# A nomogram for predicting the risk of Clostridioides difficile infection in children with ulcerative colitis: development and validation

## TL;DR

This study created a tool to predict the risk of Clostridioides difficile infection in children with ulcerative colitis, using clinical factors to help guide early diagnosis and treatment.

## Contribution

A novel nomogram model was developed and validated for predicting CDI risk in pediatric UC patients using LASSO and logistic regression.

## Key findings

- Five independent risk factors for CDI in UC children were identified: PUCAI, ESR, Vit D, FC, and prolonged antibiotic use.
- The nomogram demonstrated excellent discriminative ability with a C-index of 0.964.
- The model showed good calibration and significant net clinical benefit according to DCA.

## Abstract

This study aimed to develop a dynamic nomogram model to predict the risk of Clostridioides difficile infection (CDI) in children with ulcerative colitis (UC).

This was a retrospective study that clinical data from pediatric diagnosis and treatment with UC at Zhengzhou University Children's Hospital between January 2018 and December 2024 were retrospectively reviewed. Patients were classified into CDI (n = 35) and non-CDI (n = 86) groups based on the presence or absence of CDI. Predictor variables were selected using least absolute shrinkage and selection operator (LASSO) regression and subsequently entered into a multivariate logistic regression model. Nomograms were then constructed based on the final logistic regression analysis. The model's performance and clinical utility were assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Internal validation was performed using 1,000 bootstrap resamples.

A total of 121 children were included in the study. Based on LASSO and multivariate logistic regression analysis of 24 candidate variables, five independent risk factors for CDI in children with UC were identified: Pediatric Ulcerative Colitis Activity Index (PUCAI), erythrocyte sedimentation rate (ESR), vitamin D (Vit D), fecal calprotectin (FC), and antibiotic use exceeding seven days (all p < 0.05). The nomograms constructed with the above variables demonstrated excellent discriminative ability (C-index = 0.964, 95% CI: 0.932–0.997). The Hosmer-Lemeshow test (χ2 = 12.529, p = 0.129) and bootstrap validation revealed good concordance between the predicted probabilities and actual outcomes. Decision curve analysis (DCA) indicated significant net clinical benefit, and the model maintained robust consistency across relevant clinical subgroups.

PUCAI, ESR, Vit D, FC, and use of antibiotic use exceeding seven days were the five independent risk factors for CDI in children with UC. The resulting nomogram may support clinicians in early diagnosis and timely adjustment of therapeutic strategies.