Predictive Modeling for Colorectal Polyps in Average-Risk Asymptomatic Adults: Optimizing Screening Colonoscopy Decisions

Objective: To develop a risk prediction model for colorectal polyps in asymptomatic individuals and provide evidence-based guidance for colonoscopy screening prioritization within health examination cohorts. Methods: We retrospectively analyzed 1,619 participants undergoing health examinations at Ningbo Zhenhai Lianhua Hospital (October 2022-December 2024). Based on endoscopic findings, 847 individuals with colorectal polyps were assigned to the case group, and 772 polyp-free individuals comprised the control group. Risk factors were identified through univariate and multivariate logistic regression analyses. A predictive nomogram was constructed using significant multivariate predictors. Model discrimination was evaluated via the receiver operating characteristic (ROC) curve and concordance index (C-index), while clinical utility was assessed using decision curve analysis (DCA). Results: Multivariate analysis identified the following independent risk factors (all P<0.05): male sex (OR=2.587; 95% CI: 1.932-3.466), age ≥40 years (OR=4.821; 95% CI: 3.285-7.076) , elevated fasting plasma glucose (OR=1.441; 95% CI: 1.125-1.846) , hyperhomocysteinemia (OR=1.402; 95% CI: 1.031-1.908), carotid plaque (OR=1.614; 95% CI: 1.268-2.054) and thyroid nodules (OR=1.575; 95% CI: 1.243-1.996) . The nomogram exhibited discriminative ability for predicting polyps, with an AUC of 0.752 (95% CI: 0.729-0.776) and a C-index of 0.752 (95% CI: 0.732-0.776). Calibration curves indicated satisfactory agreement between predicted and observed probabilities. DCA confirmed significant net clinical benefit across threshold probabilities. Conclusion: This study establishes a clinically applicable nomogram with moderate predictive performance for colorectal polyps, which may facilitate risk-stratified colonoscopy screening decisions in health examination populations.