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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., Methods and Results: This retrospective, observational study analysed administrative data from inpatient cases hospitalized in 87 German Helios hospitals between 1 January 2016 and 31 August 2022. The first hospitalization of patients aged ≥18 years with a primary discharge diagnosis of HF, based on ICD-10 codes, were considered the index cases, and subsequent hospitalizations were considered as readmissions. Baseline characteristics and outcomes were stratified for co-morbid CKD using ICD-10-encoding from the index cases. Cox regression was utilized for readmission endpoints and in-hospital mortality. In total, 174 829 index cases (mean age 79 ± 15 years, 49.9% female) were included; of these, 55.0% had coexisting CKD. Patients with CKD were older, suffered from worse HF-related symptoms, had a higher co-morbidity burden, and in-hospital mortality was increased at index and during follow-up. Prevalent CKD was associated with higher rehospitalization rates and was an independent predictor for in-hospital death., Conclusions: Within this HF inpatient cohort from a multicentric German database, CKD was diagnosed in more than half of the patients and was associated with increased in-hospital mortality at baseline and during follow-up. Rehospitalizations were observed earlier and more frequently in patients with HF and co-morbid CKD., (© 2024 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Aims: Models predicting mortality in heart failure (HF) patients are often limited with regard to performance and applicability. The aim of this study was to develop a reliable algorithm to compute expected in-hospital mortality rates in HF cohorts on a population level based on administrative data comparing regression analysis with different machine learning (ML) models., Methods and Results: Inpatient cases with primary International Statistical Classification of Diseases and Related Health Problems (ICD-10) encoded discharge diagnosis of HF non-electively admitted to 86 German Helios hospitals between 1 January 2016 and 31 December 2018 were identified. The dataset was randomly split 75%/25% for model development and testing. Highly unbalanced variables were removed. Four ML algorithms were applied, and all algorithms were tuned using a grid search with multiple repetitions. Model performance was evaluated by computing receiver operating characteristic areas under the curve. In total, 59 125 cases (69.8% aged 75 years or older, 51.9% female) were investigated, and in-hospital mortality was 6.20%. Areas under the curve of all ML algorithms outperformed regression analysis in the testing dataset with values of 0.829 [95% confidence interval (CI) 0.814-0.843] for logistic regression, 0.875 (95% CI 0.863-0.886) for random forest, 0.882 (95% CI 0.871-0.893) for gradient boosting machine, 0.866 (95% CI 0.854-0.878) for single-layer neural networks, and 0.882 (95% CI 0.872-0.893) for extreme gradient boosting. Brier scores demonstrated a good calibration especially of the latter three models., Conclusions: We introduced reliable models to calculate expected in-hospital mortality based only on administrative routine data using ML algorithms. A broad application could supplement quality measurement programs and therefore improve future HF patient care., (© 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Aims: The Covid-19 pandemic affects care for cardiovascular conditions, but data on heart failure (HF) are scarce. This study aims to analyse HF care and in-hospital outcomes during the pandemic in Germany., Methods and Results: A total of 9452 HF admissions were studied using claims data of 65 Helios hospitals; 1979 in the study period (13 March 30 April 2020) and 4691 and 2782 in two control periods (13 March to 30 April 2019 and 1 January to 12 March 2020). HF admissions declined compared with both control periods by 29-38%. Cardiac resynchronization therapy was implanted in 0.55% during the study period, 0.32% [odds ratio (OR) 1.66, 95% confidence interval (CI) 0.68-4.04, P = 0.27] in the previous year and 0.43% (OR 1.35, 95% CI 0.64-2.84, P = 0.43) in the same year control. Intensive care treatment was 6.22% during the study period, 4.49% in the previous year (OR 1.46, 95% CI 1.13-1.89, P < 0.01), and 5.27% in the same year control (OR 1.19, 95% CI 0.96-1.49, P = 0.12). Length of hospital stay was 7.0 ± 5.0 days in the study and 7.8 ± 5.6 (P < 0.01) and 7.3 ± 5.1 days (P = 0.07) in the control periods. In-hospital mortality was 7.0% in the study and 5.5% in both control periods (P < 0.05)., Conclusions: During the early phase of the Covid-19 pandemic in Germany, HF treatment pathways seem not to be affected, but hospital stay shortened and in-hospital mortality increased. As the pandemic continues, this early signal demands close monitoring and further investigation of potential causes., (© 2020 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of the European Society of Cardiology.)