Diagnosis of bacterial infection in children with relapse of nephrotic syndrome: a personalized decision-analytic nomogram and decision curve analysis

Background Infections associated with nephrotic relapses (NR) are often managed according to physician preferences. A validated prediction tool will aid clinical decision-making and help in rationalizing antibiotic prescriptions. Our objective was to develop a biomarker-based prediction model and a regression nomogram for the prediction of the probability of infection in children with NR. We also aimed to perform a decision curve analysis (DCA). Methods This cross-sectional study included children (1–18 years) with NR. The outcome of interest was the presence of bacterial infection as diagnosed using standard clinical definitions. Total leucocyte count (TLC), absolute neutrophil count (ANC), quantitative C-reactive protein (qCRP), and procalcitonin (PCT) were the biomarker predictors. Logistic regression was used to identify the best biomarker model, followed by discrimination and calibration testing. Subsequently, a probability nomogram was constructed and DCA was done to determine the clinical utility and net benefits. Results We included 150 relapse episodes. A bacterial infection was diagnosed in 35%. Multivariate analysis showed the ANC + qCRP model to be the best predictive model. This model displayed excellent discrimination (AUC: 0.83), and calibration (optimism-adjusted intercept: 0.015, slope: 0.926). A prediction nomogram and web-application was developed. The superiority of the model was also confirmed by DCA in the probability threshold range of 15–60%. Conclusions An ANC-based and qCRP-based internally validated nomogram can be used for the prediction of probability of infection in non-critically ill children with NR. Decision curves from this study will aid in the decision-making of empirical antibiotic therapy, incorporating threshold probabilities as a surrogate of physician preference. Graphical abstract A higher resolution version of the Graphical abstract is available as Supplementary information