Thomas F Imperiale 1,2,3 , Patrick O Monahan 4 , Timothy E Stump 4 , David F Ransohoff 5 Show Affiliations »
Abstract
OBJECTIVE: Knowing risk for advanced colorectal neoplasia (AN) could help patients and providers choose among screening tests, improving screening efficiency and uptake. We created a risk prediction model for AN to help decide which test might be preferred, a use not considered for existing models. DESIGN: Average-risk 50-to-80-year olds undergoing first-time screening colonoscopy were recruited from endoscopy units in Indiana. We measured sociodemographic and physical features, medical and family history and lifestyle factors and linked these to the most advanced finding. We derived a risk equation on two-thirds of the sample and assigned points to each variable to create a risk score. Scores with comparable risks were collapsed into risk categories. The model and score were tested on the remaining sample. RESULTS: Among 3025 subjects in the derivation set (mean age 57.3 (6.5) years; 52% women), AN prevalence was 9.4%. The 13-variable model (c-statistic=0.77) produced three risk groups with AN risks of 1.5% (95% CI 0.72% to 2.74%), 7.06% (CI 5.89% to 8.38%) and 27.26% (CI 23.47% to 31.30%) in low-risk, intermediate-risk and high-risk groups (p value <0.001), containing 23%, 59% and 18% of subjects, respectively. In the validation set of 1475 subjects (AN prevalence of 8.4%), model performance was comparable (c-statistic=0.78), with AN risks of 2.73% (CI 1.25% to 5.11%), 5.57% (CI 4.12% to 7.34%) and 25.79% (CI 20.51% to 31.66%) in low-risk, intermediate-risk and high-risk subgroups, respectively (p<0.001), containing proportions of 23%, 59% and 18%. CONCLUSION: Among average-risk persons, this model estimates AN risk with high discrimination, identifying a lower risk subgroup that may be screened non-invasively and a higher risk subgroup for which colonoscopy may be preferred. The model could help guide patient-provider discussions of screening options, may increase screening adherence and conserve colonoscopy resources. © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.
OBJECTIVE: Knowing risk for advanced colorectal neoplasia (AN) could help patients and providers choose among screening tests, improving screening efficiency and uptake. We created a risk prediction model for AN to help decide which test might be preferred, a use not considered for existing models. DESIGN: Average-risk 50-to-80-year olds undergoing first-time screening colonoscopy were recruited from endoscopy units in Indiana. We measured sociodemographic and physical features, medical and family history and lifestyle factors and linked these to the most advanced finding. We derived a risk equation on two-thirds of the sample and assigned points to each variable to create a risk score. Scores with comparable risks were collapsed into risk categories. The model and score were tested on the remaining sample. RESULTS: Among 3025 subjects in the derivation set (mean age 57.3 (6.5) years; 52% women), AN prevalence was 9.4%. The 13-variable model (c-statistic=0.77) produced three risk groups with AN risks of 1.5% (95% CI 0.72% to 2.74%), 7.06% (CI 5.89% to 8.38%) and 27.26% (CI 23.47% to 31.30%) in low-risk, intermediate-risk and high-risk groups (p value <0.001), containing 23%, 59% and 18% of subjects, respectively. In the validation set of 1475 subjects (AN prevalence of 8.4%), model performance was comparable (c-statistic=0.78), with AN risks of 2.73% (CI 1.25% to 5.11%), 5.57% (CI 4.12% to 7.34%) and 25.79% (CI 20.51% to 31.66%) in low-risk, intermediate-risk and high-risk subgroups, respectively (p<0.001), containing proportions of 23%, 59% and 18%. CONCLUSION: Among average-risk persons, this model estimates AN risk with high discrimination, identifying a lower risk subgroup that may be screened non-invasively and a higher risk subgroup for which colonoscopy may be preferred. The model could help guide patient-provider discussions of screening options, may increase screening adherence and conserve colonoscopy resources. © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.
Entities: Chemical
Keywords:
cancer prevention; colonoscopy; colorectal cancer screening
Mesh： See more »
Year: 2020
PMID： 32994311 DOI： 10.1136/gutjnl-2020-321698
Source DB: PubMed Journal: Gut ISSN： 0017-5749 Impact factor: 23.059