Background: Although a fecal immunochemical test (FIT) allows quantitation of fecal hemoglobin (f-Hb), it is typically used as a binary result to identify subjects above a predetermined cut-off concentration.
Aims: To investigate whether f-Hb concentration is useful for risk stratification of advanced colorectal neoplasia (CRN).
Methods: This was a retrospective study conducted in a university hospital in Korea. Of 34,547 participants who underwent FIT from June 2013 to May 2015, 1532 (4.4%) showed positive results (≥100ng Hb/ml). Of participants with positive results, 738 subjects aged ≥50 years who underwent colonoscopy were analyzed.
Results: Increasing quartile of f-Hb concentration (Q4 vs. Q1; odds ratio, 3.87; 95% confidence interval, 2.36-6.34), as well as older age, male sex, smoking, and metabolic syndrome (MetS), was significantly associated with a higher prevalence of advanced CRN. Risk for advanced CRN increased 5.13-, 4.27-, 5.12-, and 7.15-fold, respectively, among individuals with f-Hb in the fourth quartile who had risk factors such as age ≥70 years, male sex, smoking, and MetS compared with individuals with first quartile levels of f-Hb who did not have those risk factors.
Conclusion: In addition to age, sex, smoking status, and MetS, f-Hb concentration in individuals with positive results from FIT can be used to stratify the probability of detection of advanced CRN.
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Article Synopsis
- Two-step screening for colorectal cancer (CRC) begins with a fecal immunochemical test (FIT) to identify individuals who may need a follow-up colonoscopy based on their fecal hemoglobin concentration (f-Hb).
- The study emphasizes the need to adjust the f-Hb positivity threshold in order to optimize detection rates for colorectal neoplasia while managing the number of follow-up colonoscopies required. This balance impacts clinical accuracy, colonoscopy workload, and overall effectiveness in reducing CRC incidence and mortality.
- As non-invasive screening methods evolve, new biomarkers from various biological samples are being developed, employing machine learning for better predictive accuracy in identifying individuals at risk for CRC.
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