Scientific Reports · 2020 · 16 citations · 28 references
MedicineBiostatisticsOncologyCancer ScreeningClinical EpidemiologyEpidemiologyCancer Burden AssessmentColorectal CancerDiabetes MellitusPredictive Health ModelingRisk PredictionBootstrap ResamplingCancer Risk Prediction ModelsClinical Prediction RulesClinical Risk Prediction ToolsLogistic RegressionLogistic Regression AnalysisMultivariable Regression AnalysisPredictive Nomograms
Colorectal cancer remains a major health burden worldwide and is closely related to type 2 diabetes. This study aimed to develop and validate a colorectal cancer risk prediction model to identify high-risk individuals with type 2 diabetes. Records of 930 patients with type 2 diabetes were reviewed and data were collected from 1 November 2013 to 31 December 2019. Clinical and demographic parameters were analyzed using univariable and multivariable logistic regression analysis. The nomogram to assess the risk of colorectal cancer was constructed and validated by bootstrap resampling. Predictors in the prediction nomogram included age, sex, other blood-glucose-lowering drugs and thiazolidinediones. The nomogram demonstrated moderate discrimination in estimating the risk of colorectal cancer, with Hosmer-Lemeshow test P = 0.837, an unadjusted C-index of 0.713 (95% CI 0.670-0.757) and a bootstrap-corrected C index of 0.708. In addition, the decision curve analysis demonstrated that the nomogram would be clinically useful. We have developed a nomogram that can predict the risk of colorectal cancer in patients with type 2 diabetes. The nomogram showed favorable calibration and discrimination values, which may help clinicians in making recommendations about colorectal cancer screening for patients with type 2 diabetes.
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