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Meta-analysis and external validation of a risk model for gastrointestinal bleeding after percutaneous coronary
Hualong Ma1, Cong Peng2, Xiaoge Liu1
1Jinan University, Guangzhou, Guangdong, China.
Insights
Gastrointestinal bleeding (GIB) after percutaneous coronary intervention (PCI) is common. A new model, Model I, accurately predicts GIB risk using nine factors, outperforming existing scores for better patient management.
Area of Science:
- Cardiology
- Gastroenterology
- Medical Informatics
Background:
- Percutaneous coronary intervention (PCI) is crucial for coronary artery disease management.
- Postoperative gastrointestinal bleeding (GIB) is a significant complication following PCI.
- Existing risk prediction tools for GIB post-PCI have limited accuracy.
Purpose of the Study:
- To conduct a comprehensive meta-analysis to identify risk factors for GIB after PCI.
- To develop and validate novel predictive models for GIB post-PCI.
- To compare the performance of new models against established risk scores.
Main Methods:
- A meta-analysis of 77 studies involving 7,211,114 patients undergoing PCI.
- Identification of 60 significant risk factors for GIB from 129 initial factors.
- Development and external validation of ten predictive models, including the superior Model I.
Main Results:
- Model I, incorporating nine readily available risk factors, achieved an AUC of 0.842 in external validation.
- Model I demonstrated superior predictive performance compared to the CRUSADE (AUC=0.770) and PRECISE-DAPT (AUC=0.772) scores.
- Statistical analyses confirmed Model I's enhanced clinical utility and reclassification ability.
Conclusions:
- GIB following PCI is multifactorial, necessitating improved risk stratification.
- Model I offers superior accuracy in predicting GIB post-PCI, surpassing current scoring systems.
- The model's reliance on routinely available data facilitates immediate clinical application for personalized risk management.
Background:
Percutaneous coronary intervention (PCI) is a cornerstone in the management of coronary artery disease; however, postoperative gastrointestinal bleeding (GIB) represents a significant complication that adversely impacts patient prognosis. Numerous factors influence GIB, yet no comprehensive meta-analysis has synthesized these to date. Current predictive tools, such as the CRUSADE and PRECISE-DAPT scores, exhibit limited efficacy in forecasting GIB following PCI, underscoring the urgent need for a more precise model to enhance risk management.
Methods:
This study employed a meta-analysis to identify risk factors for GIB post-PCI and subsequently developed predictive models based on these findings. The meta-analysis incorporated 77 studies encompassing a total of 7,211,114 patients with PCI. Ten predictive models were constructed from the analysis and validated in an external cohort of 3425 patients with PCI from two tertiary hospitals.
Results:
A total of 129 influencing factors were included, with a meta-analysis conducted on 71, identifying 60 factors significantly associated with GIB. Model I, the most clinically applicable model comprising nine risk factors (female sex, advanced age, smoking, prior gastrointestinal ulcer, renal insufficiency, non-use of proton pump inhibitors, anticoagulant use, anemia, and glycoprotein IIb/IIIa receptor antagonist administration), demonstrated superior performance in external validation with an AUC of 0.842. This outperformed the CRUSADE score (AUC = 0.770) and PRECISE-DAPT score (AUC = 0.772), with DeLong's test, significant positive Net Reclassification Improvement Index, and Integrated Discrimination Improvement further confirming its enhanced clinical utility.
Conclusions:
GIB following PCI is influenced by a multitude of factors. Model I excels in predicting this complication, surpassing existing scoring systems, and offers substantial clinical value by enabling personalized risk management to improve patient outcomes. All nine predictors are routinely available at the bedside or in the electronic health record, facilitating immediate clinical implementation without additional testing.
