Patient Characteristics and Outcomes of Hospitalized Chronic Kidney Disease Patients with and without Type 2 Diabetes Mellitus: Observations from the German Claims Data-Based Cohort of the CaReMe-CKD Multinational Study.

Introduction: Type 2 diabetes mellitus (T2DM) is a leading cause of chronic kidney disease (CKD) globally. Both conditions substantially worsen patients' prognosis. Current data on German in-hospital CKD cohorts are scarce.

Characteristics and outcome of hospitalized patients with heart failure stratified for chronic kidney disease.

Aims: Heart failure (HF) and chronic kidney disease (CKD) place significant challenges on the healthcare system, and their co-existence is associated with shared adverse outcomes. The multinational CaReMe project was initiated to provide contemporary, real-world epidemiological data on cardiovascular and reno-metabolic diseases. Utilizing data from the German CaReMe cohort, we characterize a multicentric HF population and describe in-hospital outcomes stratified for co-morbid CKD.

Retrospective, Observational Analysis on the Impact of SARS-CoV-2 Variant Omicron in Hospitalized Immunocompromised Patients in a German Hospital Network-The VISAGE Study.

Aims: Endemic SARS-CoV-2 infections still burden the healthcare system and represent a considerable threat to vulnerable patient cohorts, in particular immunocompromised (IC) patients. This study aimed to analyze the in-hospital outcome of IC patients with severe SARS-CoV-2 infection in Germany.

Methods: This retrospective, observational study, analyzed administrative data from inpatient cases ( = 146,324) in 84 German Helios hospitals between 1 January 2022 and 31 December 2022 with regard to in-hospital outcome and health care burden in IC patients during the first 12 months of Omicron dominance.

Artificial intelligence-based identification of left ventricular systolic dysfunction from 12-lead electrocardiograms: external validation and advanced application of an existing model.

Aims: The diagnostic application of artificial intelligence (AI)-based models to detect cardiovascular diseases from electrocardiograms (ECGs) evolves, and promising results were reported. However, external validation is not available for all published algorithms. The aim of this study was to validate an existing algorithm for the detection of left ventricular systolic dysfunction (LVSD) from 12-lead ECGs.