Klinikum Konstanz

Climate change impacts are increasingly manifested at local scales, where mitigation and adaptation strategies are implemented. Despite the growing wealth of available climate data and services, their effective usage in local climate impact assessment and decision-making processes for mitigation and adaptation planning remains limited due to scale mismatches, computational constraints, complexity, and usability barriers for non-domain experts. Addressing these challenges requires both advanced computational methods and improved access to climate data and analysis tools. The EU Horizon project, FOCAL, bridges the gap between data, services, and their users by implementing an open compute platform that combines intelligent workflow management with high-performance computing (HPC) resources to allow for an efficient exploration of climate data on a local scale. In addition, innovative artificial intelligence (AI) tools are developed and made available to enhance climate data analysis in terms of speed, robustness, pattern detection, and localization; thereby expanding the toolkit of climate data analysis and impact assessment methods. A main objective of FOCAL is to support science-based, actionable decision-making processes in forestry and urban planning through its provided tools. In a co-design process involving developers and potential platform users from two forest pilot regions with contrasting ecological and management contexts (Forest Pilots) as well as a pilot city (Urban Pilot), web applications for intuitive user-platform-interaction and workflows, grounded in state-of-the-art climate science, to address concrete user questions in forestry and urban planning have been specified. As a result, decision makers can efficiently use climate data for the development of climate adaptation strategies.This contribution focuses on the Urban Pilot, implemented for the pilot city Constance (Baden-Württemberg, southern Germany), located at the western end of Lake Constance. Three core workflows have been developed:1) Regional climate change workflow: provision of robust regional climate change information for the past and the future under different global warming levels for urban areas, based on regional climate model and localized climate data, serving multi-sectoral local climate impact assessments;2) Urban hot and cool spot workflow: detection and high-spatial-resolution visual exploration of hot and cool spots in urban environments, supporting exposure assessment by integrating additional data (e.g., population or infrastructure data), risk assessment, and the planning of urban heat resilience measures and cooling spaces;3) Urban blue spot workflow: identification of blue spots (rainfall accumulation hazards) and provision of blue spot data in urban landscapes using processed precipitation data and extreme precipitation scenarios, supporting applications in hydrological modeling, flood risk management, and climate adaptation.By leveraging HPC-based data processing and AI-assisted analysis, these workflows translate complex climate data into actionable, locally relevant information. While demonstrated for the pilot city Constance, the methods and workflows are transferable to other urban areas, contributing to scalable and reproducible climate services.

Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learning convolutional neural networks (DL-CNNs) have demonstrated dermatologist-level accuracy in identifying CM. We hypothesized that support from DL-CNN could increase dermatologists' accuracy in differentiating CM from MelSim. This cross-sectional reader study analyzed the diagnostic performance of a DL-CNN and 27 dermatologists for 200 skin lesions (100 CM, 100 MelSim). The DL-CNN assigned malignancy scores ranging from 0 to 1 (> 0.5 indicating malignancy). Dermatologists assessed lesions across three levels: (I) dermoscopy only, (II) full case information (dermoscopy, close-up images, metadata), and (III) full case information plus DL-CNN scores. Primary outcomes were sensitivity, specificity, and ROC-AUC of dermatologists with or without DL-CNN-support (level-II versus -III). The DL-CNN and dermatologists in level-II showed a comparable sensitivity (95 % CI) of 90.0 % (82.6-94.5 %) and 90.1 % (86.9-93.2 %, p = 0.153), respectively. However, the DL-CNN's specificity (67.0 % (57.3-75.4 %) versus 73.2 % (69.1-77.3 %)) and ROC-AUC (0.889 (0.845-0.932) versus 0.951 (0.920-0.982)) were significantly lower than for dermatologists (all p < 0.01). When dermatologists integrated DL-CNN predictions (level-III) their sensitivity increased to 91.4 % (88.3-94.5 %, p < 0.001) without markedly changing specificity (74.2 % (70.6-77.7 %, p = 0.435)) or ROC-AUC (0.954 (0.927-0.982, p = 0.581)). Collaboration with a DL-CNN slightly improved dermatologists' diagnostic accuracy in a mixed CM and MelSim dataset, by increasing sensitivity without a loss of specificity. The DL-CNN's level of sensitivity in this difficult-to-diagnose dataset underlines the potential as an assistant diagnostic tool.

