Study on Ultrasonic Imaging of Nursing Care for Preventing and Treating Clinical Infection of Hemodialysis Patients Based on Smart Medical Big Data

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Abstract

The ultrasonic imaging research of nursing care for preventing and treating clinical infection of hemodialysis patients based on smart medical big data is studied. 100 hemodialysis patients were selected from May 2019 to May 2020. The patients were randomly divided into the observation group and routine group with 50 cases in each group. The PWV of common carotid artery was measured by ultrasonic rapid imaging technology, including BS value at the beginning of systole and ES value at the end of systole. According to the effect of preventive nursing intervention of intelligent medical treatment, the MHD group adopted preventive nursing intervention, while the routine group adopted traditional nursing service. The infection rate and quality of life score of patients in both groups were evaluated. The results showed that there were significant differences in BS and ES values between the MHD group and PWV in the normal group (P<0.05). There was no significant difference in BS value and ES value between MHD patients with plaque and those without plaque (P>0.05). It is proved that the ultrafast ultrasound imaging technology is safe, simple, noninvasive, nonradioactive, and fast and can automatically and accurately detect carotid PWV. It is expected to become a new imaging method for quantitative evaluation of arteriosclerosis degree in MHD patients. Preventive nursing intervention can reduce the incidence of infection in hemodialysis patients and improve their quality of life. Smart medical treatment has brought us a lot of convenience. As patients, we should change our concept, actively participate in it, and contribute to the development of smart medical treatment.

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CITATION STYLE

APA

Wen, Y., Li, H., & Gao, Y. (2021). Study on Ultrasonic Imaging of Nursing Care for Preventing and Treating Clinical Infection of Hemodialysis Patients Based on Smart Medical Big Data. Contrast Media and Molecular Imaging, 2021. https://doi.org/10.1155/2021/2551063

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