Objective To screen the independent influencing factors for gastrointestinal bleeding (GIB) in hospitalized patients with coronary heart disease (CHD) and to construct and validate a nomogram prediction model.

Methods A total of 440 CHD patients who developed GIB during hospitalization were selected as GIB group, and another 320 CHD patients hospitalized in the department of cardiovascular medicine were randomly selected as non-GIB group. The clinical data of the two groups were analyzed and compared. Multivariate logistic regression analysis was used to screen the indepen-dentinfluencing factors for GIB. Based on these factors, a nomogram prediction model for the risk of GIB in hospitalized CHD patients was constructed. The entire dataset was randomly divided into training set (n=532) and validation set (n= 228) in a 7∶ 3 ratio. The performance of the nomogram model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results Multivariate logistic regression analysis showed that body mass index (BMI), history of digestive system diseases, CHD classification, albumin, white blood cell count, monocyte-to-lymphocyte ratio (MLR), and low-density lipoprotein were all independent influencing factors for GIB in CHD patients (P < 0.05). ROC curve analysis indicated that the nomogram model (excluding low-density lipoprotein) constructed based on independent influencing factors exhibited good discrimination in both the training set (area under the curve: 0.839, 95%CI, 0.805 to 0.873) and the validation set (area under the curve: 0.810, 95%CI, 0.751 to 0.868). Calibration curve analysis demonstrated good consistency between the predicted probabilities and the observed incidence of GIB in hospitalized CHD patients in both the training and validation sets. DCA results revealed that the nomogram model had a good clinical net benefit.

Conclusion The nomogram model constructed based on independent influencing factors has good predictive performance for the risk of GIB in hospitalized CHD patients and can provide a basis for clinicians to promptly identify GIB and adjust medication regimens.