In an online survey conducted by the Gastrointestinal and Abdominal Imaging Working Group of the German Radiological Society (DRG), the current state regarding enteral contrast administration in computed tomography (CT) of the abdomen in Germany was established and critically evaluated. The results of the survey are intended to serve as a basis for expert recommendations on enteral contrast administration in abdominal CT.DRG members were invited to take part in the online survey by email, newsletter, and via the DRG online portal. The survey was available for 70 days. It comprised 61 questions on enteral contrast administration in various clinical scenarios, with an average operation approximately 10 to 15 minutes. The results were analyzed using descriptive statistical methods.1,001 radiologists completed the survey. Of the participants, 67% worked in a hospital and 33% in private practice or a medical care center. Furthermore, 52% of the participants were senior physicians or board-certified radiologists. Enteral contrast administration is being performed selectively for various indications, but with considerable differences in technique and regularity. A lack of substantial diagnostic gain, as well as time-related or organizational factors, were the main reasons cited against enteral contrast administration.The results of the survey demonstrate a heterogeneous, selective, and indication-specific implementation of enteral contrast administration. There are considerable differences regarding technical implementation and realization. There is a strong desire for generally applicable recommendations along with their integration in respective SOPs. · Enteral contrast administration in abdominal CT is being performed very heterogeneously.. · Guidelines lack specifications regarding enteral contrast administration.. · There is a strong desire for recommendations and their implementation in relevant SOPs.. · Stadelmann J, Grenacher L, Juchems MS et al. Enteral Contrast Administration in Abdominal Computed Tomography: Status Quo in Germany. Rofo 2025; DOI 10.1055/a-2594-7579.

To determine characteristics and clinical outcomes of German patients with coronary artery disease (CAD) with or without peripheral artery disease (PAD) who initiated dual pathway inhibition (DPI) using rivaroxaban 2.5mg twice daily plus aspirin 100mg once daily in clinical practice in Germany and to compare those with the results for CAD patients of the COMPASS trial. XATOA was an international prospective registry with a mean follow-up period of 15months. There were 1641 German CAD patients included, of which 747 patients (45.5%) had CAD only and 894 patients (54.5%) had both CAD and PAD. Baseline characteristics (age, sex, medical history, prior medications) were similar between the subgroups of German CAD patients and comparable to CAD patients of COMPASS. The incidence of major adverse cardiovascular events (MACE) was comparable between CAD patient subgroups of XATOA Germany (only CAD patients: 3.7%, CAD + PAD patients: 3.9%) and was similar to the incidence for CAD patients overall in XATOA and in COMPASS (All CAD patients XATOA: 3.8% vs. 4.2% COMPASS CAD). Incidence for major bleeding was markedly lower in CAD patients of XATOA Germany compared to COMPASS CAD patients (All CAD patients XATOA: 1.1% vs. 3.2% in COMPASS CAD). In this real world experience among German patients with CAD enrolled in XATOA, DPI with rivaroxaban and aspirin was associated with low incidence of MACE and major bleeding.

Zusammenfassung Einwilligungsunfähige Patienten können bislang nur durch Gesundheitsbevollmächtigte oder gerichtlich bestellte Betreuer vertreten werden. Diese Rechtslage ändert sich mit Wirkung zum 01.01.2023: Seitdem sind Ehegatten (und eingetragene Lebenspartner) kraft Gesetzes automatisch (also ohne gesonderten Bestellungsakt) in sämtlichen Angelegenheiten der Gesundheitssorge vertretungsberechtigt [1]. Ziel des Ehegattennotvertretungsrechts ist die Stärkung der Autonomie der Patienten, die Angleichung der gesetzlichen Regelung an das Verständnis der Rechte von Ehegatten in der breiten Bevölkerung sowie eine Entlastung der Betreuungsgerichte.