No abstract

An increase in the number of patients with end stage heart failure is leading to increased use of ventricular assist devices (VAD). However, sometimes the optimal time point for implantation of left ventricular or biventricular support remains unclear. Data analysis using an electronic database may help to make the decision making process more precise and thus improve outcome. However, it is not easy to find a balance between sufficient comprehensiveness of the data, which are selected from a huge amount of available information, and practicability of database maintenance and data analysis. We developed the Assist Database based on Access for Windows. The Assist Database consists of five main parts: (1) demographic and admission data, diagnosis, goal, and type of VAD; (2) preoperative period; (3) postoperative period up to 30 days; (4) follow-up period; and (5) statistical evaluation. The preoperative and postoperative parts include hemodynamic data; ventilatory support; laboratory results; results from echocardiographic, neurologic, pathologic, and other examinations; medication; and complications. The follow-up part documents readmissions, complications, and outcome. From April 1987 to October 2002, eight different types of VAD were implanted in 654 patients in our institution. Their data were retrospectively added to the Assist Database using medical records and different previously used electronic databases. Since the Assist Database came into routine use, it has been supplied daily with selected data of current patients. On the data entry level, the data arising from medical records are entered either manually via standard forms or automatically from other electronic documentation systems used in our hospital in routine patient care to collect laboratory results, demographic data, blood transfusion data, and operative data and from electronic patient charts via interfaces. The structure of the database is designed to facilitate the data analysis level. The database presented is one of three databases united to form a network. The structure of the Assist Database facilitates comprehensive, time saving data collection, which allows different online data analyses. These analyses may affect the decision making process and thus improve outcome. However, achieving a balance between the volume of available information, the time consumed, and the relevance of the data for further analysis remains difficult. The Assist Database should include information relevant for the decision making process and for the prediction of outcome. In particular, data collection should be focused on patients' preoperative condition and on postoperative organ function and quality of life. Further, different databases (for patients with congestive heart failure, assist device patients, and transplanted patients) should be unified to form a network to avoid the repeated collection of identical data, to save time, and to increase the quality of analysis. In the long-term, multicenter use of the Assist Database could be considered.

A quarter of the German heart surgery institutes use one or more RS methods. The most commonly used were the Cleveland Clinic Score and the Euro Score, followed by internally developed RS methods. RS methods were most frequently used for internal quality control. The degree of the severity of disease of the patients who presented for surgery could only be compared between a small number of institutes using the same RS.

All RSSs satisfactorily estimated the group risk for mortality. No RSS expressed sufficient validity to predict individuals with lethal outcome. In clinical use, CCS/Higgins proved the most practicable.

Objective: COVID-19 is a highly contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Preventing in-hospital infections is crucial to protect patients and hospital staff.Methods: At the very beginning of the COVID-19 pandemic, the German Heart Center initiated obligatory wearing of surgical face masks for patients and employees, SARS-CoV-2 screening for all patients, and symptom-based testing for employees. In addition, access restriction, closure of outpatient departments, and postponing non-urgent procedures were implemented with community-initiated regulations.Results: During the observation period (03/16/2020–04/27/2020), 1,128 SARS-CoV-2 tests were performed in 983 persons (1.1 tests/person; 589 in patients and 394 in hospital employees). Up to 60% of the clinical workforce was tested based on symptoms and risk (62.5% symptoms, 19.3% direct or indirect contact to known COVID-19, 4.5% returnee from risk area, 13.7% without specific reason). Patient testing for SARS-CoV-2 was obligatory (100% tested). The overall prevalence of positive tests during the observation period was 0.4% (n = 5 out of 1,128 tests performed). The incidence of new infections with SARS-CoV-2 was 0.5% (n = 5 out of 983 individuals; three healthcare workers, two patients). No nosocominal infections occurred, despite a mean number of 14.8 in-hospital contacts.Conclusion: Comprehensive SARS-CoV-2 testing and surgical face masks for patients and hospital staff, in addition to others measures, are key factors for the early detection of COVID-19 and to prevent spreading in the vulnerable hospital population.