Designing a Predictive Model for Colorectal Neoplasia Diagnosis Based on Clinical and Laboratory Findings in Colonoscopy Candidate Patients.

Abstract

Health authorities have expanded two strategies to diminish CRC-related influence: CR screening and improve diagnostic process in symptomatic patients. The aim of the current study is to design a predictive model to identify the most important risk factors that can efficiently predict patients who have high risk of colorectal neoplasia. A cross-sectional study was constructed to include all patients who had positive test for FIT or had one or more risk factors for colorectal cancer based on the guidelines of detecting high-risk groups for colorectal cancer in Iran. Multivariable binary logistic regression model was constructed for prediction of colorectal neoplasia. We used sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratio to check the accuracy. The Hosmer-Lemeshow test, chi-square test, and p value were used to determine the precision of model. Following an AIC stepwise selection model, only nine potential variables, namely gender, watery diarrhea, IBD, abdominal pain, melena, body mass index, depression drug, anti-inflammatory drug, and age, were found to be a predictor of colorectal neoplasia. The best cut-point probability in the final model was 0.27 and results of sensitivity and specificity, based on maximizing these two criteria, were 66% and 62%, respectively. Overall, our model prediction was comparable with other risk prediction models for colorectal cancer. It had a modest discriminatory power to distinguish an individual's neoplasia colorectal risk.