 0.987, 95%CI 0.974-0.991) with an adequately performed calibration. Multivariable regressions analysis in the validation cohort confirmed septal PA-TDI and LAVI/a` as independent predictors for AF. After replacing these two parameters with the prediction model, the AF score was an independent predictor in the multivariable regressions analysis The EAHsy-AF-Score was associated with a more precise prediction of AF in comparison to commonly used AF-scores (CHADS₂-, ATLAS-, ARIC-, CHARGE-AF score).

Conclusion:

The EAHsy-AF-Score containing age, hypertension and echocardiographic parameters of atrial dysfunction and remodeling precisely predicts the incidence of AF in a general population of patients with cardiovascular disease. The EAHsy-AF-Score may enable more selective rhythm monitoring in specific patients at high risk for AF.