Development and validation of a simple clinical nomogram for predicting infectious diseases in pediatric kidney transplantation recipients: a retrospective study

Abstract

To construct and verify an easy-to-use nomogram for predicting the risk of infectious diseases in pediatric kidney transplant recipients. Clinical data of hospitalized pediatric kidney transplant recipients were retrospectively analyzed. Meaningful variables identified from the multivariate stepwise logistic regression analysis were used to construct the nomogram. Internal validation was performed using Bootstrap resampling 1,000 times. The nomogram was evaluated using calibration, decision and receiver operating characteristic (ROC) curves. A total of 297 pediatric kidney transplant recipients were included (164 infected, 133 non-infected). Multivariate stepwise regression analysis identified white blood cell count (WBC), lymphocyte to monocyte ratio (MLR), platelet to neutrophil ratio (PNR), red cell distribution width-standard deviation (RDW-SD), and albumin (ALB) as significant predictors of postoperative infection. The nomogram, based on the five indicators, showed strong discrimination ability (AUC = 0.756; 95% CI [0.702–0.811]), with a sensitivity of 88.0% and a specificity of 54.3%. The calibration curve and decision curve further demonstrated good consistency and clinical practicality between the predicted and actual values. WBC, MLR, PNR, RDW-SD, and ALB are effective indicators for predicting postoperative infection in pediatric kidney transplant recipients. The nomogram constructed from these indicators can effectively predict and evaluate the early risk of infection in these patients.

Keywords

• Kidney transplantation
• Children
• Nomogram
• Prediction model
• Infectious disease