通过纳入脂蛋白(a),增强 GRACE 风险评分对接受 PCI 治疗的急性心肌梗死患者主要不良心脏事件的预测性能

IF 1.9 Q3 PERIPHERAL VASCULAR DISEASE
Xuelin Cheng , Ming Liu , Qizhe Wang , Yaxin Xu , Ru Liu , Xiaopan Li , Hong Jiang , Sunfang Jiang
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引用次数: 0

摘要

背景随着科学研究的进步,检测指标和方法也在不断发展。我们目前的研究旨在确定一些新的围手术期指标,这些指标可以提高急性心肌梗死患者院内主要不良心血管事件(MACEs)全球登记(GRACE)评分的预测准确性。终点为院内MACE。通过逐步回归分析和多变量逻辑回归,为通过提名图建立的联合模型选择指标。选择1000次重复的Bootstrap作为联合模型的内部验证。用接收者操作曲线下面积(AUC)和校准图来评价区分度和校准度。决策曲线分析(DCA)用于评估提名图的临床充分性。结果脂蛋白(a)与血清尿酸、空腹血糖和血红蛋白结合可提高 GRACE 风险评分。联合模型的 AUC 为 0.86,这表明它比单独的 GRACE 风险评分(AUC, 0.81; P < 0.05)具有更好的判别能力。联合模型的校准图显示,模型预测与实际观测结果之间具有良好的一致性,优于 GRACE 风险评分。结论脂蛋白(a)有望增强 GRACE 风险评分的预测能力,但将其与其他常用指标相结合可能更有益处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced predictive performance of the GRACE risk score by incorporating lipoprotein(a) for major adverse cardiac events in acute myocardial infarction patients undergoing PCI

Background

As scientific research advances, the landscape of detection indicators and methodologies evolves continuously. Our current study aimed to identify some novel perioperative indicators that can enhance the predictive accuracy of the Global Registry of Acute Coronary Events (GRACE) score for the in-hospital major adverse cardiovascular events (MACEs) in patients with acute myocardial infarction.

Methods

A total of 647 adult patients with AMI admitted to the emergency department were consecutively enrolled in the retrospective research starting from June 2016 to September 2019. The endpoint was in-hospital MACE. Stepwise regression analysis and multivariate logistic regression were performed to select the indicators for the union model established by nomogram. Bootstrap with 1000 replicates was chosen as the internal validation of the union model. The area under the receiver operating curve (AUC) and calibration plot were used to evaluate the discrimination and calibration. Decision curve analysis (DCA) was performed to evaluate the clinical sufficiency of the nomogram. Akaike's information criterion (AIC) and Bayesian Information Criterion (BIC) were used to evaluate the goodness of fit.

Results

Lipoprotein(a) combined with serum uric acid, fasting blood glucose, and hemoglobin could improve the GRACE risk score. The AUC of the union model was 0.86, which indicated a better discriminative ability than the GRACE risk score alone (AUC, 0.81; P < 0.05). The calibration plots of the union model showed favorable consistency between the prediction of the model and actual observations, which was better than the GRACE risk score. DCA plots suggested that the union model had better clinical applicability than the GRACE risk score.

Conclusion

Lipoprotein(a) has shown promise in augmenting the predictive capability of the GRACE risk score, however, it may be beneficial to integrate it with other commonly used indicators.

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