Rationale and design of the VAST-AF trial: A randomized controlled, blinded, clinical trial to evaluate transcutaneous vagal nerve stimulation for the prevention of persistent atrial fibrillation recurrence.

BACKGROUND: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and is linked to significant symptoms and an elevated risk of heart failure, thromboembolism and disabling stroke. Not only do patients suffer from AF and the concomitant complications, but it is a great economic burden for healthcare systems all over the world. Despite remarkable progress in the field of AF, the basic mechanisms of AF development remain unresolved. Data suggests that the (cardiac) autonomous nervous system (ANS) plays a significant role in AF. Recent studies have shown that stimulating the ANS could have a beneficial effect on paroxysmal and postoperative AF. Consequently, this therapy could provide another viable target for treating persistent AF, as well. METHODS: The VAST-AF trial is a prospective, double-blinded, randomized, and sham-controlled clinical trial. One hundred and twenty patients diagnosed with persistent AF and cardioversion in sinus rhythm (SR) will be randomly assigned to either transcutaneous vagal nerve stimulation (tVNS) or sham treatment in a 1:1 ratio. The primary objective of this study is to examine whether a daily tVNS reduces the recurrence rate of AF. Secondary endpoints include quality of life, time to first AF recurrence and ECG parameters of the ANS. Follow-up is scheduled at 30 days, 3 and 6 months. After 3 months, stimulation is withdrawn, and patients evaluated regarding a still detectable effect of tVNS. CONCLUSION: The VAST-AF trial represents the first randomized and sham-controlled study to investigate the potential benefits of transcutaneous vagal nerve stimulation on the recurrence of atrial fibrillation. Patients with persistent atrial fibrillation and successful electrical cardioversion will be assessed. A decrease in the rate of recurrence and consecutive hospitalizations could decidedly enhance the quality of life of patients and decrease healthcare expenses. Nevertheless, it does not compete with treatments such as catheter ablation, but rather serves as an additional tool in the armamentarium of the electrophysiologist.

Proteomics biomarker discovery for individualized prevention of familial pancreatic cancer using statistical learning.

BACKGROUND: The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve the results' interpretability. METHODS: We applied model-based gradient boosting and adaptive lasso to deal with the small, unbalanced study design via simultaneous variable selection and model fitting. In addition, we used stability selection to identify a stable subset of selected biomarkers and, as a result, obtain even more interpretable results. In each step, we compared the performance of the different analytical pipelines and validated our approaches via simulation scenarios. RESULTS: In the simulation study, model-based gradient boosting showed a more accurate prediction performance in the small, unbalanced, and high-dimensional datasets than adaptive lasso and could identify more relevant variables. Furthermore, using model-based gradient boosting, we discovered a subset of promising serum biomarkers that may potentially improve the current screening procedure of FPC. CONCLUSION: Advanced statistical learning methods helped us overcome the shortcomings of an unbalanced study design in a valuable clinical dataset. The discovered serum biomarkers provide us with a clear direction for further investigations and more precise clinical hypotheses regarding the development of FPC and optimal strategies for its early detection.