Objectives To analyze the risk factors for cardiovascular disease (CVD) in patients with chronic obstructive pulmonary disease (COPD) of different severities. Methods The study included 50 patients with mild-to-moderate COPD and 50 with severe-to-very severe COPD admitted between January 2014 and January 2016. Comorbidities were recorded on the basis of data obtained from medical charts and clinical evaluations. The Charlson comorbidity index was calculated, and the Hospital Anxiety and Depression Scale (HADS) score was determined in each subject. Results There were more prevalences of smoking, depression and dyslipidemia in the patients with mild-to-moderate COPD than those with severe-to-very severe COPD (all P<0.001). The prevalences of high blood pressure, diabetes mellitus, alcoholism, and chronic heart failure were not different significantly between the two groups. The Charlson comorbidity index and HADS scores were not different between the two groups. Conclusions Comorbidities are fairly common in COPD regardless of its severity. Certain risk factors for CVD, as smoking, dyslipidemia, and depression, appear to be more prevalent in patients with mild-to-moderate COPD.
ObjectivesTo explore the safety and efficacy of beta-blockers (BBs) in patients with chronic obstructive pulmonary disease (COPD) and its effect on prognosis. MethodsThe data of 366 patients with acute exacerbation of COPD in this department were analyzed retrospectively. The use rate and related events of BBs were evaluated, including comorbidity, indications, contraindications and related clinical indicators. ResultsOf the 366 patients, 156 (42.6%) had at least one indication of the use of BBs, but only 53 (34.0%) of these patients used BBs, and 61 patients (39.1%) had no contraindications but did not use BBs. At admission, 72 patients (19.7%) were treated with BBs, 177 (45.6%) with antiplatelet drugs, 145 (39.6%) with statins, and angiotensin converting enzyme inhibitor/angiotensin II receptor blocker was used in 168 (45.9%) patients. Twenty-five patients (6.8%) had ischemic heart disease during hospitalization. Fifty-seven patients (15.6%) had cardiovascular and cerebrovascular events during admission. The patients with cardiovascular and cerebrovascular events had longer hospitalization (P<0.01) and higher in-hospital mortality (P=0.02). ConclusionsPatients with COPD have a clear indication of BBs use, but the clinical use rate is still very low. Further research is needed to explore the prescription disorders of BBs in patients with COPD.
It has been found that the incidence of cardiovascular disease in patients with lower limb amputation is significantly higher than that in normal individuals, but the relationship between lower limb amputation and the episodes of cardiovascular disease has not been studied from the perspective of hemodynamics. In this paper, numerical simulation was used to study the effects of amputation on aortic hemodynamics by changing peripheral impedance and capacitance. The final results showed that after amputation, the aortic blood pressure increased, the time averaged wall shear stress of the infrarenal abdominal aorta decreased and the oscillatory shear index of the left and right sides was asymmetrically distributed, while the time averaged wall shear stress of the iliac artery decreased and the oscillatory shear index increased. The changes above were more significant with the increase of amputation level, which will result in a higher incidence of atherosclerosis and abdominal aortic aneurysm. These findings preliminarily revealed the influence of lower limb amputation on the occurrence of cardiovascular diseases, and provided theoretical guidance for the design of rehabilitation training and the optimization of cardiovascular diseases treatment.
With the development of science and technology, artificial intelligence is gradually integrated into every aspect of daily life and the medical field is no exception. Cardiovascular diseases, as the first killer to global health, is the focus of new technologies and methods. In this study, the application of computer vision, natural language processing, robotics and machine learning in cardiovascular disease studies were reviewed and prospected, in order to promote the development for new technologies and applications in the future.
Cardiovascular disease is the leading cause of death worldwide, accounting for 48.0% of all deaths in Europe and 34.3% in the United States. Studies have shown that arterial stiffness takes precedence over vascular structural changes and is therefore considered to be an independent predictor of many cardiovascular diseases. At the same time, the characteristics of Korotkoff signal is related to vascular compliance. The purpose of this study is to explore the feasibility of detecting vascular stiffness based on the characteristics of Korotkoff signal. First, the Korotkoff signals of normal and stiff vessels were collected and preprocessed. Then the scattering features of Korotkoff signal were extracted by wavelet scattering network. Next, the long short-term memory (LSTM) network was established as a classification model to classify the normal and stiff vessels according to the scattering features. Finally, the performance of the classification model was evaluated by some parameters, such as accuracy, sensitivity, and specificity. In this study, 97 cases of Korotkoff signal were collected, including 47 cases from normal vessels and 50 cases from stiff vessels, which were divided into training set and test set according to the ratio of 8 : 2. The accuracy, sensitivity and specificity of the final classification model was 86.4%, 92.3% and 77.8%, respectively. At present, non-invasive screening method for vascular stiffness is very limited. The results of this study show that the characteristics of Korotkoff signal are affected by vascular compliance, and it is feasible to use the characteristics of Korotkoff signal to detect vascular stiffness. This study might be providing a new idea for non-invasive detection of vascular stiffness.