The safety of clinically indicated replacement or routine replacement of peripheral intravenous catheters: A randomized controlled study.

OBJECTIVE: The primary purpose of this study was to explore the safety of peripheral intravenous catheter (PIVC) replacement every 96 h compared to that of clinically indicated catheter removal. METHODS: A prospective, single-blind, randomized controlled trial was conducted. A random number table method was used. Six hundred patients treated with PIVC intravenous infusion in 10 nursing units of a hospital from September to October 2019 were selected. Sixty were collected from each nursing unit, including 30 in the clinically indicated replacement group and 30 in the routine replacement group. The incidence of phlebitis, catheter-related infection (CRI), occlusion, infiltration, and any form of infusion therapy failure were compared between the two groups. SPSS 23.0 software was used. RESULTS: The dwelling times of PIVC in the clinically indicated replacement group and routine replacement group were significantly different (hours) (83.62 ± 50.08, 69.75 ± 25.54, t = 3.021, p = 0.003). The incidence of any form of infusion therapy failure (RR = 4.448, 95% CI: 3.158-6.265, p < 0.001), phlebitis (RR = 2.416, 95% CI: 1.595-3.660, p < 0.001), occlusion (RR = 6.610, 95% CI: 3.062-14.268, p < 0.001), infiltration (RR = 2.607, 95% CI: 1.130-6.016, p = 0.020), accidental dislodgement (RR = 2.027, 95% CI: 1.868-2.200, p = 0.013), and pain at the insertion site (RR = 2.521, 95% CI: 1.742-3.649, p < 0.001) was higher in the clinically indicated replacement group than that in the routine replacement group. The overall survival curve of PIVC was drawn with Kaplan-Meier survival analysis. The median survival time of intravenous infusion was 59.58 h; the cumulative survival rates of 48 h, 72 h, and 96 h were 77.00%, 51.33%, and 20.33%, respectively. CONCLUSION: Replacement of PIVC every 96 h is safer than clinically indicated.

KPLS-KSER based approach for quality-related monitoring of nonlinear process.

As a commonly used data modeling method, kernel principal least square (KPLS) has been widely used in a variety of process monitoring tasks like soft sensing and fault detection. However, it is not easy to implement fault diagnosis based on KPLS model because the nonlinear mapping of kernel methodology completely obscures the correspondence between the original variable sample and the kernel model. Although the kernel-gradient based algorithms have successfully extracted the analytical solution of contribution plot, they are still very inefficient and impractical because of their high amount of computation. In order to overcome this inherent defect, a new idea of kernel sample equivalent replacement (KSER) will be used in this paper, which reveals an important relationship between centralized kernel matrix and the variance-covariance matrix of centralized variable matrix. By integrating KSER into KPLS, a linear regression between the original variable sample and the output could be directly constructed. This approach makes it possible to use the linear algorithm to diagnose the faulty variables. Based on which, an improved contribution plot algorithm is further integrated to the new model to locate faulty variables. Thereby, the complex nonlinear diagnosis problem can be finally solved by a linear manner with extremely low computational complexity. Simulation results of both numerical and industrial examples demonstrate the effectiveness of the proposed method.

A nonlinear quality-related fault detection approach based on modified kernel partial least squares.

In this paper, a new nonlinear quality-related fault detection method is proposed based on kernel partial least squares (KPLS) model. To deal with the nonlinear characteristics among process variables, the proposed method maps these original variables into feature space in which the linear relationship between kernel matrix and output matrix is realized by means of KPLS. Then the kernel matrix is decomposed into two orthogonal parts by singular value decomposition (SVD) and the statistics for each part are determined appropriately for the purpose of quality-related fault detection. Compared with relevant existing nonlinear approaches, the proposed method has the advantages of simple diagnosis logic and stable performance. A widely used literature example and an industrial process are used for the performance evaluation for the proposed method.