Development and validation of a predictive model for endoscopic improvement of Crohn's disease.

Abstract

Background: At present, there is a lack of non-invasive indicators to evaluate the changes in endoscopic activity between two visits for patients with Crohn's disease (CD).

Aim: To develop a model for predicting whether endoscopic activity will improve in CD patients.

Methods: This is a single-center retrospective study that included patients diagnosed with CD from January 2014 to December 2022. The patients were randomly divided into a modeling group (70%) and an internal validation group (30%), with an external validation group from January 2023 to March 2024. Univariate and binary logistic regression analyses were conducted to identify independent risk factors, which were used to construct a nomogram model. The model's performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Additionally, further sensitivity analyses were performed.

Results: One hundred seventy patients were included in the training group, while 64 were included in the external validation group. A binary logistic stepwise regression analysis revealed that the changes in the amplitudes of albumin (ALB) and fibrinogen (FIB) were independent risk factors for endoscopic improvement. A nomogram model was developed based on these risk factors. The area under the curve of the model for the training group, internal validation group, and external validation group were 0.802, 0.788, and 0.787, respectively. The average absolute errors of the calibration curves were 0.011, 0.016, and 0.018, respectively. DCA indicated that the model performs well in clinical practice. Additionally, sensitivity analysis demonstrated that the model has strong robustness and applicability.

Conclusion: Our study shows that changes in the amplitudes of ALB and FIB are effective predictors of endoscopic improvement in patients with CD during follow-up visits compared to their previous ones.