Extension of the GRACE score for non-ST-elevation acute coronary syndrome: a development and validation study in ten countries.

IF 24.1 1区 医学 Q1 MEDICAL INFORMATICS
Florian A Wenzl, Klaus F Kofoed, Moa Simonsson, Gareth Ambler, Niels M R van der Sangen, Erik Lampa, Francesco Bruno, Mark A de Belder, Jiri Hlasensky, Matthias Mueller-Hennessen, Maria A Smolle, Peizhi Wang, José P S Henriques, Wouter J Kikkert, Henning Kelbæk, Luboš Bouček, Sergio Raposeiras-Roubín, Emad Abu-Assi, Jaouad Azzahhafi, Matthijs A Velders, Konstantinos Stellos, Thomas Engstrøm, Dean R P P Chan Pin Yin, Clive Weston, David Adlam, Hans Rickli, Evangelos Giannitsis, Dragana Radovanovic, Jiri Parenica, Charalambos A Antoniades, Keith A A Fox, Fabrizio D'Ascenzo, Jurriën M Ten Berg, Lars V Køber, Stefan James, John Deanfield, Thomas F Lüscher
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引用次数: 0

Abstract

Background: The Global Registry of Acute Coronary Events (GRACE) scoring system guides the management of patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) according to current guidelines. However, broad validation of the sex-specific GRACE 3.0 in-hospital mortality model, and corresponding models for predicting long-term mortality and the personalised effect of early invasive management, are still needed.

Methods: We used data of 609 063 patients with NSTE-ACS from ten countries between Jan 1, 2005, and June 24, 2024. A machine learning model for 1-year mortality was developed in 400 054 patients from England, Wales, and Northern Ireland. Both the in-hospital mortality model and the new 1-year mortality model were externally validated in patients from Sweden, Switzerland, Germany, Denmark, Spain, the Netherlands, and Czechia. A separate machine learning model to predict the individualised effect of early versus delayed invasive coronary angiography and revascularisation on a composite primary outcome of all-cause death, non-fatal recurrent myocardial infarction, hospital admission for refractory myocardial ischaemia, or hospital admission for heart failure at a median follow-up of 4·3 years was developed and externally validated in participants from geographically different sets of hospitals in the Danish VERDICT trial.

Findings: The in-hospital mortality model (area under the receiver operating characteristic curve [AUC] 0·90, 95% CI 0·89-0·91) and the 1-year mortality model (time-dependent AUC 0·84, 95% CI 0·82-0·86) showed excellent discriminative abilities on external validation across all countries. Both models were well calibrated and decision curve analyses suggested favourable clinical utility. Compared with score version 2.0, both models provided improved discrimination and risk reclassification. The individualised treatment effect model effectively identified patients who would benefit from early invasive management on external validation. Patients with high predicted benefit had reduced risk of the composite outcome when randomly assigned to early invasive management (hazard ratio 0·60, 95% CI 0·41-0·88), whereas patients with no-to-moderate predicted benefit did not (1·06, 0·80-1·40; pinteraction=0·014). The individualised treatment effect model suggested that the group of patients with NSTE-ACS who benefit from early intervention might be incompletely captured by current treatment strategies.

Interpretation: The updated GRACE 3.0 scoring system provides a validated, practical tool to support personalised risk assessment in patients with NSTE-ACS. Prediction of an individual's long-term cardiovascular benefit from early invasive management could refine future trial design.

Funding: Swiss Heart Foundation, University of Zurich Foundation, Kurt and Senta Herrmann Foundation, Theodor and Ida Herzog-Egli Foundation, and Foundation for Cardiovascular Research-Zurich Heart House.

非st段抬高急性冠状动脉综合征GRACE评分的扩展:10个国家的发展和验证研究
背景:全球急性冠状动脉事件登记(GRACE)评分系统根据现行指南指导非st段抬高急性冠状动脉综合征(NSTE-ACS)患者的管理。然而,性别特异性GRACE 3.0住院死亡率模型,以及预测长期死亡率和早期有创治疗的个性化效果的相应模型,仍然需要广泛的验证。方法:我们使用了2005年1月1日至2024年6月24日来自10个国家的609063例NSTE-ACS患者的数据。对来自英格兰、威尔士和北爱尔兰的40054名患者开发了1年死亡率的机器学习模型。住院死亡率模型和新的1年死亡率模型在瑞典、瑞士、德国、丹麦、西班牙、荷兰和捷克的患者中进行了外部验证。一个单独的机器学习模型,用于预测早期与延迟侵入性冠状动脉造影和血运重建对全因死亡、非致死性复发性心肌梗死、难治性心肌缺血住院、在丹麦的VERDICT试验中,研究人员在来自不同地区医院的参与者中开发并外部验证了中位随访时间为4.3年的心力衰竭住院治疗方法。结果:住院死亡率模型(受试者工作特征曲线下面积[AUC] 0.90, 95% CI 0.89 - 0.91)和1年死亡率模型(时间相关AUC 0.84, 95% CI 0.82 - 0.86)在所有国家的外部验证中都显示出出色的判别能力。两种模型都经过了很好的校准,决策曲线分析显示了良好的临床应用。与评分2.0版本相比,两种模型都提供了更好的识别和风险再分类。个体化治疗效果模型在外部验证中有效识别了早期有创治疗的受益患者。当随机分配到早期有创治疗时,预测获益高的患者的综合结局风险降低(风险比0.60,95% CI 0.41 - 0.88),而预测获益无至中度的患者则没有(1.06,0.80 - 1.40;p相互作用= 0.014)。个体化治疗效果模型表明,受益于早期干预的NSTE-ACS患者群体可能未被当前的治疗策略完全捕获。解释:更新后的GRACE 3.0评分系统为NSTE-ACS患者的个性化风险评估提供了一个经过验证的实用工具。预测早期侵入性治疗对个体心血管的长期益处可以完善未来的试验设计。资助:瑞士心脏基金会、苏黎世大学基金会、Kurt and Senta Herrmann基金会、Theodor and Ida Herzog-Egli基金会、心血管研究基金会-苏黎世心脏之家。
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来源期刊
CiteScore
41.20
自引率
1.60%
发文量
232
审稿时长
13 weeks
期刊介绍: The Lancet Digital Health publishes important, innovative, and practice-changing research on any topic connected with digital technology in clinical medicine, public health, and global health. The journal’s open access content crosses subject boundaries, building bridges between health professionals and researchers.By bringing together the most important advances in this multidisciplinary field,The Lancet Digital Health is the most prominent publishing venue in digital health. We publish a range of content types including Articles,Review, Comment, and Correspondence, contributing to promoting digital technologies in health practice worldwide.
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