Atrial fibrillation (AF) causes important mortality and morbidity on a population-level. So far, we do not have the means to prevent AF or AF-related complications adequately. Therefore, over 70 experts on atrial fibrillation convened for the 2nd AFNET/EHRA consensus conference to suggest directions for research to improve management of AF patients (Appendix 1). The group defined three main areas in need for research in AF: 1. better understanding of the mechanisms of AF; 2. Improving rhythm control monitoring and management; and 3. comprehensive cardiovascular risk management in AF patients. The group put forward the hypothesis that successful therapy of AF and its associated complications will require comprehensive therapy. This applies e.g. to the "old" debate of "rate versus rhythm control", since rhythm control is generally added to underlying (continued) rate control therapy, but also to the emerging debate of "antiarrhythmic drugs versus catheter ablation", of which both may be needed in most patients to maintain sinus rhythm, but also to therapy of conditions that predispose to AF and contribute to cardiovascular complications such as stroke, cognitive decline, heart failure, and acute coronary syndromes. We call for research initiatives aiming at a better understanding of the different causes of AF and its complications, and at development and validation of mechanism-based therapies. The future of AF therapy may require a combination of management of underlying and concomitant conditions, early and comprehensive rhythm control therapy, adequate control of ventricular rate and cardiac function, and continuous therapy to prevent AF-associated complications (e.g. antithrombotic therapy). The reasons for these suggestions are detailed in this paper.

The validity of prognostic models is an important prerequisite for their applicability in practical clinical settings. Here, we report on a specific prognostic study on stroke patients and describe how we explored the prediction performance of our model. We considered two practically highly relevant generalization aspects, namely, the model's performance in patients recruited at a later time point (temporal transportability) and in medical centers different from those used for model building (geographic transportability). To estimate the accuracy of the model, we investigated classical internal validation techniques and leave-one-center-out cross validation (CV). Prognostic models predicting functional independence of stroke patients were developed in a training set using logistic regression, support vector machines, and random forests (RFs). Tenfold CV and leave-one-center-out CV were employed to estimate temporal and geographic transportability of the models. For temporal and external validation, the resulting models were used to classify patients from a later time point and from different clinics. When applying the regression model or the RFs, accuracy in the temporal validation data was well predicted from classical internal validation. However, when predicting geographic transportability all approaches had difficulties. We observed that the leave-one-center-out CV yielded better estimates than classical CV. On the basis of our results, we conclude that external validation in patients from different clinics is required before a prognostic model can be applied in practice. Even validating the model in patients recruited merely at a later time point does not suffice to predict how it may fare with regard to another clinic.

Following cardiac valve replacement, new small ischemic brain lesions were detected by diffusion-weighted MRI. Neurocognitive decline was present early after operation, but resolved within 4 months. A correlation of new ischemic lesions to postoperative cognitive dysfunction or clinical variables was not found.

Although neurocognitive decline after CABG is mostly transient, memory impairment can persist for months. New ischemic brain lesions on postoperative diffusion-weighted MRI do not appear to account for the persistent neurocognitive decline.