Changes in urinary renal injury markers in children with Mycoplasma pneumoniae pneumonia and a prediction model for related early renal injury

Abstract Background This study aims to analyse changes in urinary kidney injury markers in children with Mycoplasma pneumoniae pneumonia (MPP), investigate the risk factors for MPP-related acute kidney injury (AKI) and establish a model to predict MPP-related AKI. Methods Ninety-five children were enrolled based on the study’s inclusion and exclusion criteria. They were divided into a severe MPP (SMPP) group and a non-SMPP group and then into an AKI group and a non-AKI group according to the presence of AKI. A univariate logistic regression analysis was performed to explore the early risk factors for AKI. Based on a multivariate logistic regression analysis and a least absolute shrinkage and selection operator regression analysis, appropriate variables were selected to establish a prediction model, and R 4.2.2 software was used to draw nomograms and generate a dynamic nomogram website. Results Seven urinary kidney injury markers were abnormally elevated in the SMPP group and the non-SMPP group: urinary N-acetyl-β-D-glucosaminidase (NAG), β2-microglobulin, α1-microglobulin, retinol-binding protein, urinary immunoglobulin G, urinary transferrin and urinary microalbumin. Sixteen children were identified with AKI during hospitalisation. The AKI group had higher levels of urinary NAG, α1-microglobulin, β2-microglobulin, urinary microalbumin, urinary transferrin and retinol-binding protein than the non-AKI group ( P < 0.05). The MPP-related AKI prediction model consists of four indicators (serum immunoglobulin M IgM, C-reactive protein CRP, urine NAG and sputum plug presence) and a dynamic nomogram. Conclusion Urinary kidney injury markers are often elevated in children with MPP; urinary NAG is the marker most likely to be elevated, and it is especially evident in severe cases. The nomogram of the prediction model, comprising serum IgM, CRP, urinary NAG and sputum plug presence, can predict the probability of AKI in children with MPP.