Video Experimental Relacionado
Updated: Feb 20, 2026
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Desarrollo y Validación Externa de un Nomograma para Predecir Hemorragia Gastrointestinal Superior en Pacientes
Jingyi Yang1, Qifeng Liu2, Songnan Wang3
1Department of Cardiovascular Medicine, Graduate Training Base, Jinzhou Central Hospital, Jinzhou Medical University, Jinzhou, People's Republic of China.
Background:
Dual antiplatelet therapy (DAPT) after percutaneous coronary intervention (PCI) increases the risk of upper gastrointestinal bleeding (UGIB) in patients with the acute coronary syndrome (ACS). As UGIB leads to a poor prognosis, it is essential to predict its occurrence early and effectively.
Objective:
The study aimed to develop and validate a nomogram for predicting UGIB in patients with ACS undergoing DAPT after PCI.
Methods:
This study was conducted on 1820 patients with ACS receiving DAPT after PCI in Jinzhou Central Hospital from January 2019 to September 2022. A logistic regression analysis was conducted to identify the risk factors of UGIB, which were utilized to develop a model for predicting the probability of UGIB in patients receiving DAPT. A validation cohort was used for verification. The discrimination, calibration, and clinical practicability of the nomogram were verified using receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA), respectively.
Results:
Age, history of gastrointestinal ulcer/bleeding, heart failure, drinking status, and creatinine were independent risk factors for UGIB and included in our nomogram. The nomogram demonstrated good discriminative ability, with Area under the curve (AUC) values of 0.829, 0.848, and 0.838, respectively. The calibration curve and H-L test indicate that the model has good consistency (P = 0.948, P = 0.777, and P = 0.913, respectively). The nomograms can be clinically beneficial when the threshold probability is >0.02 in both the training and validation cohorts.
Conclusion:
Our prediction model can guides clinical physicians in risk stratification of undergoing DAPT patients after PCI by calculating the probability of UGIB. Our study may help clinicians in the early identification of patients at a high risk of UGIB and in providing a personalized treatment and management strategies to reduce the associated adverse outcomes.
