# Identifying Predictors of Lung Volume in Pediatric Patients Undergoing Surgery: A STROBE-Compliant Retrospective Cross-Sectional Chest Computed Tomography Study

## TL;DR

This study identifies age and height as key predictors of lung volume in young children using CT scans, aiming to improve ventilation strategies during surgery.

## Contribution

The study introduces age and sex as significant predictors of lung volume in children under five, offering a new approach to tidal volume calculation.

## Key findings

- Height was the strongest predictor of lung volume with an adjusted R-squared of 0.5621.
- Age and sex were identified as key covariates in the final predictive model.
- Bootstrap validation confirmed the model's reliability.

## Abstract

Background/Objectives: Tidal volume is determined by height and sex in adults under mechanical ventilation, and it serves as the foundation for implementing a lung-protective ventilation strategy. In children, tidal volume is often calculated based on actual body weight, without established guidelines regarding the predictors of lung volume. The aim of this study was to identify the key predictors of lung volume in children aged 0–5 years. Methods: This retrospective study involved 51 children aged 0–5 years who underwent chest computed tomography (CT) and surgery under general anesthesia between 2014 and 2024. The total lung volume was calculated using three-dimensional segmentation of the CT images. Linear regression models were used to assess predictors, including height, weight, age, sex, and body mass index (BMI). Model performance was evaluated using the adjusted R-squared and Akaike Information Criterion (AIC). Bootstrap validation with 2000 iterations was used to validate model reliability. Results: Height was the strongest predictor of lung volume (adjusted R-squared: 0.5621), and it showed a collinearity with age. The final model included age and sex as the covariates. The Bootstrap validation confirmed the model’s reliability. Conclusions: Age and sex are key predictors of the CT-derived total lung volume in children aged 0–5 years. Further studies are required to validate these findings. In addition, research is needed to derive and validate a tidal volume equation based on these predictors and assess the influence of this equation on clinical outcomes such as atelectasis, oxygenation, and inflammatory markers in pediatric surgery.