Modified scoring model incorporating waist-hip ratio for predicting advanced colorectal neoplasia
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Designing a feasible risk prediction model for advanced colorectal neoplasia (ACN) can enhance colonoscopy screening efficiency. Abdominal obesity is associated with colorectal cancer development. AIM: To propose and evaluate a modified scoring model incorporating waist-hip ratio for the prediction of ACN. METHODS: A total of 6483 patients who underwent their first screening or diagnostic colonoscopy in our center between 2020 and 2023 were recruited, in which 4592 were in the derivation cohort and 1891 formed a validation cohort. Multivariate logistic regression was used to investigate the risk factors of ACN in the derivation cohort based on endoscopic findings, and a new scoring model for ACN prediction was developed. The discriminatory capability of the scoring model was validated by the validation cohort. RESULTS: Age, male gender, smoking, and wait-to-hip ratio were identified as independent risk factors for ACN, and a 7-point scoring model was developed. The prevalence of ACN was 3.3%, 9.3% and 18.5% in participants with scores of 0-2 [low risk (LR)], 3-4 [moderate risk (MR)], and 5-7 [high risk (HR)], respectively, in the derivation cohort. With the scoring model, 49.9%, 38.4%, and 11.7% of patients in the validation cohort were categorized as LR, MR, and HR, respectively. The corresponding prevalence rates of ACN were 5.0%, 10.3%, and 17.6%, respectively. The C-statistic of the new scoring model was 0.66, which was higher than that of the Asia-Pacific Colorectal Screening model (0.63). CONCLUSION: A modified scoring model incorporating waist-hip ratio has an improved predictive performance in the prediction of ACN.
Authors: Zhong-Hui Liu, Zong-Lin Cai, Xiao-Jun Tong, Yang-Yang Sun, Xin-Yu Zhuang, Xue-Fei Yang, Joe KM Fan
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410473935
