Comparison of multiple statistical models for the development of clinical prediction scores to detect advanced colorectal neoplasms in asymptomatic Thai patients

A good clinical prediction score can help in the risk stratification of patients with colorectal cancer (CRC) undergoing colonoscopy screening. The aim of our study was to compare model performance of binary logistic regression (BLR), polytomous logistic regression (PLR), and classification and regression tree (CART) between the clinical prediction scores of advanced colorectal neoplasia (ACN) in asymptomatic Thai patients. We conducted a cross-sectional study of 1311 asymptomatic Thai patients to develop a clinical prediction model. The possible predictive variables included sex, age, body mass index, family history of CRC in first-degree relatives, smoking, diabetes mellitus, and the fecal immunochemical test in the univariate analysis. Variables with a P value of .1 were included in the multivariable analysis, using the BLR, CART, and PLR models. Model performance, including the area under the receiver operator characteristic curve (AUROC), was compared between the model types. ACN was diagnosed in 53 patients (4.04%). The AUROCs were not significantly different between the BLR and CART models for ACN prediction with an AUROC of 0.774 (95% confidence interval [95% CI]: 0.706–0.842) and 0.765 (95% CI: 0.698–0.832), respectively (P = .712). A significant difference was observed between the PLR and CART models in predicting average to moderate ACN risk with an AUROC of 0.767 (95% CI: 0.695–0.839 vs AUROC 0.675 [95% CI: 0.599–0.751], respectively; P = .009). The BLR and CART models yielded similar accuracies for the prediction of ACN in Thai patients. The PLR model provided higher accuracy for ACN prediction than the CART model.