Development of an anthropometric‐based algorithm to estimate feeding tube insertion depth in neonates

Abstract Objective To develop an anthropometric‐based algorithm for estimating feeding tube (FT) insertion depth in neonates using retrospectively collected radiographic data. Methods This observational study reviewed records of infants admitted to a Danish level IV Neonatal Intensive Care Unit from January 2023 to December 2024. Infants with FT placement, using the conventional Nose–Ear–Xiphoid (NEX) method, and radiographic verification within 24 h were included. Radiographically verified FT position and anthropometric data were used to develop new linear and multivariate quadratic models to optimize placement. Results Among 198 infants were included (median gestational age [GA] of 35 + 0 weeks [interquartile range, IQR:28 + 6–38 + 1]), 301 radiographic images showed, 144 FTs located at lumbar vertebra 1 (L1), 139 within one vertebra of L1, and 18 outside the stomach. Radiologists recommended adjustment in 107 images. Linear models (weight‐ or length‐dependent) showed strong predictive power ( R 2 > 0.84), while the multivariate quadratic model reduced bias and achieved R 2 = 0.88, confirmed by cross‐validation. Prediction accuracy within the predefined target range (L1 ± 1.5 cm) was 77% n, compared with 64% of clinically acceptable NEX placed tubes ( p < 0.001). The optimal algorithm was: FT depth = −3.46 + 0.00229 × weight (g) − 9.48 × 10 −8 × weight (g) 2 + 0.5992 × length (cm) −0.005918 × length(cm) 2 + 0.07808 × correted GA(w). Conclusion The quadratic prediction model showed good internal performance and reduced bias at anthropometric extremes, offering a standardized approach to optimize FT placement and enhance safety in neonatal care. Prospective external validation is required before clinical implementation.