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Background: predicting the 1-year survival of patients undergoing transcatheter aortic valve implantation (TAVI) is indispensable for managing safe early discharge strategies and resource optimization., Methods: Routinely acquired data (134 variables) were used from 629 patients, who underwent transfemoral TAVI from 2012 up to 2018. Support vector machines, neuronal networks, random forests, nearest neighbour and Bayes models were used with new, previously unseen patients to predict 1-year mortality in TAVI patients. A genetic variable selection algorithm identified a set of predictor variables with high predictive power., Results: Univariate analyses revealed 19 variables (clinical, laboratory, echocardiographic, computed tomographic and ECG) that significantly influence 1-year survival. Before applying the reject option, the model performances in terms of negative predictive value (NPV) and positive predictive value (PPV) were similar between all models. After applying the reject option, the random forest model identified a subcohort showing a negative predictive value of 96% (positive predictive value = 92%, accuracy = 96%)., Conclusions: Our model can predict the 1-year survival with very high negative and sufficiently high positive predictive value, with very high accuracy. The "reject option" allows a high performance and harmonic integration of machine learning in the clinical decision process.

The COVID-19 pandemic is placing a heavy burden on healthcare systems worldwide with the risk that acute cardiovascular diseases are treated too late. The present study aims to analyze patients with acute coronary syndrome in the current pandemic. A total of 966 patients (2019 n = 463, 2020 n = 503) can be evaluated. A comparison of patient care during and before the COVID-19 pandemic was made in terms of patient characteristics and pre- and in-hospital processes. Another aim is to show how many patients seek clinical care at a late stage of the disease. After Lockdown in Germany at week 12, 2020, there was a significant decrease in patients with an acute coronary syndrome (ACS), significant for STEMI cases in the first weeks after Lockdown (calendar week 13-16 2019 n = 43, 2020 n = 30; p = 0.02). The time from pain to first medical contact (time to FMC) is significantly extended during Lockdown, while internal clinical processes are unchanged. The rate of subacute myocardial infarction is numerically, but not significantly increased in calendar weeks 15, 2020 ( p = 0.40) and 16 ( p = 0,19). In addition, elderly patients avoid treatment for multifactorial reasons, and the longer overall pain to FMC may impact long-term mortality.

Background: The vascular access in transcatheter aortic valve replacement (TAVR) was initially dominated by a surgical approach. Meanwhile, percutaneous closure systems became a well-established alternative. The aim of this study was to compare the clinical outcome between the two approaches., Methods: In this retrospective study, we observed 787 patients undergoing a TAVR-Procedure between 2013 and 2019. Of those, 338 patients were treated with surgical access and 449 with the Perclose ProGlide™-System (Abbott, Chicago, IL, USA). According to the Bleeding Academic Research Consortium (BARC) and Valve Academic Research Consortium (VARC) criteria, the primary combined endpoints were defined., Results: Overall hospital mortality was 2.8% with no significant difference between surgical (3.8%) and percutaneous (2.2%) access ( p = 0.182). Major vascular complications or bleeding defined as the primary combined endpoint was not significantly different in either group (Surgical group 5.3%, ProGlide group 5.1%, p = 0.899). In the ProGlide group, women with pre-existing peripheral artery disease (PAD) were significantly more often affected by a vascular complication ( p = 0.001 for female sex and p = 0.03 for PAD)., Conclusions: We were able to show that the use of both accesses is safe. However, the surgical access route should also be considered in case of peripheral artery disease.