Full BLOOD count TRends for colorectal cAnCer deteCtion (BLOODTRACC): external validation of colorectal cancer prediction models in English primary care
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Introduction
Colorectal cancer has low survival rates when diagnosed late-stage. We previously developed sex-specific dynamic risk prediction models utilising trends in the full blood count (FBC), a blood test commonly performed in primary care, to support early detection. We aimed to externally validate these prediction models.
Methods
We performed a hybrid case-control and cohort study of patients with at least one FBC test. We first excluded FBCs within two years before diagnosis (cases) or study exit (controls) and selected the most recent FBC as the baseline test per patient from the resulting data. Patients were aged at least 40 years at baseline and had no history of colorectal cancer. The models included age (years) at baseline and simultaneous trends over historical haemoglobin, mean corpuscular volume (MCV), and platelet measurements measured over five years before baseline to inform two-year risk of colorectal cancer diagnosis. Performance measures included the c-statistic and calibration slope.
Results
We included 2,956,977 males and 3,561,349 females, with 0.4% (n=12,578) and 0.3% (n=11,939) diagnosed with colorectal cancer, respectively. The c-statistic (95% CI) was 0.73 (0.72-0.73) for males and 0.74 (0.74-0.75) for females. The calibration slope (95% CI) was 0.92 (0.89-0.94) for males and 0.95 (0.93-0.98) for females. Calibration was good in subgroups of patient data, except under-predicted risk in those aged 70+ years, White individuals, and those with higher IMD. The c-statistic (95% CI) was similar regardless of the number of FBCs used to define trend and increased as the longitudinal trend window increased until around 2.5-3.0 years for men (0.73 (0.71-0.74)) and 3.0-3.5 years for women (0.73 (0.72-0.75)) and decreased with increasing longitudinal windows thereafter.
Conclusion
Utilising temporal changes in the FBC test could enhance risk stratification for colorectal cancer. Further research may highlight approaches for improving predictive performance further.
