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Risk Scores for Predicting Advanced Colorectal Neoplasia in the Average-risk Population: A Systematic Review and
Le Peng1,1, Korbinian Weigl1,1,1, Daniel Boakye1,1
1Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany. Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany. German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany. Division of Preventive Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Objectives:
A systematic review and meta-analysis was performed to summarize the available evidence on risk scores for predicting advanced colorectal neoplasia (advanced adenomas and cancer) in average-risk and asymptomatic populations undergoing screening colonoscopy.
Methods:
PubMed, EMBASE, and Web of Science databases were searched up to 28 March 2018. Studies that developed or validated a risk score to predict the risk of advanced colorectal neoplasia were included. Two reviewers independently extracted study characteristics including diagnostic performance indicators and assessed risk of bias and applicability in the included studies. Meta-analyses were conducted to determine the overall discrimination of risk scores evaluated by more than 1 study.
Results:
A total of 22 studies including 17 original risk scores were identified. Risk scores included a median number of 5 risk factors. Factors most commonly included were age, sex, family history in first-degree relatives, body mass index and smoking. The area under the receiver operating characteristic curve of risk scores ranged from 0.62 to 0.77 in the individual studies and from 0.61 to 0.70 in the meta-analyses.
Conclusions:
Although the majority of available risk scores had relatively weak discriminatory power, they may be of some use for risk stratification in CRC screening. Rather than developing more risk scores based on environmental risk factors, future research should focus on exploring possibilities of enhancing predictive power by combining risk factor data with novel laboratory matters, such as polygenetic risk scores.
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