Monitoring-Based Model for Personalizing Fecal Incontinence in Patients With Crohn's Disease: A Multicenter Inception
Can Wang1, Fan Yang2, Lichao Qiao1
1Department of Colorectal Surgery, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, China.
Background And Aims:
Fecal incontinence (FI) is a common complaint that greatly affects the quality of life of patients with Crohn's disease (CD) and is associated with the clinical characteristics of CD. We aimed to identify risk factors related to FI and construct a risk prediction model for FI in patients with CD.
Methods:
This retrospective study included 600 Chinese patients with CD from 4 IBD centers between June 2016 and October 2021. The patients were assigned to the training (n = 480) and testing cohorts (n = 120). Two nomograms were developed based on the logistic regression and Cox regression models to predict the risk factors for FI in patients with CD. The discriminatory ability and accuracy of the nomograms were evaluated using the receiver operating characteristic (ROC) curves and the area under the ROC curves (AUCs). Additionally, the Kaplan-Meier survival curve was also used further to validate the clinical efficacy of the Cox regression model.
Results:
The overall prevalence of FI was 22.3% (n = 134 of 600). In the logistic regression model, age at diagnosis (odds ratio [OR], 1.032; P = .033), penetrating behavior of disease (OR, 3.529; P = .008) and Perianal Disease Activity Index score >4 (OR, 3.068; P < .001) were independent risk factors for FI. In the Cox regression model, age at diagnosis (hazard ratio [HR], 1.027; P = .018), Montreal P classification (HR, 2.608; P = .011), and Perianal Disease Activity Index score >4 (HR, 2.190; P = .001) were independent predictors of the prevalence of FI over time. Two nomograms were developed to facilitate risk score calculation, and they showed good discrimination ability according to AUCs.
Conclusions:
In this study, we identified 4 risk factors related to the prevalence of FI and developed 2 models to effectively predict the risk scores of FI in CD patients, helping to delay the course of FI and improve the prognosis with timely intervention.
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