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Sci Rep ; 14(1): 11633, 2024 05 21.
Article in English | MEDLINE | ID: mdl-38773186

ABSTRACT

This retrospective cohort study aimed to identify baseline patient characteristics involving modifiable lifestyle factors that are associated with the development of colorectal adenomas, and establish and validate a nomogram for risk predictions among high-risk populations with negative index colonoscopy. A total of 83,076 participants who underwent an index colonoscopy at the Tianjin Union Medical Center between 2004 and 2019 were collected. According to meticulous inclusion and exclusion criteria, 249 subjects were enrolled and categorized into the primary and validation cohorts. Based on the primary cohort, we utilized the LASSO-Cox regression and the univariate/multivariate Cox proportional hazards (Cox-PH) regression parallelly to select variables, and incorporated selected variables into two nomogram models established using the multivariate Cox-PH regression. Comparison of the Akaike information criterion and the area under the receiver operating characteristic curve of the two models demonstrated that the nomogram model constituted by four covariates retained by the LASSO-Cox regression, including baseline age, body mass index, physical activity and family history of colorectal cancer (CRC) in first-degree relatives, performed better at predicting adenoma-free survival probabilities. Further validation including the concordance index, calibration plots, decision curve analysis and Kaplan-Meier survival curves also revealed good predictive accuracy, discriminating ability, clinical utility and risk stratification capacity of the nomogram model. Our nomogram will assist high-risk individuals with negative index colonoscopy to prevent colorectal adenoma occurrence and CRC morbidity with improved cost-effectiveness.


Subject(s)
Adenoma , Colonoscopy , Colorectal Neoplasms , Life Style , Nomograms , Humans , Colorectal Neoplasms/diagnosis , Male , Female , Middle Aged , Adenoma/diagnosis , Retrospective Studies , Aged , Risk Factors , Adult , Proportional Hazards Models , ROC Curve
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