Predictors of treatment refusal in patients with colorectal cancer: A systematic review

This systematic review was conducted to investigate predictors of treatment refusal in colorectal cancer (CRC) patients. An understanding of these predictors would inform statistical models for the identification of high-risk patients who might benefit from interventions that seek to improve treatment compliance. We performed a search of PubMed and Scopus to identify potentially relevant studies on predictors of treatment refusal in CRC patients that were published between January 1, 2000 and December 31, 2021. We screened manuscripts using predefined eligibility criteria. Information on study design, study location, patient characteristics, treatments, rates and predictors of treatment refusal, and the impact of treatment refusal on mortality or survival were collected from eligible studies. Study quality was assessed using the Newcastle-Ottawa score. The overall findings of the review process were summarized using descriptive statistics and a narrative synthesis. A total of 13 studies were included in this review. Ten studies reported on refusal of CRC surgery, refusal rate: 0.25%–3.26%; three studies reported on chemotherapy refusal (one of which reported on both surgery and chemotherapy refusal), refusal rate: 7.8%–41.5%; and one study reported on refusal of any cancer treatment, refusal rate: 8.7%. The bulk of the published literature confirmed the harmful association between treatment refusal and poor survival outcomes in CRC patients. Frequently cited predictors of treatment refusal included patient demographic characteristics (age, race, gender), clinical characteristics (disease stage, comorbidity), and factors that impact access to cancer care services (healthcare insurance, facility level). Potentially high rates of treatment refusal pose a challenge to CRC control. This review has identified several factors which must be considered when attempting to reduce treatment refusal in CRC patients. Furthermore, these factors should be tested as components of predictive risk models for this important outcome.