Colon Age: A Metric for Whether and How to Screen Male Veterans for Early-Onset Colorectal Cancer.

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 RRs for six scenarios: one low-risk scenario (no risk factors present), four intermediate risk scenarios (some risk factors present), and one high-risk scenario (all risk factors present) for three age groups (35-39, 40-44, and 45-49 years). For each scenario, we estimated absolute colorectal cancer risk using Surveillance Epidemiology and End Results colorectal cancer incidence rates and each scenario's RR. We identified the current Surveillance Epidemiology and End Results 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 equals or exceeds that for 50- to 54-year-olds and for 70- to 74-year-olds, respective recommendations were made for (any) colorectal cancer screening and screening with colonoscopy. Among the scenarios, there was inconsistency between the two models for the 35 to 39 and 40 to 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- to 44-year-olds. The models were well aligned on whether and how to screen most 45- to 49-year-olds. Using risk factors for EOCRC with colorectal cancer incidence rates, "colon age" may be useful for shared decision-making about whether and how to screen male veterans <50 years. For 45- to 49-year-olds, the 7-variable model may be preferred by patients, providers, and health systems. Prevention Relevance: A new metric known as "colon age" expresses risk of EOCRC based on biological risk and may be useful for providers to explain and for patients to understand colorectal cancer risk when considering whether and how to be screened for colorectal cancer prior to age 45 or 50.

Colon Age: A Metric for Whether and How to Screen Male Veterans for Early-Onset Colorectal Cancer.

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.

Mortality After Postcolonoscopy Colorectal Cancer in the Veterans Affairs Health Care System.

Importance: Postcolonoscopy colorectal cancer (PCCRC) refers to colorectal cancer (CRC) diagnosed after a colonoscopy in which no cancer was found and is reflective of colonoscopy quality at the individual and system levels. Colonoscopy is widely performed in the Veterans Affairs (VA) health care system, but the prevalence of PCCRC and its associated mortality are unknown. Objective: To examine PCCRC prevalence and its all-cause mortality (ACM) and CRC-specific mortality (CSM) within the VA health care system. Design, Setting, and Participants: This retrospective cohort study used VA-Medicare administrative data to identify 29 877 veterans aged 50 to 85 years with newly diagnosed CRC between January 1, 2003, and December 31, 2013. Patients whose colonoscopy occurred less than 6 months before CRC diagnosis with no other colonoscopy within the previous 36 months were categorized as having detected CRC (DCRC). Those who had a colonoscopy that did not detect CRC between 6 and 36 months before CRC diagnosis were categorized as having postcolonoscopy CRC (PCCRC-3y). A third group included patients with CRC and no colonoscopy within the prior 36 months. The final analysis of the data was performed in September 2022. Exposures: Prior receipt of colonoscopy. Main Outcomes and Measures: Cox proportional hazards regression (with censoring, last follow-up December 31, 2018) analyses were conducted to compare PCCRC-3y and DCRC for 5-year ACM and CSM after CRC diagnosis. Results: Of 29 877 patients with CRC (median [IQR] age, 67 [60-75] years; 29 353 [98%] male; 5284 [18%] Black, 23 971 [80%] White, and 622 [2%] other), 1785 (6%) were classified as having PCCRC-3y and 21 811 (73%) as having DCRC. The 5-year ACM rates were 46% vs 42% for patients with PCCRC-3y vs patients with DCRC. The 5-year CSM rates were 26% vs 25% for patients with PCCRC-3y vs patients with DCRC. In multivariable Cox proportional hazards regression analysis, there was no significant difference in ACM and CSM between patients with PCCRC-3y (adjusted hazard ratio [aHR], 1.04; 95% CI, 0.98-1.11; P = .18) and patients with DCRC (aHR, 1.04; 95% CI, 0.95-1.13; P = .42). However, compared with patients with DCRC, patients with no prior colonoscopy had significantly higher ACM (aHR, 1.76; 95% CI, 1.70-1.82; P < .001) and CSM (aHR, 2.22; 95% CI, 2.12-2.32; P < .001). Compared with patients with DCRC, patients with PCCRC-3y had significantly lower odds of having undergone colonoscopy performed by a gastroenterologist (odds ratio, 0.48; 95% CI, 0.43-0.53; P < .001). Conclusions and Relevance: This study found that PCCRC-3y constituted 6% of CRCs in the VA system, which is similar to other settings. Compared with patients with CRC detected by colonoscopy, those with PCCRC-3y have comparable ACM and CSM.

Risk Factors for Early-onset Sporadic Colorectal Cancer in Male Veterans.

