Background: Colorectal cancer (CRC) has become the malignant tumor of the digestive tract with the highest incidence in our country, posing a serious threat to the health of our people. Early colon cancer is mostly due to the malignant transformation of colon polyps, so that early detection and resection have been shown to be effective in reducing the incidence and mortality of CRC. This study tried to investigate the related risk factors of and construct a predictive nomogram for colorectal polyps, providing meaningful guidance basis for risk stratification and screening. Methods: A total of 1,799 patients who underwent colonoscopies in the Health Management Centre of the Affiliated Hospital of Yangzhou University were recruited to this study. Univariate and multivariate logistic analyses were performed to determine the risk factors for colorectal polyps, and a predictive nomogram was constructed based on the multivariable model. We determined the predictive value of the nomogram by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCAs). Results: The logistic regression analysis showed that age (P<0.001), gender (P<0.001), eosinophil count (P=0.005), hemoglobin level (P=0.039), and LDL-C/HDL-C ratio (LHR; P<0.001) were independent predictors of the development of colorectal polyps. The above independent risk factors were incorporated, and an individualized nomogram model was successfully established. The C-index of the nomogram was 0.679 in our model, and with the bootstrap method, the prediction curve fit well with the ideal curve, suggesting that the prediction curve constructed in this study has good predictive ability. Conclusions: Age, gender, eosinophil count, hemoglobin level, and LHR are risk factors for the development of colorectal polyps. Establishing a nomogram prediction model for colorectal polyps is helpful for the early clinical screening of high-risk patients with colorectal polyps, improving the detection rate of polyps and reducing the incidence of colorectal cancer (CRC). 2022 Journal of Gastrointestinal Oncology. All rights reserved.
Background: Colorectal cancer (CRC) has become the malignant tumor of the digestive tract with the highest incidence in our country, posing a serious threat to the health of our people. Early colon cancer is mostly due to the malignant transformation of colon polyps, so that early detection and resection have been shown to be effective in reducing the incidence and mortality of CRC. This study tried to investigate the related risk factors of and construct a predictive nomogram for colorectal polyps, providing meaningful guidance basis for risk stratification and screening. Methods: A total of 1,799 patients who underwent colonoscopies in the Health Management Centre of the Affiliated Hospital of Yangzhou University were recruited to this study. Univariate and multivariate logistic analyses were performed to determine the risk factors for colorectal polyps, and a predictive nomogram was constructed based on the multivariable model. We determined the predictive value of the nomogram by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCAs). Results: The logistic regression analysis showed that age (P<0.001), gender (P<0.001), eosinophil count (P=0.005), hemoglobin level (P=0.039), and LDL-C/HDL-C ratio (LHR; P<0.001) were independent predictors of the development of colorectal polyps. The above independent risk factors were incorporated, and an individualized nomogram model was successfully established. The C-index of the nomogram was 0.679 in our model, and with the bootstrap method, the prediction curve fit well with the ideal curve, suggesting that the prediction curve constructed in this study has good predictive ability. Conclusions: Age, gender, eosinophil count, hemoglobin level, and LHR are risk factors for the development of colorectal polyps. Establishing a nomogram prediction model for colorectal polyps is helpful for the early clinical screening of high-risk patients with colorectal polyps, improving the detection rate of polyps and reducing the incidence of colorectal cancer (CRC). 2022 Journal of Gastrointestinal Oncology. All rights reserved.
Authors: Mari Øines; Lise M Helsingen; Michael Bretthauer; Louise Emilsson Journal: Best Pract Res Clin Gastroenterol Date: 2017-06-28 Impact factor: 3.043
Authors: Bernard Levin; David A Lieberman; Beth McFarland; Kimberly S Andrews; Durado Brooks; John Bond; Chiranjeev Dash; Francis M Giardiello; Seth Glick; David Johnson; C Daniel Johnson; Theodore R Levin; Perry J Pickhardt; Douglas K Rex; Robert A Smith; Alan Thorson; Sidney J Winawer Journal: Gastroenterology Date: 2008-02-08 Impact factor: 22.682