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Clinical trial--derived risk model may not generalize to real-world patients with acute coronary syndrome
Andrew T Yan1, Philip Jong, Raymond T Yan
1Terrence Donnelly Heart Centre, Division of Cardiology, St Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.
Insights
The GRACE risk model better predicts in-hospital death in acute coronary syndromes (ACS) patients than the PURSUIT model. Validating risk models in general ACS populations is crucial for clinical practice.
Area of Science:
- Cardiology
- Clinical Risk Stratification
Background:
- Accurate risk stratification is vital for managing acute coronary syndromes (ACS).
- The generalizability of existing risk models to diverse ACS populations is often uncertain.
- This study aimed to validate and compare two risk models in a contemporary, less selected ACS cohort.
Purpose of the Study:
- To validate and compare the performance of the Platelet glycoprotein IIb/IIIa in Unstable angina: Receptor Suppression Using Integrilin Therapy (PURSUIT) and Global Registry of Acute Cardiac Events (GRACE) risk models.
- To assess the applicability of these models in a contemporary, less selected population with ACS.
- To determine which model offers superior risk assessment for in-hospital mortality.
Main Methods:
- Prospective, observational study of 4627 ACS patients from the Canadian ACS Registry.
- Evaluation of in-hospital mortality prediction using PURSUIT and GRACE risk models for non-ST-elevation ACS.
- Assessment of model discrimination via c-statistic and calibration using the Hosmer-Lemeshow goodness-of-fit test.
Main Results:
- In-hospital mortality was 2.4% overall and 1.5% in the non-ST-elevation ACS subgroup.
- Both PURSUIT and GRACE models demonstrated strong prognostic discrimination (c-statistics 0.84 and 0.83, respectively).
- The GRACE model showed good calibration, while the PURSUIT model exhibited poor calibration with risk overestimation.
Conclusions:
- Both PURSUIT and GRACE models effectively discriminate in-hospital mortality in ACS patients.
- The GRACE risk model offers superior calibration and risk assessment in a broader ACS population.
- Emphasizes the need for validating risk models in general populations before clinical implementation.
Background:
Accurate risk stratification can guide clinical decision-making in the management of acute coronary syndromes (ACS). However, the applicability of risk models to the general ACS population remains unclear. The purpose of this study was to validate and compare a modified international clinical trial and a registry-based risk model in a contemporary, less selected ACS population.
Methods:
In the prospective, observational Canadian ACS Registry, 4627 patients with ACS were enrolled from 51 centers. Baseline patient data were recorded on standardized case report forms. We evaluated risk models derived from the Platelet glycoprotein IIb/IIIa in Unstable angina: Receptor Suppression Using Integrilin Therapy (PURSUIT) and the Global Registry of Acute Cardiac Events (GRACE) predicting in-hospital death among patients with non-ST-elevation ACS. Model discrimination was measured by the c-statistic, and calibration was assessed graphically and by the Hosmer-Lemeshow goodness-of-fit test.
Results:
In-hospital mortality rates were 2.4% overall and 1.5% among the patients with non-ST-elevation ACS (n = 2925; 63.2%) in our validation cohort. Both the in-hospital PURSUIT and GRACE risk models showed similar and good prognostic discrimination (c-statistics = 0.84 and 0.83, respectively; P = .69 for difference). The GRACE model also demonstrated good calibration (Hosmer-Lemeshow P = .40). In contrast, calibration in the PURSUIT model was poor (Hosmer-Lemeshow P < .001), with consistent overestimation of risks.
Conclusions:
Both the PURSUIT and GRACE models demonstrated good discrimination for in-hospital mortality rates in the Canadian ACS Registry. However, the GRACE risk model, derived from a less selected population, provided superior calibration in risk assessment across the spectrum of ACS. Our findings underscore the potential importance of risk model validation in the general ACS population rather than a clinical trial population to establish its generalizability before integration into clinical practice.
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