Optimizing individualized management of patients with ulcerative colitis: Identification of risk factors predicting ulcerative colitis-associated neoplasia

Author:

Jiang Wenyu1,Lu Meijiao2,Zhang Li1,Xu Chenjing1,Wang Ruohan1,Xu Ying1,Tang Wen2,Zhang Hongjie1

Affiliation:

1. Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China

2. Department of Gastroenterology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China.

Abstract

The risk of developing colorectal neoplasia in patients with ulcerative colitis (UC) is increased. The purpose of this study is to analyze the risk factors of UC-associated neoplasia (UCAN) in UC patients and establish a clinical prediction model. 828 UC patients were included in this retrospective study. 602 patients were in discovery cohort and 226 patients were in validation cohort (internal validation cohort/external validation cohort: 120/106). Clinical and endoscopic data were collected. The discovery cohort was divided into UC group and UCAN group for univariate and multivariate binary logistic analyses. The UCAN clinical prediction model was established and verified. In the univariate analysis, 7 risk factors were related to UCAN. Multivariate logistic regression analysis showed that age at diagnosis of UC (OR: 1.018, 95% CI: 1.003–1.033), Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score (OR: 1.823, 95% CI: 1.562–2.128), and size of polyps (size1: OR: 6.297, 95% CI: 3.669–10.809; size2: OR: 12.014, 95% CI: 6.327–22.814) were independent risk factors of UCAN. A mathematical equation was established. The area under the ROC curve (AUC) of this model was calculated to be 0.845 (95%CI: 0.809–0.881). The sensitivity was 0.884 and the specificity was 0.688. The AUC of internal validation cohort was 0.901 (95%CI: 0.815, 0.988), sensitivity was 75.0% and specificity was 92.6%. The AUC of external validation cohort was 0.842 (95%CI: 0.709, 0.976), sensitivity was 62.5% and specificity was 93.9%. This prediction model is simple, practical, and effective for predicting the risk of UCAN, which is beneficial to the individualized management of patients with UC.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

General Medicine

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