Abstracts by keywords: hospitalization
The aim of this study was to investigate the prevalence and factors associated with in-hospital mortality in patients with heart failure (HF). This multicenter cross-sectional study was based on data from HF patients from fourteen hospitals across Germany. A total of 19,288 inpatients (15,431 patients) were included (mean age: 79.4 years, 49.9% female). Mortality was 9.7%, increasing from 4.2% in the age group ≤65 years to 15.1% in the age group >90 years. Left heart failure with symptoms at rest, acute renal failure, respiratory failure, pneumonia, and pleural effusion were associated with an increased risk of death. Paradoxically, several chronic cardiovascular and metabolic diseases—including hypertensive heart disease, essential hypertension, pulmonary heart disease, obesity, dyslipidemia, and diabetes—were associated with a lower risk of death as secondary diagnoses. While our study provides a comprehensive snapshot of in-hospital mortality in HF patients, it also highlights the need for further studies to better understand the complex interplay of various risk factors.
03.06.2024
Given the challenges of demographic change, the healthcare system is faced with the question of how to allocate resources efficiently and fairly. Only 20% of the insured are responsible for 81.2% of the hospital, medication and doctor expenses of the statutory health insurance (GKV) (1). Approximately 5% of patients account for 50% of utilization (Fig. 1). Almost half of these expenses relate to hospital treatment. In many cases, these are unavoidable and serve to ensure the safety and good quality of treatment for patients. A common reason for claiming benefits is the presence of a chronic illness. The top 10 reasons for hospitalization include diagnoses such as heart failure, angina, hypertension, and type 2 diabetes (1). The acute deterioration of chronic diseases such as diabetes or heart failure often leads to hospitalization. The risk of hospitalization increases significantly if a person has several chronic illnesses at the same time. In order to be able to assess a person's risk, risk prediction models are developed (predictive modeling). The aim of risk models is to avoid (re-)admissions to hospital, to prevent the incurrence of hospital costs and to achieve better health outcomes for the patient with intensive, cheaper medical intervention (2). Risk models can take a lot of information from the service sectors and master data into account in parallel and can be used to predict hospital stays for any reason as well as for specific diseases.