BMC gastroenterology · 2025
Analysis of risk factors for Clostridioides difficile infection in patients with ulcerative colitis and development of a predictive nomogram.
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Qiu Kanghua, Ye Peng
Аннотация
OBJECTIVE: This study aimed to identify the risk factors associated with Clostridioides difficile infection (CDI) in patients with ulcerative colitis (UC) and to develop a predictive nomogram.
METHODS: We prospectively enrolled 331 UC patients from April 2022 to October 2024. We randomly assigned them to a training set (n = 232) and a validation set (n = 99) in a 7:3 ratio. In the training set, there were 51 patients had CDI (CDI group) and 181 patients did not (non-CDI group). Clinical data were recorded. Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to screen risk factors for CDI in UC patients. R software was used to construct a nomogram model for predicting CDI. The calibration curve evaluated the model's calibration, while Receiver operating characteristic (ROC) curve assessed its discriminative power. Clinical decision curve analysis was conducted to evaluate clinical utility.
RESULTS: Age, obesity prevalence, and use of infliximab and glucocorticoids were higher in the CDI group compared to the non-CDI group, and albumin levels were lower (P < 0.05). Increased age, obesity, use of infliximab and glucocorticoids, and decreased albumin were independent risk factors for UC with CDI (P < 0.05). The training and validation sets showed that the calibration curve fitted well with the ideal curve, and the area under the ROC curve (AUC) was 0.930 and 0.877, respectively, indicating good calibration and discrimination. Clinical decision curve analysis showed high clinical effectiveness.
CONCLUSION: The proposed nomogram demonstrated excellent performance in predicting CDI risk among patients with UC.
METHODS: We prospectively enrolled 331 UC patients from April 2022 to October 2024. We randomly assigned them to a training set (n = 232) and a validation set (n = 99) in a 7:3 ratio. In the training set, there were 51 patients had CDI (CDI group) and 181 patients did not (non-CDI group). Clinical data were recorded. Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to screen risk factors for CDI in UC patients. R software was used to construct a nomogram model for predicting CDI. The calibration curve evaluated the model's calibration, while Receiver operating characteristic (ROC) curve assessed its discriminative power. Clinical decision curve analysis was conducted to evaluate clinical utility.
RESULTS: Age, obesity prevalence, and use of infliximab and glucocorticoids were higher in the CDI group compared to the non-CDI group, and albumin levels were lower (P < 0.05). Increased age, obesity, use of infliximab and glucocorticoids, and decreased albumin were independent risk factors for UC with CDI (P < 0.05). The training and validation sets showed that the calibration curve fitted well with the ideal curve, and the area under the ROC curve (AUC) was 0.930 and 0.877, respectively, indicating good calibration and discrimination. Clinical decision curve analysis showed high clinical effectiveness.
CONCLUSION: The proposed nomogram demonstrated excellent performance in predicting CDI risk among patients with UC.