Five-Year Colon Surveillance After Screening Colonoscopy
ABSTRACT Outcomes of colon surveillance after colorectal cancer screening with colonoscopy are uncertain. We conducted a prospective study to measure incidence of advanced neoplasia in patients within 5.5 years of screening colonoscopy.
Three thousand one hundred twenty-one asymptomatic subjects, age 50 to 75 years, had screening colonoscopy between 1994 and 1997 in the Department of Veterans Affairs. One thousand one hundred seventy-one subjects with neoplasia and 501 neoplasia-free controls were assigned to colonoscopic surveillance over 5 years. Cohorts were defined by baseline findings. Relative risks for advanced neoplasia within 5.5 years were calculated. Advanced neoplasia was defined as tubular adenoma greater than > or =10 mm, adenoma with villous histology, adenoma with high-grade dysplasia, or invasive cancer.
Eight hundred ninety-five (76.4%) patients with neoplasia and 298 subjects (59.5%) without neoplasia at baseline had colonoscopy within 5.5 years; 2.4% of patients with no neoplasia had interval advanced neoplasia. The relative risk in patients with baseline neoplasia was 1.92 (95% CI: 0.83-4.42) with 1 or 2 tubular adenomas <10 mm, 5.01 (95% CI: 2.10-11.96) with 3 or more tubular adenomas <10 mm, 6.40 (95% CI: 2.74-14.94) with tubular adenoma > or =10 mm, 6.05 (95% CI: 2.48-14.71) for villous adenoma, and 6.87 (95% CI: 2.61-18.07) for adenoma with high-grade dysplasia.
There is a strong association between results of baseline screening colonoscopy and rate of serious incident lesions during 5.5 years of surveillance. Patients with 1 or 2 tubular adenomas less than 10 mm represent a low-risk group compared with other patients with colon neoplasia.
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ABSTRACT: Background Fecal immunochemical tests (FITs) are recommended to screen average-risk adults for colorectal cancer (CRC). Little research has examined whether a two-sample FIT affects participant uptake, compared with a one-sample FIT. Examining participant uptake is important, as evidence suggests that a two-sample FIT may increase the sensitivity to detect CRC.Objective This study had two objectives: (i) to evaluate FIT completion in a population that received either a one-sample FIT kit (1-FIT) or a two-sample FIT kit (2-FIT) and (ii) to understand whether uptake varies by age, sex, or receipt of prior CRC screening.Methods We conducted a randomized controlled trial in which 3081 participants who were aged between 50 and 75 years and were at an average risk for CRC, and who had requested FITs, randomly received 1-FIT (n=1540) or 2-FIT (n=1541) kits. FIT completion was defined as the completion and return of a one-sample test by the patients in the 1-FIT group or of both sample tests by those in the 2-FIT group. Cox proportional hazard regression models were used to determine the independent effect of group type (2-FIT vs. 1-FIT) on the completion of the FIT, adjusting for age, sex, and receipt of prior CRC screening.ResultsThe 2-FIT group had lower test completion rates (hazard ratio=0.87; 95% confidence interval=0.78-0.97; P=0.01) after adjusting for age, sex, and receipt of prior CRC screening. Participant uptake did not vary by age, sex, or receipt of prior CRC screening.Conclusion This unique, rigorous randomized controlled trial found that the 2-FIT regimen decreases completion of FIT. Further research is needed to understand whether decreases in participant uptake are offset by increased gains in test sensitivity.European journal of cancer prevention: the official journal of the European Cancer Prevention Organisation (ECP) 09/2014; 23(6). DOI:10.1097/CEJ.0000000000000084 · 2.76 Impact Factor
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ABSTRACT: We suggest a model framework, in which an individual patient's risk for colonic neoplasia varies based on findings from his previous colonoscopies, to predict longitudinal colonoscopy results. The neoplasia natural history model describes progression through four neoplasia development states with patient age. Multiple natural history model parameter sets are assumed to act concurrently on the colon and parameter set prevalence combinations, whose a priori likelihoods are a function of patient sex, provide a basis set for patient-level predictions. The novelty in this approach is that after a colonoscopy, both the parameter set combination likelihoods and their model predictions can adjust in a Bayesian manner based on the results and conditions of the colonoscopy. The adjustment of model predictions operationalizes the clinical knowledge that multiple or advanced neoplasia at baseline colonoscopy is an independent predictor of multiple or advanced neoplasia at follow-up colonoscopy - and vice versa for negative colonoscopies - and the adjustment of parameter set combination likelihoods accounts for the possibility that patients may have different neoplasia development rates. A model that accurately captures serial colonoscopy results could potentially be used to design and evaluate post-colonoscopy treatment strategies based on the risk of individual patients. To support model identification, observational longitudinal colonoscopy results, procedure details, and patient characteristics were collected for 4084 patients. We found that at least two parameter sets specific to each sex with model adjustments was required to capture the longitudinal colonoscopy data and inclusion of multiple possible parameter set combinations, which account for random variations within the population, was necessary to accurately predict the second-time colonoscopy findings for patients with a history of advanced adenomas. Application of this model to predict CRC risks for patients adhering to guideline recommended follow-up colonoscopy intervals found that there are significant differences in risk with patient age, gender, and preparation quality and demonstrates the need for a more rigorous investigation into these recommendations.Computer methods and programs in biomedicine 08/2013; 112(3). DOI:10.1016/j.cmpb.2013.07.007 · 1.09 Impact Factor
- Gastroentérologie Clinique et Biologique 05/2009; 33(4):253-7. · 1.14 Impact Factor