Risk-adjusted colorectal cancer screening using the FIT and routine screening data: development of a risk prediction model.

BACKGROUND: The faecal immunochemical test (FIT) is replacing the guaiac faecal occult blood test in colorectal cancer screening. Increased uptake and FIT positivity will challenge colonoscopy services. We developed a risk prediction model combining routine screening data with FIT concentration to improve the accuracy of screening referrals.

METHODS: Multivariate analysis used complete cases of those with a positive FIT (⩾20 μg g-1 ) and diagnostic outcome (n=1810; 549 cancers and advanced adenomas). Logistic regression was used to develop a risk prediction model using the FIT result and screening data: age, sex and previous screening history. The model was developed further using a feedforward neural network. Model performance was assessed by discrimination and calibration, and test accuracy was investigated using clinical sensitivity, specificity and receiver operating characteristic curves.

RESULTS: Discrimination improved from 0.628 with just FIT to 0.659 with the risk-adjusted model (P=0.01). Calibration using the Hosmer-Lemeshow test was 0.90 for the risk-adjusted model. The sensitivity improved from 30.78% to 33.15% at similar specificity (FIT threshold of 160 μg g-1 ). The neural network further improved model performance and test accuracy.

CONCLUSIONS: Combining routinely available risk predictors with the FIT improves the clinical sensitivity of the FIT with an increase in the diagnostic yield of high-risk adenomas.