Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Feb 7, 2024; 30(5): 450-461
Published online Feb 7, 2024. doi: 10.3748/wjg.v30.i5.450
Development and validation of a prediction model for early screening of people at high risk for colorectal cancer
Ling-Li Xu, Yi Lin, Li-Yuan Han, Yue Wang, Jian-Jiong Li, Xiao-Yu Dai
Ling-Li Xu, Jian-Jiong Li, Xiao-Yu Dai, Department of General Surgery, Ningbo No. 2 Hospital, Ningbo 315000, Zhejiang Province, China
Yi Lin, Center for Health Economics, Faculty of Humanities and Social Sciences, University of Nottingham, Ningbo 315100, Zhejiang Province, China
Li-Yuan Han, Department of Global Health, Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo 315000, Zhejiang Province, China
Yue Wang, School of Public Health, Medical College of Soochow University, Suzhou 215123, Jiangsu Province, China
Co-first authors: Ling-Li Xu and Yi Lin.
Co-corresponding authors: Jian-Jiong Li and Xiao-Yu Dai.
Author contributions: Xu LL, Lin Y, Li JJ and Dai XY participated in the study design; Xu LL and Lin Y statistically analyzed, interpreted, and drafted the manuscript; Xu LL and Lin Y revised the manuscript; Han LY and Wang Y contributed to data collection and organization; all authors contributed to the revision of the final manuscript and approved the final version of the manuscript; Li JJ and Dai XY provided financial support and study supervision.
Supported by the Project of NINGBO Leading Medical Health Discipline, No. 2022-B11; Ningbo Natural Science Foundation, No. 202003N4206; and Public Welfare Foundation of Ningbo, No. 2021S108.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the Ningbo No. 2 Hospital.
Informed consent statement: All involved persons gave their informed written consent prior to study inclusion and any and all details that might disclose the identity of the subjects under study were omitted.
Conflict-of-interest statement: The authors declare no potential conflicts of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xiao-Yu Dai, MD, Chief Physician, Department of General Surgery, Ningbo No. 2 Hospital, No. 41 Northwest Street, Haishu Zone, Ningbo 315000, Zhejiang Province, China. daixiaoyu1968@163.com
Received: October 16, 2023
Peer-review started: October 16, 2023
First decision: December 6, 2023
Revised: December 19, 2023
Accepted: January 12, 2024
Article in press: January 12, 2024
Published online: February 7, 2024
Abstract
BACKGROUND

Colorectal cancer (CRC) is a serious threat worldwide. Although early screening is suggested to be the most effective method to prevent and control CRC, the current situation of early screening for CRC is still not optimistic. In China, the incidence of CRC in the Yangtze River Delta region is increasing dramatically, but few studies have been conducted. Therefore, it is necessary to develop a simple and efficient early screening model for CRC.

AIM

To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.

METHODS

Data of 64448 participants obtained from Ningbo Hospital, China between 2014 and 2017 were retrospectively analyzed. The cohort comprised 64448 individuals, of which, 530 were excluded due to missing or incorrect data. Of 63918, 7607 (11.9%) individuals were considered to be high risk for CRC, and 56311 (88.1%) were not. The participants were randomly allocated to a training set (44743) or validation set (19175). The discriminatory ability, predictive accuracy, and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic (ROC) curves and calibration curves and by decision curve analysis. Finally, the model was validated internally using a bootstrap resampling technique.

RESULTS

Seven variables, including demographic, lifestyle, and family history information, were examined. Multifactorial logistic regression analysis revealed that age [odds ratio (OR): 1.03, 95% confidence interval (CI): 1.02-1.03, P < 0.001], body mass index (BMI) (OR: 1.07, 95%CI: 1.06-1.08, P < 0.001), waist circumference (WC) (OR: 1.03, 95%CI: 1.02-1.03 P < 0.001), lifestyle (OR: 0.45, 95%CI: 0.42-0.48, P < 0.001), and family history (OR: 4.28, 95%CI: 4.04-4.54, P < 0.001) were the most significant predictors of high-risk CRC. Healthy lifestyle was a protective factor, whereas family history was the most significant risk factor. The area under the curve was 0.734 (95%CI: 0.723-0.745) for the final validation set ROC curve and 0.735 (95%CI: 0.728-0.742) for the training set ROC curve. The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.

CONCLUSION

The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age, BMI, WC, lifestyle, and family history exhibited high accuracy.

Keywords: Colorectal cancer, Early screening model, High-risk population, Nomogram model, Questionnaire survey, Dietary habit, Living habit

Core Tip: This was the first large-scale study to investigate early screening for detection of colorectal cancer (CRC) in Ningbo, China, which was part of the national early screening CRC program. The study focused on collecting information on the general population who attended annual health checks. Our findings showed that the area under the curve was 0.734 for the final validation set receiver operating characteristic (ROC) curve and 0.735 for the training set ROC curve. Therefore, we developed an early screening model with high accuracy for CRC.