A Simple, Non-Invasive Score to Predict Paroxysmal Atrial Fibrillation.

ABSTRACT

Paroxysmal atrial fibrillation (pAF) is a major risk factor for stroke but remains often unobserved. To predict the presence of pAF, we developed model scores based on echocardiographic and other clinical parameters from routine cardiac assessment. The scores can be easily implemented to clinical practice and might improve the early detection of pAF. In total, 47 echocardiographic and other clinical parameters were collected from 1000 patients with sinus rhythm (SR; n = 728), pAF (n = 161) and cAF (n = 111). We developed logistic models for classifying between pAF and SR that were reduced to the most predictive parameters. To facilitate clinical implementation, linear scores were derived. To study the pathophysiological progression to cAF, we analogously developed models for cAF prediction. For classification between pAF and SR, amongst 12 selected model parameters, the most predictive variables were tissue Doppler imaging velocity during atrial contraction (TDI, A'), left atrial diameter, age and aortic root diameter. Models for classifying between pAF and SR or between cAF and SR showed areas under the ROC curves of 0.80 or 0.93, which resembles classifiers with high discriminative power. The novel risk scores were suitable to predict the presence of pAF based on variables readily available from routine cardiac assessment. Modelling helped to quantitatively characterize the pathophysiologic transition from SR via pAF to cAF. Applying the scores may improve the early detection of pAF and might be used as decision aid for initiating preventive interventions to reduce AF-associated complications.