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"Our findings first shed new light on the preoperative estimation of the risk factors of PICCrelated complications for the children with blood diseases in China" Chang et al (2021).

Abstract:

Background: Although the peripherally inserted central catheter (PICC) has been widely utilized, there is still a lack of large sample size-based relevant risk factor investigation for the children with blood diseases in a single center of China.

Methods: We performed a retrospective cohort study through including a total of 2,974 cases aged 0-18 years with blood diseases and PICC insertion. Success rates of different PICC operation techniques were compared. Targeting the common PICC-related complications, we performed the univariate and multivariate logistic regression analyses. Then, based on the screened risk factors, the prediction modeling analysis of binary logistic regression was conducted.

Results: The “B-ultrasound plus Seldinger technology” showed a higher success rate of PICC placement than the “non-assistive blind insertion”. The catheter type was closely linked to the occurrence of catheter occlusion. The age, insertion site, and catheter type might be the risk factors of phlebitis, while the insertion site, operation season, and catheter type might be associated with catheter fracture. Furthermore, based on these risk factors, we established the nomogram prediction models of phlebitis, rash occurrence, and catheter fracture, respectively, which shows a good predictive ability and a moderate level of predictive accuracy.

Conclusions: Our findings first shed new light on the preoperative estimation of the risk factors of PICCrelated complications for the children with blood diseases in China.

Reference:

Chang LX, Chen YW, Wang MC, Zhao SY, Wang M, Tian Y, Tang L, Wang JX, Yang MM, Zhu XF, Zhang HM. Analysis of peripherally inserted central catheter-related complications: a retrospective cohort study of 2,974 children with blood diseases in a single center of China. Ann Palliat Med. 2021 Mar 12:apm-20-1771. doi: 10.21037/apm-20-1771. Epub ahead of print. PMID: 33752422.