Patient-Initiated Colonoscopy Scheduling Effectively Increases Colorectal Cancer Screening Adherence

Risk factors and prediction algorithm for advanced neoplasia on screening colonoscopy for average-risk individuals

BACKGROUND

Screening with colonoscopy for all average-risk population is probably not cost-effective due to the limited sources and over-generalization of the risk, and risk stratification can be used to optimize colorectal cancer screening.

OBJECTIVES

We aimed to assess risk factors for advanced neoplasia (AN) and a classification tree algorithm to predict the risk.

DESIGN

This is a retrospective cross-sectional study.

METHODS

This study was composed of consecutive asymptomatic average-risk individuals undergoing first screening colonoscopy between 2008 and 2019. Detailed characteristics including background diseases, habits, and medications were collected. We used multivariable logistic regression to investigate the associations between clinical variables and the presence of AN and built a classification algorithm to predict AN.

RESULTS

A total of 3856 patients were included (73.2% male, median age 55). Adenoma and AN detection rate were 15.8% and 3.4%, respectively. On multivariable analysis, predictors of AN [odds ratio (OR), 95% confidence interval (CI)] were age (1.04, 1.01-1.06, p = 0.003), male sex (2.69, 1.56-4.64, p < 0.001), and smoking (1.97, 1.38-2.81, p  < 0.001). A classification tree algorithm showed that smoking was the most important risk factor for prediction of AN (4.9% versus 2.4%, p < 0.001), followed by age with a cutoff value of 60 in the smokers (8.4% versus 3.8%, p = 0.001) and 50 in the non-smokers (2.9% versus 0.9%, p = 0.004).

CONCLUSION

Smoking habits, old age, and male gender are highly associated with an increased risk for AN and should be incorporated in the individualized risk-assessment to adapt a screening program.

Ultrasound Image Features under Decomposition Algorithm to Analyze the Nursing Intervention on Patients with Colon Polyps Undergoing Endoscopic Resection

Based on the ultrasonic imaging and endoscopic resection of the intelligent segmentation algorithm, this study is aimed at exploring whether nursing intervention can promote the good recovery of patients with colon polyps, hoping to find a new method for clinical treatment of the colon polyps. Patients with colon polyps were divided into an experimental group (fine nursing) and a control group (general nursing). The colonoscopy polyp ultrasound image was preprocessing to select the seed points and background points. The random walk decomposition algorithm was applied to calculate the probability of each marked point, and then, the marked image was outputted. The accuracy of the intelligent segmentation algorithm was 81%. The incidence of complications in the experimental group was 4.83%, which was lower than 16.66% in the control group, and the difference was statistically obvious (P < 0.05). Perioperative refined nursing intervention for colon polyp patients undergoing endoscopic electrosurgical resection can decrease postoperative adverse reactions; reduce postoperative mucosal perforation, blood in the stool, abdominal pain, and small bleeding; lower the incidence of postoperative complications; and allow patients to recover quickly, enhancing the life comfort of patient.

Proven Strategies for Increasing Adherence to Colorectal Cancer Screening

Although colorectal cancer (CRC) can be prevented or detected early through screening and surveillance, barriers that lower adherence to screening significantly limit its effectiveness. Therefore, implementation of interventions that address and overcome adherence barriers is critical to efforts to decrease morbidity and mortality from CRC. This article reviews the current available evidence about interventions to increase adherence to CRC screening.