A Risk Prediction Index for Advanced Colorectal Neoplasia at Screening Colonoscopy

Abstract

Background Eliciting patient preferences within the context of shared decision-making has been advocated for colorectal cancer screening. Risk stratification for advanced colorectal neoplasia (ACN) might facilitate more effective shared decision-making when selecting an appropriate screening option. Our objective was to develop and validate a clinical index for estimating the probability ACN at screening colonoscopy. Methods We conducted a cross-sectional analysis of 3,543 asymptomatic, mostly average risk patients 50–79 years of age undergoing screening colonoscopy at two urban safety net hospitals. Predictors of ACN were identified using multiple logistic regression. Model performance was internally validated using bootstrapping methods. Results The final index consisted of 5 independent predictors of risk (age, smoking, alcohol intake, height and a combined sex/race/ethnicity variable). Smoking was the strongest predictor (net reclassification improvement [NRI], 8.4%) and height the weakest (NRI, 1.5%). Using a simplified weighted scoring system based on 0.5 increments of the adjusted odds ratio, the risk of ACN ranged from 3.2% (95% CI, 2.6 to 3.9) for the low-risk group (score ≤ 2) to 8.6% (95% CI, 7.4–9.7) for the intermediate/high-risk group (score 3–11). The model had moderate to good overall discrimination (C-statistic, 0.69; 95% CI, 0.66–0.72) and good calibration (P=0.73 to 0.93). Conclusions A simple 5-item risk index based on readily available clinical data accurately stratifies average-risk patients into low- and intermediate/high-risk categories for ACN at screening colonoscopy. Uptake into clinical practice could facilitate more effective shared decision-making for CRC screening, particularly in situations where patient and provider test preferences differ.

Performance of individual and joint risk stratification by an environmental risk score and a genetic risk score in a colorectal cancer screening setting

“…During the last decades, the number of incident cases has increased in most countries, with a tendency to rise in the coming years . Studies on CRC risk have identified potential factors associated with the disease, and a number of studies have proposed scoring tools to identify individuals at high risk of colorectal neoplasm . Risk scores for CRC or advanced colorectal neoplasm (ACRN) have included environmental risk factors (demographic, family history and lifestyle) or genetic susceptibility loci .…”

Risk prediction models for colorectal cancer: A Scoping review

“…However, 11 models are specific for females [20,22,35,42,45,50] while 10 models are specific for males [20,22,35,45,48]. Although many models have not restricted application to a specific age limit, 11 models have been developed from populations aged 30 years and above [15,20,42,43,50], 10 from populations aged 50 years and above [18,25,26,45,51] and seven from populations aged 40 years and above [14,19,23,24,36,38,52]. In addition, one model has used a population above 20 years [35] while another, populations lower than 80 years [34].…”

“…Socio-demographic characteristics of the study populations A majority of the risk prediction models have been developed in United States of America [18,22,33,38,39,42,44,45,48,49,50,51,53,54] followed by Korea [14,17,20,35]. Three models have been developed in China [16,23,36] and Japan [40] while, Netherlands [25,26], Germany [21], Taiwan [32,46] and United Kingdom [31,34] have developed two models each.…”

“…A majority (n=35) of models include variables obtained through a questionnaire [18,19,21,22,23,24,25,26,27,28,30,33,36,37,38,39,40,41,42,43,44,45,46,49,50,51,53,54]. The questionnaires range from those with only one or two simple questions to those with detailed questions on factors such as diet, physical activity, alcohol and smoking habits and past medical facts.…”

“…A majority of the studies have used multiple logistic regression to identify the risk predictors [16,27,28,30,33,34,35,41,43,47,50,52] followed by the allocation of the risk points, based on the values of beta-coefficients [14,15,18,21,23,24,36,37,39,45,46,48]. Fourteen models have incorporated Cox proportional hazards regression to develop the risk model and the score [17,19,20,22,38,40].…”