Development and validation of novel models for the prediction of intravenous corticosteroid resistance in acute severe ulcerative colitis using logistic regression and machine learning. (30th September 2022)

• Record Type: Journal Article
• Authors: Yu, Si; Li, Hui; Li, Yue; Xu, Hui; Tan, Bei; Tian, Bo-Wen; Dai, Yi-Min; Tian, Feng; Qian, Jia-Ming
• Is Part Of: Gastroenterology report. Volume 10(2022)
• Publication Date: 2022-09-30
• Subjects: acute severe ulcerative colitis -- steroid resistance -- colectomy -- machine learning; Gastroenterology -- Periodicals; 616.33
• DOI: 10.1093/gastro/goac053
• Languages: English
• ISSNs: 2052-0034

Abstract

Abstract: Background: The early prediction of intravenous corticosteroid (IVCS) resistance in acute severe ulcerative colitis (ASUC) patients remains an unresolved challenge. This study aims to construct and validate a model that accurately predicts IVCS resistance. Methods: A retrospective cohort was established, with consecutive inclusion of patients who met the diagnosis criteria of ASUC and received IVCS during index hospitalization in Peking Union Medical College Hospital between March 2012 and January 2020. The primary outcome was IVCS resistance. Classification models, including logistic regression and machine learning-based models, were constructed. External validation was conducted in an independent cohort from Shengjing Hospital of China Medical University. Results: A total of 129 patients were included in the derivation cohort. During index hospitalization, 102 (79.1%) patients responded to IVCS and 27 (20.9%) failed; 18 (14.0%) patients underwent colectomy in 3 months; 6 received cyclosporin as rescue therapy, and 2 eventually escalated to colectomy; 5 succeeded with infliximab as rescue therapy. The Ulcerative Colitis Endoscopic Index of Severity (UCEIS) and C-reactive protein (CRP) level at Day 3 are independent predictors of IVCS resistance. The areas under the receiver-operating characteristic curves (AUROCs) of the logistic regression, decision tree, random forest, and extreme-gradient boosting models were 0.873 (95% confidence interval [CI], 0.704–1.000), 0.648 (95% CI, 0.463–0.833), 0.650 (95% CI, 0.441–0.859), and 0.604 (95% CI, 0.416–0.792), respectively. The logistic regression model achieved the highest AUROC value of 0.703 (95% CI, 0.473–0.934) in the external validation. Conclusions: In patients with ASUC, UCEIS and CRP levels at Day 3 of IVCS treatment appeared to allow the prompt prediction of likely IVCS resistance. We found no evidence of better performance of machine learning-based models in IVCS resistance prediction in ASUC. A nomogram based on the logistic regression model might aid in the management of ASUC patients.