An Interpretable CT-based Prediction Model Assists in Predicting Length of Stay in Ulcerative Colitis Patients: A
Jun Lu1, Hongzhe Zhao2, Junlin Li3
1Department of Radiology, Beijing Friendship Hospital, Capital Medical University, China (J.L., H.X., J.Z., T.C., X.H., Y.W., X.M., J.J., Z.W., L.X., Z.Y.); State Key Laboratory of Digestive Health, China (J.L., H.X., Z.W., L.X., Z. Y.).
Rationale And Objectives:
This study aimed to develop and validate an interpretable prediction model integrating CT-derived imaging biomarkers and clinical laboratory parameters to predict prolonged length of stay (LOS) in ulcerative colitis (UC).
Materials And Methods:
In this multi-center retrospective study, 273 UC patients were enrolled from three hospitals. Patients were divided into training (n = 184) and external validation (n = 89) cohorts. CT visual features and body composition parameters were evaluated. Univariate and multivariable logistic regression were used to select predictive features. The imaging, clinical, and combined model were developed and evaluated. The feature contribution was qualified using the Shapley additive explanation (SHAP) algorithm.
Results:
The combined model with six key predictors including visceral adipose tissue (VAT) density, visceral obesity, bowel wall thickening, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and albumin demonstrated superior performance with area under the curve (AUCs) of 0.862 and 0.867 in the training and validation cohorts for predicting prolonged LOS(LOS≥14 days), respectively. It significantly outperformed both the imaging-only and clinical-only models (all p < 0.05). SHAP analysis confirmed VAT density as the most influential feature. With the addition of the clinical biomarkers to the CT-derived prediction model, the prediction performance significantly improved with integrated discrimination improvement (IDI=0.187, 0.189) and net reclassification improvement (NRI=0.193, 0.268) in the training and validation cohort, respectively (all p < 0.05).
Conclusion:
The combined prediction model is promising for predicting prolonged LOS in UC. Moreover, VAT density was established as an independent predictor of LOS and was identified by SHAP analysis as the most influential feature in the predictive model.
Related Concept Videos
01:30Inflammatory Bowel Disease III: Diagnostic Studies and Management I-Nutritional Therapy
Diagnostic studies
A colonoscopy is the definitive screening test, distinguishing ulcerative colitis from other colon diseases with similar symptoms. During a colonoscopy test, inflamed mucosa with exudate ulcerations can be observed, and biopsies are taken to determine the histologic characteristics of the colonic...
01:20Inflammatory Bowel Disease II: Ulcerative Colitis