Cheng Xuelin, Liu Ming, Wang Qizhe, Xu Yaxin, Liu Ru, Li Xiaopan, Jiang Hong, Jiang Sunfang
Department of Health Management Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Department of General Practice, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
Int J Cardiol Cardiovasc Risk Prev. 2024 Jul 26;22:200315. doi: 10.1016/j.ijcrp.2024.200315. eCollection 2024 Sep.
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.
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.
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; < 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.
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.
随着科学研究的进展，检测指标和方法不断演变。我们当前的研究旨在确定一些新的围手术期指标，以提高全球急性冠状动脉事件注册研究（GRACE）评分对急性心肌梗死患者院内主要不良心血管事件（MACE）的预测准确性。
从2016年6月至2019年9月，共有647例入住急诊科的成年急性心肌梗死患者连续纳入回顾性研究。终点为院内MACE。进行逐步回归分析和多因素逻辑回归，以选择由列线图建立的联合模型的指标。选择1000次重复抽样的自助法作为联合模型的内部验证。采用受试者操作特征曲线下面积（AUC）和校准图来评估辨别力和校准情况。进行决策曲线分析（DCA）以评估列线图的临床充分性。使用赤池信息准则（AIC）和贝叶斯信息准则（BIC）来评估拟合优度。
脂蛋白（a）联合血清尿酸、空腹血糖和血红蛋白可改善GRACE风险评分。联合模型的AUC为0.86，表明其辨别能力优于单独的GRACE风险评分（AUC为0.81；<0.05）。联合模型的校准图显示模型预测与实际观察之间具有良好的一致性，优于GRACE风险评分。DCA图表明联合模型比GRACE风险评分具有更好的临床适用性。
脂蛋白（a）在增强GRACE风险评分的预测能力方面显示出前景，然而，将其与其他常用指标整合可能会更有益。
