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Extracorporeal life support systems (ECLS) are life-sustaining measures for severe cardiovascular diseases, serving as bridging treatment either until cardiovascular function is restored or alternative treatment, such as heart transplantation or the implantation of permanent ventricular assist devices is performed. Given the insufficient evidence and frequent urgency of implantation without initial patient consent, the ethical challenges and psychological burden for patients, relatives and the interprofessional intensive care team are significant. As with any treatment, an appropriate therapeutic goal for ECLS treatment based on the indications and patient informed consent is mandatory. In order to integrate the necessary ethical considerations into everyday clinical practice, a structured algorithm for handling ECLS is proposed here, which takes ethical aspects into due account. [ABSTRACT FROM AUTHOR]

Big data and artificial intelligence are buzzwords that everyone is talking about and yet always provide a touch of science fiction to the scenery. What is the status of these topics in anesthesia? Are the first robots already rolling through the corridors while doctors are getting bored as all the work has been done? Spoiler alert! We are still far away from achieving this. Initially, paper charts and analogue notes stand in the way of comprehensive digitization. Source systems need to be merged and data standardized, harmonized and validated. Therefore, the friendly android that is rolling towards us, waving and holding a freshly brewed cup of coffee in our thoughts will have to wait; however, a glimpse of the future is already evident in some clinics and the first promising developments are already showing what could be the standard tomorrow. Learning algorithms calculate the length of stay individually for each patient in the intensive care unit (ICU), reducing negative consequences such as readmission and mortality. The field of ultrasound technology for regional anesthesia and closed-loop anesthesia systems is also demonstrating the benefits of artificial intelligence (AI)-assisted technologies in practice. The efforts are diverse and ambitious but they repeatedly collide with privacy challenges and significant capital expenditure, which weigh heavily on an already financially strained healthcare system; however, anyone who listens carefully to the medical staff knows that robots are not what they would expect and the buzzwords big data and artificial intelligence might be less science fiction than initially assumed. [ABSTRACT FROM AUTHOR]

Background: The present guidelines ( http://leitlinien.net ) focus exclusively on cardiogenic shock due to myocardial infarction (infarction-related cardiogenic shock, ICS). The cardiological/cardiac surgical and the intensive care medicine strategies dealt with in these guidelines are essential to the successful treatment and survival of patients with ICS; however, both European and American guidelines on myocardial infarction and heart failure and also position papers on cardiogenic shock focused mainly on cardiological aspects.Methods: Evidence on the diagnosis, monitoring and treatment of ICS was collected and recommendations compiled in a nominal group process by delegates of the German Cardiac Society (DGK), the German Society for Medical Intensive Care Medicine and Emergency Medicine (DGIIN), the German Society for Thoracic and Cardiovascular Surgery (DGTHG), the German Society for Anaesthesiology and Intensive Care Medicine (DGAI), the Austrian Society for Internal and General Intensive Care Medicine (ÖGIAIM), the Austrian Cardiology Society (ÖKG), the German Society for Prevention and Rehabilitation of Cardiovascular Diseases (DGPR) and the German Interdisciplinary Association for Intensive Care and Emergency Medicine (DIVI), under the auspices of the Working Group of the Association of Medical Scientific Societies in Germany (AWMF). If only poor evidence on ICS was available, general study results on intensive care patients were inspected and presented in order to enable analogue conclusions.Results: A total of 95 recommendations, including 2 statements were compiled and based on these 7 algorithms with defined instructions on the course of treatment. [ABSTRACT FROM AUTHOR]

How to make interdisciplinarity work? In the present paper, two possible preconditions are discussed, on the hand interdisciplinarity within one person, on the other hand a recognized and admitted problem within a discipline that could be addressed or even solved with knowledge from a different discipline. Interdisciplinarity within one person can not be fostered through double degrees or pleas for openness, but by the independent pursuit of a question or topic across disciplinary borders. Similarly, fruitful exchange among disciplines can not be forced, but needs a pressing problem within one discipline that could be solved from another discipline's perspective. However, a person with inner interdisciplinarity is needed to make the connection: to know about the problem in one discipline and find the solution in another. These travelers across discipline borders, however, might feel like failures on their way as the social system of science rewards staying at home. [ABSTRACT FROM AUTHOR]