Development and validation of a clinical score for predicting risk of adenoma at screening colonoscopy

Abstract

Background: Currently, no clinical tools use demographic and risk factor information to predict the risk of finding an adenoma in individuals undergoing colon cancer screening. Such a tool would be valuable for identifying those who would most benefit from screening colonoscopy.

Methods: We used baseline data from men and women who underwent screening colonoscopy from the randomized, multicenter National Colonoscopy Study (NCS) to develop and validate an adenoma risk model. The study, conducted at three sites in the United States (Minneapolis, MN; Seattle, WA; and Shreveport, LA) asked all participants to complete baseline questionnaires on clinical risk factors and family history. Model parameters estimated from logistic regression yielded an area under the receiver operating characteristic curve (AUROCC) used to assess prediction.

Results: Five hundred forty-one subjects were included in the development model, and 1,334 in the validation of the risk score. Variables in the prediction of adenoma risk for colonoscopy screening were age (likelihood ratio test for overall contribution to model, P < 0.001), male sex (P < 0.001), body mass index (P < 0.001), family history of at least one first-degree relative with colorectal cancer (P = 0.036), and smoking history (P < 0.001). The adjusted AUROCC of 0.67 [95% confidence interval (CI), 0.61-0.74] for the derivation cohort was not statistically significantly different from that in the validation cohort. The adjusted AUROCC for the entire cohort was 0.64 (95% CI, 0.60-0.67).

Conclusion: We developed and validated a simple well-calibrated risk score.

Impact: This tool may be useful for estimating risk of adenomas in screening eligible men and women.

Conflict of interest statement

CONFLICTS OF INTEREST

The authors disclose no conflicts.

©2015 American Association for Cancer Research.

Figures

Figure 1

Odds ratios (unadjusted) estimated from the logistic model with 95% CIs.

Figure 2

The unadjusted, all-data ROCC based on our model Bootstrap-adjusted sensitivities are shown as black dots along with 95% CIs for given values of (1 − specificity). Our final logistic model was: logit[Prob(adenoma)] = −1.536 + 0.057(bmi) − 0.026(smoker) + 0.011(pack years) + 0.13(aspirin) + 0.082(age) + 0.367(CRC FDR) + 0.334(CRC SDR) + 0.526(male) − 0.432(NSAID) + 0.232(nonwhite) − 0.053(age*aspirin) + 0.011(age*NSAID) − 0.044(age*male) − 0.025(age*bmi) − 0.373(male*aspirin) + 0.314(male*NSAID)

Figure 3

Plot of estimated risk of adenoma detection (horizontal axis) by proportion of the population at or below that risk (vertical axis). For example, approximately 40% of our cohort had a predicted risk