Background: Colorectal cancer (CRC) is the 3rd leading cancer killer among men and women in the US. The Strategies and Opportunities to STOP Colon Cancer in Priority Populations (STOP CRC) project aimed to increase CRC screening among patients in Federally Qualified Health Centers (FQHCs) through a mailed fecal immunochemical test (FIT) outreach program. However, rates of completion of the follow-up colonoscopy following an abnormal FIT remain low. We developed a multivariable prediction model using data available in the electronic health record to assess the probability of patients obtaining a colonoscopy following an abnormal FIT test.
Methods: To assess the probability of obtaining a colonoscopy, we used Cox regression to develop a risk prediction model among a retrospective cohort of patients with an abnormal FIT result.
Results: Of 1596 patients with an abnormal FIT result, 556 (34.8%) had a recorded colonoscopy within 6 months. The model shows an adequate separation of patients across risk levels for non-adherence to follow-up colonoscopy (bootstrap-corrected C-statistic > 0.63). The refined model included 8 variables: age, race, insurance, GINI income inequality, long-term anticoagulant use, receipt of a flu vaccine in the past year, frequency of missed clinic appointments, and clinic site. The probability of obtaining a follow-up colonoscopy within 6 months varied across quintiles; patients in the lowest quintile had an estimated 18% chance, whereas patients in the top quintile had a greater than 55% chance of obtaining a follow-up colonoscopy.
Conclusions: Knowing who is unlikely to follow-up on an abnormal FIT test could help identify patients who need an early intervention aimed at completing a follow-up colonoscopy.
Trial registration: This trial was registered at ClinicalTrials.gov ( NCT01742065 ) on December 5, 2012. The protocol is available.
Keywords: Colonoscopy; Colorectal cancer screening; Fecal immunochemical test; Follow-up colonoscopy; Multivariable prediction model; Precision medicine; Predictive analytics.