Evidence map›Paper›PMID 42494086›Full record

ArticleJournal of nursing management2026

An Equation to Estimate PICC Catheter Length in Very Low Birth Weight Infants.

Jiaxin Li, Yubing Dong, Qingling Yu, Aifen Cao, Shan Jiang, Yinsui Huang, Xiaoyun Xiong

Abstract read
In one paragraph

Article in Journal of nursing management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jiaxin LiHeping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.ORCID https://orcid.org/0009-0004-5307-6807
Yubing DongShenzhen Maternity and Child Healthcare Hospital, Women and Children's Medical Center, Southern Medical University, Shenzhen, Guangdong, China, fimmu.com.
Qingling YuGuangzhou University of Chinese Medicine, Guangzhou, China, gzucm.edu.cn.
Aifen CaoShenzhen Maternity and Child Healthcare Hospital, Women and Children's Medical Center, Southern Medical University, Shenzhen, Guangdong, China, fimmu.com.
Shan JiangShenzhen Maternity and Child Healthcare Hospital, Women and Children's Medical Center, Southern Medical University, Shenzhen, Guangdong, China, fimmu.com.
Yinsui HuangShenzhen Maternity and Child Healthcare Hospital, Women and Children's Medical Center, Southern Medical University, Shenzhen, Guangdong, China, fimmu.com.
Xiaoyun XiongShenzhen Maternity and Child Healthcare Hospital, Women and Children's Medical Center, Southern Medical University, Shenzhen, Guangdong, China, fimmu.com.

Funding

Guangdong Provincial High-level Clinical Key Specialty Project SZGSP009Key Laboratory for Pregnancy Disorders and Maternal and Child Health of Shenzhen ZDSYS20230626091559006National Health Commission of the People's Republic of China 2025FYH21Natural Science Foundation of Shenzhen Municipality JCYJ20250604191408012Research Fund of Shenzhen Maternity & Child Healthcare Hospital FYB2022004Shenzhen "Sanming" Project of Medicine SZSM202211001Shenzhen Science and Technology Innovation Program JCYJ20230807120213026Shenzhen Science and Technology Innovation Program JCYJ20240813115035045
6 · The paper itself

Abstract

objectivePeripherally inserted central catheters (PICCs) are essential for very low birth weight infants (VLBWIs), but optimal insertion depth remains challenging. Existing estimation methods often yield suboptimal accuracy. This study aimed to develop and validate anthropometric parameter-based formulas to predict lower extremity PICC insertion depth using the first popliteal crease as an anatomical landmark in VLBWI, seeking to improve first-attempt success rates and procedural safety.

methodsA prospective study was conducted in a tertiary NICU, enrolling 329 VLBWIs who underwent lower limb PICC insertion between May 2023 and May 2024. Data collected included sex, target vein for catheterization, body weight, length, head circumference at insertion, and vertical distance from the puncture site to the first popliteal crease. Pearson correlation and linear regression analyses were performed to examine associations between anthropometric parameters and optimal catheter length. Univariate prediction formulas (weight-based, length-based, and head circumference-based models) and multivariate regression models were established. Bland-Altman analysis was used to validate model agreement.

resultsUnivariate regression analysis yielded the following: weight-based model: total insertion depth (cm) = [8.31 + (5 × body weight [kg])] ± PS-PTC distance, where "+" applies when the puncture site is distal to the popliteal crease and "-" applies when proximal; 8.31 is the regression intercept and five is the regression slope coefficient. Length-based model: total insertion depth (cm) = [1.13 + (0.35 × body length [cm])] ± PS-PTC distance; 1.13 is the intercept and 0.35 is the slope coefficient. Head circumference-based model: total insertion depth (cm) = [2.33 + (0.43 × head circumference [cm])] ± PS-PTC distance; 2.33 is the intercept and 0.43 is the slope coefficient. Multivariate regression analysis established four combination models, with Model IV (weight + length + head circumference) showing optimal performance.

conclusionAnthropometric parameter-based prediction formulas provide precise guidance for lower extremity PICC placement in VLBWI, enhancing placement success and safety. This approach supports individualized care and is especially valuable where ultrasound guidance is limited.

Indexed as

Catheterization, PeripheralInfant, Very Low Birth WeightFemaleHumansInfant, NewbornMaleProspective Studies

Identifiers

PMID42494086
PMCPMC13396696

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.