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Bleeding in patients undergoing percutaneous coronary intervention: the development of a clinical risk algorithm from
Sameer K Mehta1, Andrew D Frutkin, Jason B Lindsey
1Division of Cardiovascular Research, Mid America Heart Institute, Saint Luke's Hospital, Kansas City, MO 64111, USA.
Insights
Bleeding after percutaneous coronary intervention (PCI) is common. A new risk algorithm identifies key clinical factors to predict bleeding events in PCI patients, aiding treatment decisions.
Area of Science:
- Cardiology
- Interventional Cardiology
- Clinical Risk Prediction
Background:
- Bleeding complications following percutaneous coronary intervention (PCI) are linked to worse patient outcomes, including increased mortality and healthcare costs.
- Identifying patients at high risk for bleeding is crucial for optimizing treatment strategies and improving patient safety after PCI.
Purpose of the Study:
- To identify baseline clinical characteristics associated with bleeding complications after PCI.
- To develop a simplified and clinically useful algorithm for predicting bleeding risk in patients undergoing PCI.
Main Methods:
- Analysis of data from 302,152 PCI procedures across 440 US centers within the National Cardiovascular Data Registry.
- Development of a risk prediction model using 15 clinical elements identified in an 80% training cohort, validated on the remaining 20% of the data.
- Definition of bleeding complications included transfusion, prolonged hospital stay, or a significant hemoglobin drop (>3.0 g/dL).
Main Results:
- Bleeding complications occurred in 2.4% of patients undergoing PCI.
- Key predictors of bleeding included age, gender, heart failure history, reduced glomerular filtration rate, peripheral vascular disease, prior PCI status, heart failure severity (NYHA/CCS Class IV), and acute coronary syndromes (STEMI, NSTEMI), and cardiogenic shock.
- A validated risk algorithm stratified patients into categories with increasing bleeding rates (0.7% for scores ≤7, 1.8% for 8-17, and 5.1% for ≥18).
Conclusions:
- Baseline clinical factors significantly associated with post-PCI bleeding have been identified.
- A simplified, clinically actionable risk algorithm has been developed to estimate bleeding risk in patients undergoing PCI.
- This risk prediction tool has the potential to guide therapeutic decision-making and improve outcomes for patients receiving PCI.
Background:
Bleeding in patients undergoing percutaneous coronary intervention (PCI) is associated with increased morbidity, mortality, length of hospitalization, and cost. We identified baseline clinical characteristics associated with bleeding complications after PCI and developed a simplified, clinically useful algorithm to predict patient risk.
Methods And Results:
Data were analyzed from 302 152 PCI procedures performed at 440 US centers participating in the National Cardiovascular Data Registry. As defined by the National Cardiovascular Data Registry, bleeding required transfusion, prolonged hospital stay, and/or a drop in hemoglobin >3.0 g/dL from any location, including percutaneous entry site, retroperitoneal, gastrointestinal, genitourinary, and other/unknown location. Bleeding complications occurred in 2.4% of patients. From the best-fitting model consisting of 15 clinical elements associated with post-PCI bleeding in a random 80% training cohort, we developed a parsimonious risk algorithm. Predictors of bleeding included age, gender, previous heart failure, glomerular filtration rate, peripheral vascular disease, no previous PCI, New York Heart Association/Canadian Cardiovascular Society Functional Classification class IV heart failure, ST-elevation myocardial infarction, non-ST-elevation myocardial infarction, and cardiogenic shock. The parsimonious model was validated in the remaining 20% of the population (c-statistic, 0.72) and in clinically relevant subgroups of patients. This simplified model was used to derive a clinical risk algorithm, with larger numbers corresponding with greater risk. In 3 categories, bleeding rates were greater in patients with higher estimates (
Conclusions:
This report identifies baseline clinical factors associated with bleeding and proposes a clinically useful algorithm to estimate bleeding risk. This model is potentially actionable in altering therapeutic decision making and improving outcomes in patients undergoing PCI.
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