Prediction of advanced colorectal neoplasia based on metabolic parameters among symptomatic patients.

Abstract

Worldwide, colorectal cancer (CRC) is the third most common cancer and ranks second among the leading causes of cancer death. This study aims to devise and validate a scoring system based on metabolic parameters to predict the risk of advanced colorectal neoplasia (ACN) in a large Chinese population. This was a cohort study of 495584 symptomatic subjects aged 40years or older who have received colonoscopy in Hong Kong from 1997 to 2017. The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic curve. Age, male gender, inpatient setting, abnormal aspartate transaminase/alanine transaminase, white blood cell, plasma gamma-glutamyl transferase, high-density lipoprotein cholesterol, triglycerides, and hemoglobin A1c were significantly associated with ACN. A scoring of <2.65 was designated as "low risk (LR)." Scores at 2.65 or above had prevalence higher than the overall prevalence and hence were assigned as "high risk (HR)." The prevalence of ACN was 32% and 11%, respectively, for HR and LR groups. The AUC for the risk score in the derivation and validation cohort was 70.12%. This study has validated a simple, accurate, and easy-to-use scoring algorithm, which has a high discriminatory capability to predict ACN in symptomatic patients. Future studies should examine its predictive performance in other population groups.