Colon age: A metric for whether and how to screen male Veterans for early-onset colorectal cancer

Abstract: We aimed to develop a metric for estimating risk for early-onset colorectal cancer (EOCRC) to help decide whether and how to screen persons < age 50. We used risk prediction models derived and validated on male Veterans to calculate the relative risks (RRs) for 6 scenarios: one low-risk scenario (no risk factors present), four intermediate risk scenarios (some factors present), and one high-risk scenario (all factors present) for three age groups (35-39, 40-44, and 45-49 years). For each scenario, we estimated absolute CRC risk using SEER CRC incidence rates and each scenario's RR. We identified the current SEER 5-year age group to which the revised estimate was closest and refer to the midpoint of this group as the "colon age". When the revised estimate was ≥ that for 50-54-year-olds and for 70-74-year-olds, respective recommendations were made for (any) CRC screening and screening with colonoscopy. Among the scenarios, there was inconsistency between the two models for the 35-39 and 40-44 age groups, with only the 15-variable model recommending screening for the higher-risk 35-to-39-year-olds. Both models recommended screening for some intermediate risk and high-risk 40-44-year-olds. The models were well-aligned on whether and how to screen most 45-49-year-olds. Using risk factors for EOCRC with CRC incidence rates, "colon age" may be useful for shared decision making about whether and how to screen male Veterans < 50 years. For 45-49-year-olds, the 7-variable model may be preferred by patients, providers, and health systems.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.