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基于临床参数的高频超声引导下早产儿最佳经外周静脉穿刺中心静脉置管(PICC)置入深度预测模型的构建与应用

Construction and application of a predictive model for optimal peripherally inserted central catheter (PICC) insertion depth in preterm infants under high-frequency ultrasound guidance based on clinical parameters.

作者信息

Li Wenlan, Gou Bo, We Xin, Zhang Jicheng, Liu Bentian, Zhou Zhuyu, Liu Jian

机构信息

Department of Ultrasound, The First Affiliated Hospital of Chengdu Medical College, Chengdu, China.

Department of Ultrasound, Deyang People's Hospital, Deyang, China.

出版信息

PLoS One. 2025 Sep 16;20(9):e0332447. doi: 10.1371/journal.pone.0332447. eCollection 2025.

DOI:10.1371/journal.pone.0332447
PMID:40956820
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12440187/
Abstract

OBJECTIVE

This study aims to construct and validate a predictive formula based on routine clinical parameters for determining the optimal catheter placement depth (OCPD) in preterm infants undergoing peripherally inserted central catheter (PICC) insertion via the basilic vein (BV) or axillary vein (AXV). The goal is to provide a standardized reference protocol for precise PICC placement in neonatal intensive care, with the aim of enhancing procedural accuracy and reducing catheter-related complications.

METHODS

This prospective study enrolled 105 preterm infants, who were categorized into two groups based on the puncture site: the basilic vein PICC group (BV-PICC, n = 59) and the axillary vein PICC group (AXV-PICC, n = 46). All catheter placements were performed under high-frequency ultrasound guidance to ensure accurate positioning and optimal catheter depth. The optimal catheter insertion length was recorded for each infant. Subsequently, clinical data were collected and analyzed, including gestational age, head circumference, chest circumference, maximum abdominal circumference, mid-forearm circumference, body weight, and body length. Multiple linear regression analysis was conducted to explore the relationship between optimal catheter depth and the clinical parameters of the preterm infants. Based on this analysis, a predictive formula for PICC insertion depth in preterm infants was developed using clinical parameters.

RESULTS

In the BV-PICC group, birth weight and weight at the time of catheterization were identified as significant predictors. The optimal catheter placement depth (cm) was calculated using the following formula: OCPD = 8.205 + 0.005 × birth weight (g)-0.003 × weight at catheterization (g). In the AXV-PICC group, only body length at catheterization was identified as a significant predictor. The formula was: OCPD = 1.024 + 0.177 × body length at catheterization (cm).

CONCLUSION

The predictive formulas for the OCPD of PICC inserted via different pathways in preterm infants, developed under high-frequency ultrasound guidance and based on clinical parameters, demonstrated accuracy and excellent clinical applicability. These formulas provide a valuable reference for both standardized and individualized PICC placement in preterm infants, thereby facilitating evidence-based clinical decision-making and potentially reducing catheter-related complications.

摘要

目的

本研究旨在构建并验证一种基于常规临床参数的预测公式,用于确定经贵要静脉(BV)或腋静脉(AXV)行外周静脉穿刺中心静脉置管(PICC)的早产儿的最佳导管置入深度(OCPD)。目标是为新生儿重症监护中精确的PICC置管提供标准化参考方案,以提高操作准确性并减少导管相关并发症。

方法

本前瞻性研究纳入了105例早产儿,根据穿刺部位分为两组:贵要静脉PICC组(BV-PICC,n = 59)和腋静脉PICC组(AXV-PICC,n = 46)。所有导管置入均在高频超声引导下进行,以确保准确的定位和最佳的导管深度。记录每个婴儿的最佳导管插入长度。随后,收集并分析临床数据,包括胎龄、头围、胸围、最大腹围、前臂中段周长、体重和身长。进行多元线性回归分析以探讨最佳导管深度与早产儿临床参数之间的关系。基于此分析,使用临床参数建立了早产儿PICC插入深度的预测公式。

结果

在BV-PICC组中,出生体重和置管时体重被确定为显著预测因素。最佳导管置入深度(cm)使用以下公式计算:OCPD = 8.205 + 0.005×出生体重(g)-0.003×置管时体重(g)。在AXV-PICC组中,仅置管时身长被确定为显著预测因素。公式为:OCPD = 1.024 + 0.177×置管时身长(cm)。

结论

在高频超声引导下并基于临床参数建立的早产儿经不同途径置入PICC的OCPD预测公式,显示出准确性和良好的临床适用性。这些公式为早产儿标准化和个体化PICC置管提供了有价值的参考,从而有助于基于证据的临床决策制定,并可能减少导管相关并发症。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37c5/12440187/b750e11d923b/pone.0332447.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37c5/12440187/b750e11d923b/pone.0332447.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/37c5/12440187/b750e11d923b/pone.0332447.g001.jpg

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