Evaluation of a prediction model for colorectal cancer: retrospective analysis of 2.5 million patient records

Abstract

Earlier detection of colorectal cancer greatly improves prognosis, largely through surgical excision of neoplastic polyps. These include benign adenomas which can transform over time to malignant adenocarcinomas. This progression may be associated with changes in full blood count indices. An existing risk algorithm derived in Israel stratifies individuals according to colorectal cancer risk using full blood count data, but has not been validated in the UK. We undertook a retrospective analysis using the Clinical Practice Research Datalink. Patients aged over 40 with full blood count data were risk-stratified and followed up for a diagnosis of colorectal cancer over a range of time intervals. The primary outcome was the area under the receiver operating characteristic curve for the 18–24-month interval. We also undertook a case–control analysis (matching for age, sex, and year of risk score), and a cohort study of patients undergoing full blood count testing during 2012, to estimate predictive values. We included 2,550,119 patients. The area under the curve for the 18–24-month interval was 0.776 [95% confidence interval (CI): 0.771, 0.781]. Performance improves as the time interval reduces. The area under the curve for the age-matched case–control analysis was 0.583 [0.574, 0.591]. For the population risk-scored in 2012, the positive predictive value at 99.5% specificity was 8.8% with negative predictive value 99.6%. The algorithm offers an additional means of identifying risk of colorectal cancer, and could support other approaches to early detection, including screening and active case finding.

Early detection of colorectal cancer is associated with improved survival, but patients are often diagnosed at a stage beyond surgical cure. We have validated in UK primary care a new risk algorithm for colorectal cancer that draws on full blood count data. Changes in full blood count indices enable identification of patients at risk, offering a new approach to support early detection.

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