Aims Timing and surgical strategies in acute infective endocarditis are still questionable. We sought to investigate clinical outcomes of patients undergoing mitral valve repair (MVR) compared with mitral valve replacement [mitral valve prosthesis (MVP)] for acute infective endocarditis. Methods From 2004 to 2019, 109 consecutive patients with acute mitral valve infective endocarditis were retrospectively investigated. Patients were divided into two groups according to surgical strategy: MVR 53/109 (48.6%) versus MVP 56/109 (51.4%). Primary end points were in-hospital mortality and overall survival at 10 years. Secondary end point was the freedom from infective endocarditis relapse. Results Our institutional surgical approach for infective endocarditis allowed us to achieve MVR in 48.6% of patients. Hospital mortality was comparable between the two groups [MVR: 1/53 (1.9%) versus MVP: 2/56 (3.6%), P = 1.000]. Overall 10-year survival was 80.0 ± 14.1 and 77.2 ± 13.5% for MVR and MVP, respectively (P = 0.648). MVR showed a lower incidence of infective endocarditis relapse compared with MVP (MVR: 93.6 ± 7.1 versus MVP: 80.9 ± 10.8%, P = 0.041). At Cox regression, infective endocarditis relapse was an independent risk factor for death (hazard ratio 4.03; 95% confidence interval 1.41–11.52; P = 0.009). Conclusion The tendency to postpone surgery in stable patients with mitral infective endocarditis allowed achievement of MVR in almost 50% of patients. Although repair remains the approach of choice in our institution, no differences between MVR and MVP were reported in terms of early/late survival. However, MVP had a higher incidence of infective endocarditis relapse that represents an independent risk of mortality.

In industrial applications, the continuously growing development of multi-sensor approaches, together with the trend of creating data-rich environments, are straining the effectiveness of the traditional Statistical Process Control (SPC) tools. Industrial data streams frequently violate the statistical assumptions on which SPC tools are based, presenting non-normal or even mixture distributions, strong autocorrelation and complex noise patterns.\ud \ud To tackle these challenges, novel nonparametric approaches are required. Machine learning techniques are suitable to deal with distributional assumption violations and to cope with complex data patterns. Recent studies showed that those methods can be used in quality control problems by exploiting only in-control data for training (such a learning paradigm is also known as “one-class-classification”).\ud \ud In recent studies, the use of distribution-free multivariate SPC methods was proposed, based on unsupervised statistical learning tools, pointing out the difficulty of defining suitable control regions for non-normal data. In this paper, a Self-Organizing Map (SOM) based monitoring approach is presented. The SOM is an automatic data-analysis method, widely applied in recent works to clustering and data exploration problems. A very interesting feature of this method consists of its capability of providing a computationally efficient way to estimate a data-adaptive control region, even in the presence of high dimensional problems. Nevertheless, very few authors adopted the SOM in an SPC monitoring strategy. The aim of this work is to exploit the SOM network architecture, and proposing a network design approach that suites the SPC needs. A comparison study is presented, in which the process monitoring performances are compared against literature benchmark methods. The comparison framework is based on both simulated data and real data from a roll grinding application