Development and validation of nomogram prediction model for severe kidney disease in children with Henoch–Schönlein purpura: A prospective analysis of two independent cohorts—forecast severe kidney disease outcome in 2,480 hospitalized Henoch–Schönlein purpura children

Abstract

This study aimed to develop and validate a nomogram to forecast severe kidney disease (SKD) outcomes for hospitalized Henoch–Schönlein purpura (HSP) children. The predictive model was built based on a primary cohort that included 2,019 patients with HSP who were diagnosed between January 2009 and December 2013. Another cohort consisting of 461 patients between January 2014 and December 2016 was recruited for independent validation. Patients were followed up for 24 months in development/training and validation cohorts. The data were gathered at multiple time points after HSP (at 3, 6, 12, and 24 months) covering severe kidney disease as the severe outcome after HSP. The least absolute shrinkage and selection operator (LASSO) regression model was utilized to decrease data dimension and choose potentially relevant features, which included socioeconomic factors, clinical features, and treatments. Multivariate Cox proportional hazards analysis was employed to establish a novel nomogram. The performance of the nomogram was assessed on the aspects of its calibration, discrimination, and clinical usefulness. The nomogram comprised serious skin rash or digestive tract purpura, severe gastrointestinal (GI) manifestations, recurrent symptoms, and renal involvement as predictors of SKD, providing favorable calibration and discrimination in the training dataset with a C-index of 0.751 (95% CI, 0.734–0.769). Furthermore, it demonstrated receivable discrimination in the validation cohort, with a C-index of 0.714 (95% CI, 0.678–0.750). With the use of decision curve analysis, the nomogram was proven to be clinically useful. The nomogram independently predicted SKD in HSP and displayed favorable discrimination and calibration values. It could be convenient to promote the individualized prediction of SKD in patients with HSP.