Suppr超能文献

GRACE和ACTION风险模型在预测院内死亡率方面的表现:泰国经皮冠状动脉介入治疗登记处的外部验证、模型修订与更新

Performance of the GRACE and the ACTION risk model in the prediction of in-hospital mortality: external validation, model revision and updating in the Thai Percutaneous Coronary Intervention Registry.

作者信息

Kiatchoosakun Songsak, Chamnarnphol Noppadol, Wongwipaporn Chaiyasith, Pussadhamma Burabha, Roongsangmanoon Worawut, Siriyotha Sukanya, Thakkinstian Ammarin, Sansanayudh Nakarin

机构信息

Khon Kaen University, Nai Mueang, Khon Kaen, Thailand.

Prince of Songkla University-Hat Yai Campus, Hat Yai, Thailand.

出版信息

Open Heart. 2025 May 21;12(1):e003027. doi: 10.1136/openhrt-2024-003027.

Abstract

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.

摘要

背景

在临床实践中实施风险评分模型之前,外部验证至关重要。本研究使用泰国经皮冠状动脉介入注册研究(TPCIR)检验了全球急性冠状动脉事件注册研究(GRACE)和急性冠状动脉治疗与干预结果网络(ACTION)注册研究-遵循指南(GWTG)(ACTION注册研究-GWTG)风险评分(AR-G RS)的性能。

方法

本研究纳入了2015年11月至2018年5月期间接受经皮冠状动脉介入治疗(PCI)的11455例急性冠状动脉综合征(ACS)患者。使用鉴别力(C统计评分)和校准(Hosmer-Lemeshow(HL)指数)对GRACE和AR-G RS模型进行外部验证、修订和更新。从多变量分析中逐步选择临床预测因素,以评估修订和更新模型中每个风险评分的性能。

结果

住院死亡率为4.4%。GRACE和AR-G RS对住院死亡率显示出良好的鉴别力(C统计量分别为0.8957和0.8823),校准最佳(HL,p分别为0.036和0.006),惩罚率分别为0.005和0.006。与原始GRACE和AR-G RS模型相比,更新后的模型显著提高了鉴别性能,C统计量为0.9118,惩罚率为0.006。

结论

GRACE和AR-G RS在TPCIR中保持了良好的性能。基于常规PCI实践,我们证明更新后的模型可以提高GRACE和AR-G RS在预测接受PCI的ACS患者住院死亡率方面的准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a30d/12097003/b5bfe945543c/openhrt-12-1-g001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验