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Published on: March 27, 2018
Predicting Likelihood for Coronary Artery Bypass Grafting After Non-ST-Elevation Myocardial Infarction: Finding the
Ali Shafiq1, Jae-Sik Jang2, Faraz Kureshi1
1Cardiovascular Outcomes Research, Saint Luke's Mid America Heart Institute, Kansas City, Missouri; Division of Cardiology, University of Missouri, Kansas City, Missouri.
Insights
The Global Risk of Acute Coronary Events (GRACE) model best predicts the need for coronary artery bypass grafting (CABG) in non-ST-elevation myocardial infarction (NSTEMI) patients. A GRACE score below 9 identifies low-risk patients, aiding treatment decisions.
Area of Science:
- Cardiology
- Clinical Medicine
- Health Services Research
Background:
- Dual antiplatelet therapy is withheld in up to 50% of non-ST-elevation myocardial infarction (NSTEMI) patients due to bleeding risks associated with potential coronary artery bypass grafting (CABG).
- Existing models predicting CABG likelihood post-NSTEMI lack independent validation, necessitating further research.
Purpose of the Study:
- To validate existing models for predicting coronary artery bypass grafting (CABG) in non-ST-elevation myocardial infarction (NSTEMI) patients.
- To improve the predictive performance of the best-performing CABG risk model.
Main Methods:
- Utilized the 24-center Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients' Health Status (TRIUMPH) registry (2005-2008) comprising 2,473 NSTEMI patients.
- Assessed prior CABG prediction models (Modified Thrombolysis in Myocardial Infarction, TIMI 18, Poppe, GRACE) using c-statistics and calibration.
- Introduced variables from the TRIUMPH registry to enhance the best model's predictive accuracy.
Main Results:
- 11.8% of NSTEMI patients underwent in-hospital CABG.
- The Global Risk of Acute Coronary Events (GRACE) model demonstrated the best discrimination (c-statistic 0.62) and calibration among evaluated models.
- Adding TRIUMPH variables did not significantly improve the GRACE model's discrimination.
- A GRACE score < 9 exhibited high sensitivity (96%) for identifying patients at low risk of requiring CABG, representing 21% of the study cohort.
Conclusions:
- The Global Risk of Acute Coronary Events (GRACE) model remains the optimal tool for predicting the need for coronary artery bypass grafting (CABG) in non-ST-elevation myocardial infarction (NSTEMI) patients.
- The GRACE model offers a broad risk spectrum and high sensitivity, particularly for low-risk patients (GRACE score < 9).
- No significant improvements were achieved by augmenting the GRACE model with additional variables from the TRIUMPH registry.
Background:
Up to half of patients with non-ST-elevation myocardial infarction (NSTEMI) do not receive dual antiplatelet therapy before angiography "pretreatment" because of the risk of increased bleeding if coronary artery bypass grafting (CABG) operation is needed. Several models have been published that predict the likelihood of CABG after NSTEMI, but they have not been independently validated. The purpose of this study was to validate these models and improve the best one.
Methods:
We studied patients with NSTEMI who were enrolled in the 24-center Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients' Health Status (TRIUMPH) registry between 2005 and 2008. Previous CABG prediction models were assessed using c-statistics and calibration assessments to determine the best model. Variables from TRIUMPH likely to be associated with CABG were tested to see whether they could improve the best model's performance.
Results:
Among 2,473 patients with NSTEMI, 11.8% underwent in-hospital CABG. C-statistics for the Modified Thrombolysis in Myocardial Infarction, Treat Angina With Aggrastat and Determine the Cost of Therapy With an Invasive or Conservative Strategy-Thrombolysis in Myocardial Infarction 18, Poppe, and Global Risk of Acute Coronary Events (GRACE) models were 0.54, 0.61, 0.61, and 0.62, respectively. The GRACE model showed the best discrimination and calibration. From the TRIUMPH registry, preselected variables were added to the GRACE model but did not significantly improve model discrimination. A GRACE model risk score of less than 9 had high sensitivity (96%), thus making it useful for predicting patients with NSTEMI who were at low risk for requiring CABG, which included approximately 21% of patients with NSTEMI.
Conclusions:
This study could not improve on the GRACE model, which had the best predictive value for identifying a need for CABG after NSTEMI with a broader range of predicted risk levels and high sensitivity, especially in patients with scores lower than 9.
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