Depression, anxiety, and quality of life as predictors of rehospitalization in patients with chronic heart failure.

BACKGROUND: Chronic heart failure (CHF) is a severe condition, often co-occurring with depression and anxiety, that strongly affects the quality of life (QoL) in some patients. Conversely, depressive and anxiety symptoms are associated with a 2-3 fold increase in mortality risk and were shown to act independently of typical risk factors in CHF progression. The aim of this study was to examine the impact of depression, anxiety, and QoL on the occurrence of rehospitalization within one year after discharge in CHF patients. METHODS: 148 CHF patients were enrolled in a 10-center, prospective, observational study. All patients completed two questionnaires, the Hospital Anxiety and Depression Scale (HADS) and the Questionnaire Short Form Health Survey 36 (SF-36) at discharge timepoint. RESULTS: It was found that demographic and clinical characteristics are not associated with rehospitalization. Still, the levels of depression correlated with gender (p ≤ 0.027) and marital status (p ≤ 0.001), while the anxiety values ​​were dependent on the occurrence of chronic obstructive pulmonary disease (COPD). However, levels of depression (HADS-Depression) and anxiety (HADS-Anxiety) did not correlate with the risk of rehospitalization. Univariate logistic regression analysis results showed that rehospitalized patients had significantly lower levels of Bodily pain (BP, p = 0.014), Vitality (VT, p = 0.005), Social Functioning (SF, p = 0.007), and General Health (GH, p = 0.002). In the multivariate model, poor GH (OR 0.966, p = 0.005) remained a significant risk factor for rehospitalization, and poor General Health is singled out as the most reliable prognostic parameter for rehospitalization (AUC = 0.665, P = 0.002). CONCLUSION: Taken together, our results suggest that QoL assessment complements clinical prognostic markers to identify CHF patients at high risk for adverse events. CLINICAL TRIAL REGISTRATION: The study is registered under http://clinicaltrials.gov (NCT01501981, first posted on 30/12/2011), sponsored by Charité - Universitätsmedizin Berlin.

The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy.

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.