Non-invasive risk stratification: prognostic implications of exercise testing

[Sudden cardiac death in diabetes mellitus]

Sudden cardiac death (SCD) represents one of the most frequent causes of death in patients with diabetes. In contrast to patients without diabetes it has not been significantly reduced despite improvements in the treatment of acute myocardial infarction and long-term treatment of cardiovascular diseases as well as diabetes mellitus. Several mechanisms can be responsible for the high incidence of SCD in diabetics: 1. arrhythmogenic effects mediated via cardiac autonomic neuropathy, repolarization disturbances or sympathetic tone activation (hypoglycemia), 2. myocardial ischemia due to atherosclerosis, endothelial dysfunction, platelet aggregation or thrombophilic effects, 3. myocardial disease due to inflammation, fibrosis, associated hypertension or uremia and 4. potassium imbalance due to diabetic nephropathy or hypoglycemia. This review introduces concepts of mechanisms that are responsible for SCD in patients with diabetes. Treatment of patients with diabetes should primarily consider a systematic assessment of any deterioration of this chronic disease and of complications at an early stage. Cardiovascular drug treatment corresponds to that of non-diabetics. In antidiabetic treatment drugs with a low risk of hypoglycemia should be preferred. Treatment with implantable cardioverter defibrillators (ICD) also combined with cardiac resynchronization therapy () demonstrated a high life-saving potential particularly in patients with diabetes.

Investigation on cardiovascular risk prediction using physiological parameters

Cardiovascular disease (CVD) is the leading cause of death worldwide. Early prediction of CVD is urgently important for timely prevention and treatment. Incorporation or modification of new risk factors that have an additional independent prognostic value of existing prediction models is widely used for improving the performance of the prediction models. This paper is to investigate the physiological parameters that are used as risk factors for the prediction of cardiovascular events, as well as summarizing the current status on the medical devices for physiological tests and discuss the potential implications for promoting CVD prevention and treatment in the future. The results show that measures extracted from blood pressure, electrocardiogram, arterial stiffness, ankle-brachial blood pressure index (ABI), and blood glucose carry valuable information for the prediction of both long-term and near-term cardiovascular risk. However, the predictive values should be further validated by more comprehensive measures. Meanwhile, advancing unobtrusive technologies and wireless communication technologies allow on-site detection of the physiological information remotely in an out-of-hospital setting in real-time. In addition with computer modeling technologies and information fusion. It may allow for personalized, quantitative, and real-time assessment of sudden CVD events.