learned by the RIMARC algorithm can be used for accurately classifying the preoperative rhythm status. APs were included from 221 SR and 158 AF patients. During a learning phase, the RIMARC algorithm established a ranking order of 62 features by predictive value for SR or AF. The model was then challenged with an additional test set of features from 28 patients in whom rhythm status was blinded. The accuracy of the risk prediction for AF by the model was very good (0.93) when all features were used. Without the seven AP features, accuracy still reached 0.71. In conclusion, we have shown that training the machine-learning algorithm RIMARC with an experimental and clinical data set allows predicting a classification in a test data set with high accuracy. In a clinical setting, this approach may prove useful for finding hypothesis-generating associations between different parameters.Abstract Ex vivo recorded action potentials (APs) in human right atrial tissue from patients in sinus rhythm (SR) or atrial fibrillation (AF) display a characteristic spike-anddome or triangular shape, respectively, but variability is huge within each rhythm group. The aim of our study was to apply the machine-learning algorithm ranking instances by maximizing the area under the ROC curve (RIMARC) to a large data set of 480 APs combined with retrospectively collected general clinical parameters and to test whether the rules Ursula Ravens, Deniz Katircioglu-Öztürk, Ali Oto and H. Altay Güvenir have equally contributed. Action potential duration at 20 % of repolarization (ms) APD 50 Action potential duration at 50 % of repolarization (ms) APD 90 Action potential duration at 90 % of repolarization (ms) dV/dt max Maximum rate of depolarization (Vs −1 ) MAD Maximum area under ROC curve-based discretization PLT 20 "Plateau potential" defined as the mean potential (mV) in the time window between 20 % of APD 90 plus 5 ms RIMARC Ranking instances by maximizing the area under the ROC curve RMP Resting membrane potential (mV) ROC Receiver operating characteristics SR Sinus rhythm Electronic supplementary material

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The TRAF cohort is the first population-based, whole-country cohort of AF epidemiology, quality of care and outcomes. It provides a unique opportunity to study the patterns, causes and impact of treatments on the incidence and outcomes of AF in a developing country.

Pharmacological conversion of persistent AF with flecainide without the need for electrical cardioversion is a powerful and independent predictor of maintenance of SR. A strategy of flecainide pretreatment for 48 h prior to planned electrical cardioversion may be a useful planning of a strategy of long-term rhythm control.

With the ANTIPAF-AFNET 2 dataset, the present data-mining analyses suggest that a baseline SF-12 mental component score, age, systolic blood pressure, BUN, and creatinine level of the patients are predictors of AF burden. Additional studies are necessary to understand the distinct kidney-specific pathophysiological pathways that contribute to AF burden.