COLOFIT: Development and Internal‐External Validation of Models Using Age, Sex, Faecal Immunochemical and Blood Tests to Optimise Diagnosis of Colorectal Cancer in Symptomatic Patients

ABSTRACT Background Colorectal cancer (CRC) is the third most common cancer in the United Kingdom and the second largest cause of cancer death. Aim To develop and validate a model using available information at the time of faecal immunochemical testing (FIT) in primary care to improve selection of symptomatic patients for CRC investigations. Methods We included all adults (≥ 18 years) referred to Nottingham University Hospitals NHS Trust between 2018 and 2022 with symptoms of suspected CRC who had a FIT. Predicted 1‐year CRC diagnosis were calculated, and externally validated, using Cox proportional hazards modelling with selected multiple fractional polynomial transformations for age, faecal haemoglobin concentration (f‐Hb) value, mean corpuscular volume (MCV), platelet count and sex. Results At a CRC risk threshold of 0.6% (equivalent to f‐Hb = 10 μg Hb/g (μg/g)) overall performance of the validated model across age strata using Harrell's C index was ≥ 0.91% (overall C‐statistic 93%, 95% CI 92%–95%) with acceptable calibration. Using this model yields similar numbers of detected and missed cancers, but requires ~20% fewer investigations than a f‐Hb ≥ 10 μg/g strategy. For approximately 100,000 people per year with symptoms of suspected CRC, we predict it might save > 4500 colonoscopies with no evidence that more cancers would be missed if we used our model compared to using FIT f‐Hb ≥ 10 μg/g. Conclusions Including age, sex, MCV, platelets and f‐Hb in a survival analysis model to predict the risk of CRC yields greater diagnostic utility than a simple binary cut off f‐Hb ≥ 10 μg/g. Including age, sex, MCV, platelets and f‐Hb in a survival analysis model to predict the risk of colorectal cancer reduces false positives compared to a simple binary cut off f‐Hb ≥ 10 μgHb/g which may decrease the burden on endoscopy whilst maintaining diagnostic accuracy.