Lipid-lowering treatment in secondary prevention of ischaemic cerebrovascular disease

Implications of Ezetimibe in Combination with Low- to Moderate-Intensity Atorvastatin Adjuvant Aspirin Therapy for Cerebrovascular Disease

Objective

To analyze the significance of ezetimibe in combination with low- to moderate-intensity atorvastatin adjuvant aspirin therapy for cerebrovascular disease.

Methods

110 patients with cerebrovascular disease treated in our hospital from June 2020 to June 2021 were selected and divided into 55 patients in the control group and 55 patients in the study group according to the lottery method. After a comprehensive examination, patients in the two groups should be given aspirin for treatment; the control group was treated with conventional dose of atorvastatin on top of the above, and the study group was given ezetimibe and medium-low-dose atorvastatin on top of aspirin treatment, activities of daily living (ADL) score, carotid artery intima-media thickness, lipid level, coagulation level, clinical effect, and adverse rate of the two groups which were tested and compared.

Results

After treatment, ADL score, high-density leptin cholesterol (HDL-C), and ATIII levels increased, while carotid artery media thickness, triglyceride (TG), total cholesterol (TC), low-density leptin cholesterol (LDL-C), DD, PC, and hs-CRP levels decreased (P < 0.05). After treatment, ADL score, HDL-C, and ATIII levels were higher in the study group. The levels of carotid media thickness, TG, TC, LDL-C, DD, PC, and hs-CRP were significantly lower (P < 0.05). The clinical effect of the study group was outstanding (P < 0.05). The defect rate of the study group was lower than that of the control group, but there was no difference (P < 0.05).

Conclusion

Ezetimibe combined with medium- and low-intensity atorvastatin with aspirin in the treatment of cerebrovascular diseases can effectively improve the coagulation function of patients, reduce the level of inflammatory factors in patients, and improve the level of blood lipids in patients, with high safety and worthy of clinical application.

An Application of Deep Belief Networks in Early Warning for Cerebrovascular Disease Risk

To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention, and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.

Prediction Model of Ischemic Stroke Recurrence Using PSO-LSTM in Mobile Medical Monitoring System

Aiming at the problems of low prediction accuracy and low sensitivity of traditional ischemic stroke recurrence prediction methods, which limits its application range, by introducing an adaptive particle swarm optimization (PSO) algorithm into the Long and Short-Term Memory (LSTM) model, a prediction model of ischemic stroke recurrence using deep learning in mobile medical monitoring system is proposed. First, based on the clustering idea, the particles are divided into local optimal particles and ordinary particles according to the characteristic information and distribution of different particles. By updating the particles with different strategies, the diversity of the population is improved and the problem of local optimal solution is eliminated. Then, by introducing the adaptive PSO algorithm into the LSTM, the PSO-LSTM prediction model is constructed. The optimal super parameters of the model are determined quickly and accurately, and the model is trained combined with the patient's clinical data. Finally, by using SMOTE method to process the original data, the imbalance of positive and negative sample data is eliminated. Under the same conditions, the proposed PSO-LSTM prediction model is compared with two traditional LSTM models. The results show that the prediction accuracy of PSO-LSTM model is 92.0%, which is better than two comparison models. The effective prediction of ischemic stroke recurrence is realized.