Risk prediction models for colorectal cancer in people with symptoms: A systematic review

Abstract

Background: Colorectal cancer (CRC) is the fourth leading cause of cancer-related death in Europe and the United States. Detecting the disease at an early stage improves outcomes. Risk prediction models which combine multiple risk factors and symptoms have the potential to improve timely diagnosis. The aim of this review is to systematically identify and compare the performance of models that predict the risk of primary CRC among symptomatic individuals. Methods: We searched Medline and EMBASE to identify primary research studies reporting, validating or assessing the impact of models. For inclusion, models needed to assess a combination of risk factors that included symptoms, present data on model performance, and be applicable to the general population. Screening of studies for inclusion and data extraction were completed independently by at least two researchers. Results: Twelve thousand eight hundred eight papers were identified from the literature search and three through citation searching. 18 papers describing 15 risk models were included. Nine were developed in primary care populations and six in secondary care. Four had good discrimination (AUROC > 0.8) in external validation studies, and sensitivity and specificity ranged from 0.25 and 0.99 to 0.99 and 0.46 depending on the cut-off chosen. Conclusions: Models with good discrimination have been developed in both primary and secondary care populations. Most contain variables that are easily obtainable in a single consultation, but further research is needed to assess clinical utility before they are incorporated into practice.