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Development and External Validation of a Nomogram for Predicting Upper Gastrointestinal Bleeding in Patients After
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.
Insights
This study developed a nomogram to predict upper gastrointestinal bleeding (UGIB) in acute coronary syndrome (ACS) patients receiving dual antiplatelet therapy (DAPT) after PCI. The model aids clinicians in early risk stratification and personalized management.
Area of Science:
- Cardiology
- Gastroenterology
- Medical Informatics
Background:
- Dual antiplatelet therapy (DAPT) following percutaneous coronary intervention (PCI) increases upper gastrointestinal bleeding (UGIB) risk in acute coronary syndrome (ACS) patients.
- UGIB is associated with poor prognosis, necessitating early and effective prediction methods.
Purpose of the Study:
- To develop and validate a nomogram for predicting UGIB in ACS patients undergoing DAPT post-PCI.
- To identify independent risk factors for UGIB in this patient population.
Main Methods:
- Logistic regression analysis of 1820 ACS patients receiving DAPT after PCI.
- Development of a predictive nomogram incorporating identified risk factors.
- Validation of the nomogram's discrimination, calibration, and clinical utility using ROC curve analysis, Hosmer-Lemeshow test, and decision curve analysis.
Main Results:
- Key independent risk factors for UGIB included age, history of gastrointestinal ulcer/bleeding, heart failure, drinking status, and creatinine.
- The nomogram demonstrated strong discriminative ability with AUC values ranging from 0.829 to 0.848.
- The model showed good calibration and consistency, with P-values from the Hosmer-Lemeshow test indicating reliability.
Conclusions:
- The developed nomogram effectively predicts UGIB risk in ACS patients on DAPT post-PCI.
- This tool can guide clinical physicians in risk stratification and personalized treatment strategies.
- Early identification of high-risk patients can help reduce adverse outcomes associated with UGIB.
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.
