Implementation of Hospital-to-Home Model for Nutritional Nursing Management of Patients with Chronic Kidney Disease Using Artificial Intelligence Algorithm Combined with CT Internet +. (27th March 2022)

• Record Type: Journal Article
• Authors: Chen, Xing; Huang, Xueqin; Yin, Mingyuan
• Other Names: Pallikonda Rajasekaran M Academic Editor.
• Is Part Of: Contrast media & molecular imaging. Volume 2022(2022)
• Publication Date: 2022-03-27
• Subjects: Diagnostic imaging -- Periodicals; Magnetic resonance imaging -- Periodicals; Contrast media (Diagnostic imaging) -- Periodicals; Contrast Media -- Periodicals; Diagnostic Imaging -- Periodicals; Substances de contraste -- Périodiques; Diagnostics moléculaires -- Périodiques; Imagerie médicale; Substance de contraste; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.0754
• DOI: 10.1155/2022/1183988
• Languages: English
• ISSNs: 1555-4309

Abstract

Abstract : The objective of this study was to evaluate the application value of "Internet + hospital-to-home (H2H)" nutritional care model using the improved wavelet transform algorithm based on computed tomography (CT) images in the nutritional care management of chronic kidney disease (CKD) stages 3-5. A total of 120 patients with CKD were the research objects and they were randomly divided into two groups. The normal nutritional nursing model was used for nursing of patients in the control group, and the "Internet + H 2H″ model was used for the observation group (H2H group), with 60 cases in each group. The nursing effect was evaluated using 320-slice volume CT low-dose perfusion imaging images, anthropometry, laboratory biochemical tests, and other survey scores. The results showed that compared with the mean filter denoising (MFD) algorithm and the orthogonal wavelet denoising (OWD) algorithm, the mean square error (MSE) and signal noise ratio (SNR) values of the IWT algorithm were better (40.0781 vs 45.2891, 59.2123)/(20.0122 vs 18.2311, 15.7812) ( P < 0.05). The arm muscle circumference (MAC) (239.77 ± 18.24 vs 243.94 ± 18.72 mm) and triceps skindold (TSF) value (8.87 ± 2.74 vs 10.04 ± 2.90 mm) of the patients in the H2H group were greatly improved after the nursing ( P < 0.05). For biochemical indicators, serum albumin (ALB) (35.22 ± 4.98 vs 45.32 ± 4.21) g/L, prealbumin (PAB) (289.94 ± 72.99 vs 341.79 ± 74.45) mg/L, hemoglobin (Hb) (97.62 ± 24.87 vs 110.65 ± 28.83) g/L, and blood urea nitrogen (BUN) (15.74 ± 9.87 vs 11.06 ± 5.69) mmol/L of patients in H2H group were improved ( P < 0.05). After nursing, the nutritional screening score of the H2H group was obviously improved (83.33% (before) vs 50% (after)), the total score of health quality assessment (114.89 ± 5.23) in the H2H group was much higher than that of the control group (87.22 ± 14.89), and the satisfaction on the nursing model was higher in the H2H group (100% vs 71.67%) ( P < 0.05). The renal cortex BF before and after nursing was significantly different between the two groups of patients ( P < 0.05), and the BE of the H2H group was significantly higher than that of the control group after treatment ((335.12 ± 52.74) mL·100 g −1 ·min −1 vs (289.90 ± 53.91) mL·100 g −1 ·min −1 ) ( P < 0.05). In summary, the "Internet + H 2H″ nutritional nursing model was more individualized, which can better improve the physical quality of patients with stages 3-5 of CKD, improve the psychological state of patients, and further enhance the prognosis of the disease. In addition, the IWT algorithm showed better effects in the processing of the image of 320-slice volume CT low-dose perfusion imaging, and it was worthy of clinical application.