Key result
GRACE and AR-G RS models strongly predict in-hospital mortality, reaching ~0.91 discrimination when updated.
Why the study?
External validation is essential before implementing risk score models in clinical practice, motivating this evaluation of the GRACE and ACTION Registry-GWTG risk scores in a Thai registry.
Do the GRACE and ACTION risk models accurately predict in-hospital mortality in patients with ACS undergoing PCI?
Observational (n=11,455)
Yes
Do the GRACE and ACTION risk models accurately predict in-hospital mortality in patients with ACS undergoing PCI?
The GRACE and ACTION risk models, particularly when updated, provide accurate prediction of in-hospital mortality for ACS patients undergoing PCI in Thailand.
Answers are grounded in this paper and cite their sources.
Supports GRACE/AR-G use for ACS-PCI mortality prediction; leaves open external validation and prospective impact.
BACKGROUND: External validation is crucial before implementing a risk score model in clinical practice. This study examined the performance of Global Registry of Acute Coronary Events (GRACE) and Acute Coronary Treatment and Intervention Outcomes Network (ACTION) Registry-Get With The Guidelines (GWTG) (ACTION Registry-GWTG) Risk Score (AR-G RS) using the Thai Percutaneous Coronary Intervention Registry (TPCIR). METHODS: Included in this study were 11 455 patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI) between November 2015 and May 2018. GRACE and AR-G RS models were externally validated, revised and updated using discrimination (C-statistic score) and calibration (Hosmer-Lemeshow (HL) indexes). Clinical predictors were selected stepwise from the multivariate analysis to evaluate the performance of each risk score in the revised and updated model. RESULTS: In-hospital mortality was 4.4%. GRACE and AR-G RS demonstrated good discrimination for in-hospital mortality (C-statistics 0.8957 and 0.8823, respectively) with optimal calibration (HL, p=0.036 and 0.006, respectively) and penalty rates of 0.005 and 0.006, respectively. The updated model significantly improved the discrimination performance compared with the original GRACE and AR-G RS models, with a C-statistic of 0.9118 and a penalty of 0.006. CONCLUSION: GRACE and AR-G RS maintained a good performance in TPCIR. Based on routine PCI practice, we demonstrated that the updated model could improve the accuracy of GRACE and AR-G RS in predicting in-hospital mortality among patients with ACS who underwent PCI.
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Kiatchoosakun et al. (2025) conducted an observational in Acute coronary syndrome (n=11,455). GRACE and AR-G RS models was evaluated on In-hospital mortality (C-statistic 0.8957 (GRACE) and 0.8823 (AR-G RS)). The GRACE and AR-G RS models demonstrated good discrimination for in-hospital mortality (C-statistics 0.8957 and 0.8823), which was further improved to 0.9118 with an updated model.
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