ArticleClinical kidney journal2026
An early prediction model of steroid dependence or frequent relapses in nephrotic children: a multicentre retrospective cohort study in China.
Article in Clinical kidney journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Early identification and prediction of steroid-dependent/frequently relapsing nephrotic syndrome (SDNS/FRNS) in children have become increasingly important. The aim of this study was to develop and validate a model for the early identification and prediction of SDNS/FRNS in paediatric patients. Methods: A multicentre retrospective study was conducted in China to investigate risk factors and develop a prediction model for the early identification of potential SDNS/FRNS in children with steroid-sensitive nephrotic syndrome (SSNS). Data were collected from 4259 children diagnosed with SSNS at the Children's Hospital of Chongqing Medical University between January 2009 and December 2023. After applying strict inclusion and exclusion criteria, 2624 cases were included in this study. The cohort was divided into two groups: the control group, comprising 1064 cases of non-SDNS/FRNS, and the experimental group, comprising 1560 cases of SDNS/FRNS. Both groups were further split into training and validation sets at an 8:2 ratio. Additionally, data from other nationwide multicentre studies conducted between 2012 and 2021 were gathered for external validation. Results: This study identified 15 acute-phase clinical predictors of SDNS/FRNS development. Among the eight tested models, the LightGBM showed optimal performance [area under the curve (AUC) 0.89] when these features were used. The simplified 15-variable LightGBM model maintained strong external validation accuracy (AUC 0.93) and has been implemented in an online clinical tool for practical application. Conclusions: This work presents the first machine learning-based early prediction model for SDNS/FRNS using clinical big data from the acute phase of the condition. Additionally, it introduces an online prediction tool for paediatric SDNS/FRNS.
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