Identifying risk factors for early-onset colorectal cancer (EOCRC) could help reverse its rising incidence through risk factor reduction and/or early screening. We sought to identify EOCRC risk factors that could be used for decisions about early screening. Using electronic databases and medical record review, we compared male veterans ages 35 to 49 years diagnosed with sporadic EOCRC (2008-2015) matched 1:4 to clinic and colonoscopy controls without colorectal cancer, excluding those with established inflammatory bowel disease, high-risk polyposis, and nonpolyposis syndromes, prior bowel resection, and high-risk family history. We ascertained sociodemographic and lifestyle factors, family and personal medical history, physical measures, vital signs, medications, and laboratory values 6 to 18 months prior to case diagnosis. In the derivation cohort (75% of the total sample), univariate and multivariate logistic regression models were used to derive a full model and a more parsimonious model. Both models were tested using a validation cohort. Among 600 cases of sporadic EOCRC [mean (SD) age 45.2 (3.5) years; 66% White], 1,200 primary care clinic controls [43.4 (4.2) years; 68% White], and 1,200 colonoscopy controls [44.7 (3.8) years; 63% White], independent risk factors included age, cohabitation and employment status, body mass index (BMI), comorbidity, colorectal cancer, or other visceral cancer in a first- or second-degree relative (FDR or SDR), alcohol use, exercise, hyperlipidemia, use of statins, NSAIDs, and multivitamins. Validation c-statistics were 0.75-0.76 for the full model and 0.74-0.75 for the parsimonious model, respectively. These independent risk factors for EOCRC may identify veterans for whom colorectal cancer screening prior to age 45 or 50 years should be considered. PREVENTION RELEVANCE: Screening 45- to 49-year-olds for colorectal cancer is relatively new with uncertain uptake thus far. Furthermore, half of EOCRC occurs in persons < 45 years old. Using risk factors may help 45- to 49-year-olds accept screening and may identify younger persons for whom earlier screening should be considered. See related Spotlight, p. 479.

Identification of colorectal cancer using structured and free text clinical data.

Colorectal cancer incidence has continually fallen among those 50 years old and over. However, the incidence has increased in those under 50. Even with the recent screening guidelines recommending that screening begins at age 45, nearly half of all early-onset colorectal cancer will be missed. Methods are needed to identify high-risk individuals in this age group for targeted screening. Colorectal cancer studies, as with other clinical studies, have required labor intensive chart review for the identification of those affected and risk factors. Natural language processing and machine learning can be used to automate the process and enable the screening of large numbers of patients. This study developed and compared four machine learning and statistical models: logistic regression, support vector machine, random forest, and deep neural network, in their performance in classifying colorectal cancer patients. Excellent classification performance is achieved with AUCs over 97%.

Clinician Perspectives on Unmet Needs for Mobile Technology Among Hospitalists: Workflow Analysis Based on Semistructured Interviews.

BACKGROUND: The hospitalist workday is cognitively demanding and dominated by activities away from patients' bedsides. Although mobile technologies are offered as solutions, clinicians report lower expectations of mobile technology after actual use. OBJECTIVE: The purpose of this study is to better understand opportunities for integrating mobile technology and apps into hospitalists' workflows. We aim to identify difficult tasks and contextual factors that introduce inefficiencies and characterize hospitalists' perspectives on mobile technology and apps. METHODS: We conducted a workflow analysis based on semistructured interviews. At a Midwestern US medical center, we recruited physicians and nurse practitioners from hospitalist and inpatient teaching teams and internal medicine residents. Interviews focused on tasks perceived as frequent, redundant, and difficult. Additionally, participants were asked to describe opportunities for mobile technology interventions. We analyzed contributing factors, impacted workflows, and mobile app ideas. RESULTS: Over 3 months, we interviewed 12 hospitalists. Participants collectively identified chart reviews, orders, and documentation as the most frequent, redundant, and difficult tasks. Based on those tasks, the intake, discharge, and rounding workflows were characterized as difficult and inefficient. The difficulty was associated with a lack of access to electronic health records at the bedside. Contributing factors for inefficiencies were poor usability and inconsistent availability of health information technology combined with organizational policies. Participants thought mobile apps designed to improve team communications would be most beneficial. Based on our analysis, mobile apps focused on data entry and presentation supporting specific tasks should also be prioritized. CONCLUSIONS: Based on our results, there are prioritized opportunities for mobile technology to decrease difficulty and increase the efficiency of hospitalists' workflows. Mobile technology and task-specific mobile apps with enhanced usability could decrease overreliance on hospitalists' memory and fragmentation of clinical tasks across locations. This study informs the design and implementation processes of future health information technologies to improve continuity in hospital-based medicine.

Gains, losses, and uncertainties from computerizing referrals and consultations.

Consultations entail transitions in care between referrers and consultants, as patients visit different clinicians and care sites. This complex process has been consistently prone to communication breakdowns. Despite expectations and benefits of electronic health records (EHRs), incomplete, vague, or inappropriate referrals continue to hinder consultations; referrals can be sent to the wrong specialty service; and consultation findings frequently fail to reach referrers. Due to the inadequate support of interpersonal communication afforded by EHRs, these issues persist. Important aspects of ergonomics and human factors engineering frequently appear overlooked during the design and implementation of EHRs. Usability issues have contributed to delays in medical diagnosis, treatment, and follow-up. Some of these delays contribute to patient harms. Our multidisciplinary team of clinicians and ergonomics professionals reflects on referral and consultation. We describe how computerization in healthcare should benefit from approaches informed and developed through applied ergonomics and human factors.