ObjectivesThe purpose of this study was to construct a nomogram to identify patients at high risk of gastric precancerous lesions (GPLs). This identification will facilitate early diagnosis and treatment and ultimately reduce the incidence and mortality of gastric cancer.MethodsIn this single-center retrospective cohort study, 563 participants were divided into a gastric precancerous lesion (GPL) group (n=322) and a non-atrophic gastritis (NAG) group (n=241) based on gastroscopy and pathology results. Laboratory data and demographic data were collected. A derivation cohort (n=395) was used to identify the factors associated with GPLs to develop a predictive model. Then, internal validation was performed (n=168). We used the area under the receiver operating characteristic curve (AUC) to determine the discriminative ability of the predictive model; we constructed a calibration plot to evaluate the accuracy of the predictive model; and we performed decision curve analysis (DCA) to assess the clinical practicability predictive model.ResultsFour –predictors (i.e., age, body mass index, smoking status, and –triglycerides) were included in the predictive model. The AUC values of this predictive model were 0.715 (95% CI: 0.665-0.765) and 0.717 (95% CI: 0.640-0.795) in the derivation and internal validation cohorts, respectively. These values indicated that the predictive model had good discrimination ability. The calibration plots and DCA suggested that the predictive model had good accuracy and clinical net benefit. The Hosmer–Lemeshow test results in the derivation and validation cohorts for this predictive model were 0.774 and 0.468, respectively.ConclusionThe nomogram constructed herein demonstrated good performance in terms of predicting the risk of GPLs. This nomogram can be beneficial for the early detection of patients at high risk of GPLs, thus facilitating early treatment and ultimately reducing the incidence and mortality of gastric cancer.
