Efficacy of neurointervention combined with intravenous thrombolysis in the treatment of ischemic cerebrovascular disease and its influence on neurological function and prognosis of patients

Clinical Effect of Digital Subtraction Angiography Combined with Neurointerventional Thrombolysis for Acute Ischemic Cerebrovascular Disease and Its Influence on Vascular Endothelial Function and Oxidative Stress

Objective

Ischemic cerebrovascular disease is a commonly seen vascular disorder in clinical practice. Given the difficulty of drug therapy to achieve ideal curative effects, interventional therapy has gradually become the preferred treatment for the disease. This research primarily discusses the short-term efficacy of digital subtraction angiography- (DSA-) guided neurointerventional thrombolysis for acute ischemic cerebrovascular disease (AICVD) and its influence on vascular endothelial function (VEF) and oxidative stress (OS).

Methods

All the clinical data of 162 patients diagnosed with AICVD and treated between June 2019 and December 2021 were collected and analyzed retrospectively. They were assigned to two cohorts according to the difference in interventional methods: a conventional group (CG) given recombinant tissue plasminogen activator (rt-PA) therapy and an observation group (OG) intervened by DSA-guided neurointerventional thrombolysis. The two groups were compared with respect to short-term treatment efficacy, the National Institutes of Health Stroke Scale (NIHSS) score, cerebral hemodynamics, and VEF and OS indexes.

Results

The short-term efficacy was better in OG (93.98%) than in CG (82.28%). After treatment, the NIHSS score decreased in both cohorts with obvious differences within the group at different time points, and the posttreatment NIHSS score was lower in OG as compared to CG. OG had higher Q m and V m while lower W v, Z cv, and R v than CG. Higher endothelial-dependent flow-mediated dilatation (FMD) was observed in OG, as well as lower ankle-brachial index (ABI) and pulse wave velocity (PWV). And the posttreatment MDA was lower while SOD, GSH-Px, and TAC were higher in OG compared with those on CG. All the above differences were of statistical significance (P < 0.05).

Conclusions

DSA-guided neurointerventional thrombolysis is highly effective in the treatment of AICVD, which can not only effectively improve patients' neurological function and cerebral hemodynamics but also mitigate VEF injury and help to alleviate patients' OS.

Magnetic Resonance Image Feature Analysis under Deep Learning in Diagnosis of Neurological Rehabilitation in Patients with Cerebrovascular Diseases

To explore the impact of magnetic resonance imaging (MRI) image features based on deep learning algorithms on the neurological rehabilitation of patients with cerebrovascular diseases, eighty patients with acute cerebrovascular disease were selected as the research objects. According to whether the patients were with vascular cognitive impairment (VCI), they were divided into VCI group (34 cases) and non-VCI group (46 cases). In addition, based on the convolutional neural network (CNN), a new multimodal CNN image segmentation algorithm was proposed. The algorithm was applied to the segmentation of MRI images of patients with vascular cognitive impairment (VCI) and compared with the segmentation results of CNN and fully CNN (FCN). As a result, the segmentation results of the three different algorithms showed that the Dice coefficient, accuracy, and recall of the multimodal CNN algorithm were 0.78 ± 0.24, 0.81 ± 0.28, and 0.88 ± 0.32, respectively, which were significantly increased compared to those of other two algorithms (P < 0.05). The neurological evaluation results showed that the MMSE and MoCA scores of VCI patients were 15.4 and 14.6 ± 5.31, respectively, which were significantly lower than those of the non-VCI group (P < 0.05). The TMT-a and TMT-b scores of VCI patients were 221.7 and 385.9, respectively, which were significantly higher than those of the non-VCI group (P < 0.05). The FA and MD values of nerve function-related fibers shown in the MRI images of the VCI group were significantly different from those of the non-VCI group (P < 0.05). Therefore, the neurological recovery process of VCI patients was affected by multiple neurocognitive-related fiber structures. To sum up, the multimodal CNN algorithm can sensitively and accurately reflect the degree of neurological impairment in patients with cerebrovascular disease and can be applied to disease diagnosis and neurological evaluation of VCI patients.