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[ESC2017]中国心梗临床决策新利器——CAMI-NSTEMI评分

——CAMI最新数据:中国人专属的NSTEMI死亡风险预测模型


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当地时间8月26日,一项来自中国的研究亮相ESC2017的Rapid Fire Abstract口头报告专场,中国医学科学院阜外医院杨跃进教授、窦克非教授团队的伏蕊医生介绍了非ST段抬高型心肌梗死(NSTEMI)死亡风险预测模型CAMI-NSTEMI评分系统。


早期风险分层对预后判断和指导诊疗决策有重要意义。但是,目前临床常用的GRACE评分针对的是急性冠脉综合征患者,而不是专门针对NSTEMI患者。因此,我们急需一种针对中国NSTEMI患者的简单而实用的住院死亡率风险预测模型。


研究者从中国心肌梗死注册登记研究(CAMI)数据库中提取数据,从2013年1月至2014年9月共6209人被诊断为NSTEMI,其中有393例因关键数据缺失被排除,本研究共纳入5816名NSTEMI患者。将这些患者分为测试队列(n= 4362)和验证队列(n= 1454)。主要终点是住院死亡率,定义为住院期间的全因死亡率。


在测试队列中,通过多变量logistic回归分析提取独立预后变量,构建风险预测模型,并给每一个独立预测因子赋整数分数。采用ROC分析比较了GRACE评分与NSTEMI评分对住院死亡率的风险预测能力。


该研究建立的CAMI-NSTEMI评分包括12个预测变量:年龄,BMI,收缩压,Killip分级,心跳骤停,ST段压低,新发左束支传导阻滞,血清肌酐水平,白细胞水平,吸烟,心肌梗死病史,PCI史。评分范围0-35分。


表1 CAMI-NSTEMI评分


在测试队列中,该模型的C统计量为0.81(95% CI 0.78-0.84),拟合度非常好(Hosmer-Lemeshow p=0.30)。测试队列中,事件发生率随着NSTEMI评分增加而升高:0–10分,1.16%;11–13分,2.97%;≥14分,13.11%(P<0.001)。在验证队列中,结果相似(P<0.001)。


表2 研究结果


在所有5816名患者中,CAMI-NSTEMI评分的ROC曲线下面积为0.81,显著高于GRACE评分的0.72(P<0.01)。


研究表明,CAMI-NSTEMI评分能准确的预测亚洲NSTEMI患者的住院死亡风险,其预测能力强于GRACE评分。


CAMI-NSTEMI评分的变量易于获得,只需要临床采集最基本的病史、体格检查、血肌酐及血常规等,快速、简便、实用,而且预测能力高。CAMI-NSTEMI评分是基于我国最大规模急性心梗患者注册研究数据库而建立的,样本量大,在我国NSTEMI诊治现状的背景下,它具有非常强的代表性和推广价值。掌握了这一新型武器,可以帮助医生迅速而准确地判断患者死亡风险,为临床诊疗方案的决策提供依据。


热点专题>>>2017年欧洲心脏病学会年会(ESC2017)


【Abstract: 256】


The CAMI-NSTEMI score: A novel score system for predicting the in hospital death of non-ST-segment elevation myocardial infarction patients (Results from China Acute Myocardial Infarction Registry)


Authors:R. Fu1, Y.J. Yang1, K.F. Dou1, J.G. Yang1, H.Y. Xu1, X.J. Gao1, W. Li1, Y. Wang1, J. Liu1, 1Fuwai Hospital, Chinese Academy of Medical Sciences and Peking union     Medical College, Department of cardiology - Beijing - China People's Republic of,


Background: Early risk stratification of patients with myocardial infarction allows for prognostication and triage via initiation of vital treatment pathways. However, there is still no risk prediction models for in-hospital mortality of Asia patients with NSTEMI.


Purpose: To establish a novel score system, the NSTEMI score system to evaluate the risk of in-hospital mortality of NSTEMI of Asia patients.


Methods: Data were extracted from the CAMI registry study, from January 2013 to September 2014, 6209 patients were diagnosed with NSTEMI. 393 patients with critical data missing were excluded. A total of 5816 patients with NSTEMI were included in the present study. These patients was divided into a test cohort of 4362 patients for the construction of the risk model, and a validation group of 1454 patients for validation of the model. The primary endpoint is in-hospital mortality, which was defined as all-cause mortality during hospitalization.The NSTEMI risk score was derived in the test cohort by selection of independent prognostic variables using multivariate logistic regression. The variable with the smallest estimated coefficient was attributed 1 point and was considered as the reference variable. The scores of the other variables were determined by dividing their estimated coefficients by the coefficient of the reference variable. Multicollinearity between variables was assessed using the variance inflation factor. Discrimination and calibration were determined by the C-statistic and the Hosmer-Lemeshow (HL) goodness-of-fit test, respectively. The calibration and discrimination of the model were then assessed in the validation dataset. Receiver operating characteristic (ROC) analyses was performed to compare the capability of risk prediction of in-hospital mortality between the GRACE score and NSTEMI score.


Results: The 12 NSTEMI risk score predictor variables were Age, BMI, systolic blood pressure, Killip classification, heart arrest, ST segment depression of ECG, new onset LBBB, serum creatinine, white blood cell, smoking status, previous MI, and previous PCI. Within the test cohort, the C-statistic for this model was 0.81 (95% confidence interval [CI]: 0.78 to 0.84) and excellent calibration was observed (Hosmer-Lemeshow p=0.30). NSTEMI score ranged from 0 to 35. Event rates increased significantly as the NSTEMI risk score increased in the test cohort: 1.16% for a score of 0–10; 2.97% for 11–13; 13.11% for ≥14) (P<0.001). The pattern of increasing event rates with increasing NSTEMI risk score was confirmed in the validation group (P<0.001). For all 5816 patients, the area under the ROC curve of NSTEMI score was 0.81, which is significantly higher than the GRACE score (0.72) (P<0.01).


Conclusions: The NSTEMI score is able to accurately predict the risk of in-hospital mortality in Asia patients with NSTEMI, and its predictive power is stronger than the GRACE score.

 

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