# A New Equation to Estimate Peripherally Inserted Central Catheter Length

## TL;DR

This study provides a new equation to estimate PICC catheter length based on patient characteristics like height, weight, sex, and age.

## Contribution

The paper introduces a novel predictive equation for PICC length that can be automatically calculated using patient data.

## Key findings

- The average ECL was 48.0 cm for the right arm and 51.0 cm for the left arm.
- The new equations for ECL prediction include height, weight, sex, and age as significant factors.

## Abstract

Background and Objectives: Peripherally inserted central catheter (PICC) placement plays an important role in clinical practice. This study aimed to provide an equation for the proper estimation of catheter length in cases of PICC placement without imaging guidance in relation to patient height, weight, sex, and age. Materials and Methods: For 1137 PICC placement cases in both arm veins of 954 patients at a single center, the elbow crease to the cavoatrial junction length (ECL) was calculated as follows: ECL = (PICC length) + (distance from the elbow crease to the puncture site). We analyzed the relationship between ECL and patient characteristics and suggested a new equation for ECL based on height, weight, sex, and age. Results: The average ECL was 48.0 ± 2.4 cm in the right side and 51.0 ± 3.0 cm in the left side. ECL in the right arm was significantly correlated with patient height, sex, and age, whereas the ECL in the left arm was additionally significantly correlated with patient weight. The ECL (cm) prediction model was as follows: right ECL = 26.32 + 1.33 × (female = 1, male = 2) − 0.02 × age (years) + 0.13 × height (cm); left ECL = 22.09 + 1.28 × (female = 1, male = 2) + 0.02 × age (years) + 0.14 × height (cm) + 0.042 × weight (kg). Conclusions: The appropriate PICC length was predicted based on the patient’s height, weight, sex, and age. The equations in our study can help predict the optimal catheter length and can be automatically calculated using computerized patient information for bedside procedures in PICC.