Clinical prediction model for progression from henoch-schönlein purpura to nephritis in pediatric patients.

Abstract

Objective: To identify independent risk factors for Henoch-Schönlein purpura nephritis (HSPN) in pediatric patients.

Methods: This study enrolled 180 pediatric patients (90 with HSP, 90 with HSPN) hospitalized at the 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army from December 2022 to October 2023, with a follow-up of at least six months. Clinical data were collected at the time of the first onset of HSP. Logistic regression analysis identified risk factors, which were subsequently evaluated using Receiver Operating Characteristic (ROC) curve analysis, a calibration plot, a nomogram, and decision curve analysis.

Results: A predictive model was constructed based on serum cystatin C, serum creatinine, immunoglobulin M, and estimated glomerular filtration rate (eGFR). ROC curve analysis showed high predictive accuracy, with an AUC of 0.9444, sensitivity of 0.82, and specificity of 0.98 at the optimal cutoff point. The calibration curve indicated strong agreement between predicted and actual outcomes. Decision curve analysis suggested that the model provides significant net benefits across different risk thresholds.

Conclusion: This model effectively predicts the risk of HSPN, facilitating early intervention and improved patient outcomes.