An empirically based tool for analyzing mortality associated with congenital heart surgery
An empirically based tool for analyzing mortality associated with congenital heart surgery
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DOI：
10.1016/j.jtcvs.2009.03.071
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发表时间：
2009-11-01
影响因子：
6
通讯作者：
Edwards, Fred H.
中科院分区：
文献类型：
--
作者：
O'Brien, Sean M.;Clarke, David R.;Edwards, Fred H.
目的：分析先天性心脏病手术结果需要一种可靠的方法来估计不良结局的风险。目前使用的两个主要系统是基于对风险或复杂性的预测，这些预测主要是主观得出的。我们的目标是创建一个客观的、基于经验的指数，可以用来确定按手术方式统计估计的住院死亡率风险，并将手术分为风险类别。方法：使用2002至2007年间输入欧洲心胸外科协会(EACTS)先天性心脏手术数据库(33,360次手术)和胸外科医生协会(STS)先天性心脏手术数据库(43,934例患者)的77,294例手术的数据，对148种手术的死亡风险进行评估。使用贝叶斯模型计算特定于手术的死亡率估计，该模型调整了小分母。根据估计的死亡率，每个手术都被分配了一个数字评分(STS-EACTS先天性心脏病手术死亡率[2009])，范围从0.1到5.0。手术也按风险增加进行分类，并分为5个类别(STS-EACTS先天性心脏病手术死亡率类别[2009])，在最小化类别内差异和最大化类别间差异方面被选为最佳类别。随后在一个独立的验证样本(n=27,700)中评估了模型的性能，并与现有的两种方法进行了比较：先天性心脏病手术风险调整(RACHS-1)类别和亚里士多德基础复杂性评分。结果：不同手术类型的估计死亡率从0.3%(房间隔缺损补片修补)到29.8%(主干+主动脉弓中断修补)不等。在验证样本中，建议的STS-EACTS评分和STS-EACTS类别对预测死亡率显示出良好的区分性(C指数分别为0.784和0.773)。对于发生40次以上的手术，在验证样本中，手术的STS-EACTS评分与其实际死亡率之间的皮尔逊相关系数为0.80。在定义了RACHS-1和亚里士多德基本复杂性评分的程序子集中，辨别率最高的是STS-EACTS评分(C-INDEX=0.787)，其次是STS-EACTS类别(C-INDEX=0.778)、RACHS-1类别(C-INDEX=0.745)和亚里士多德基本复杂性评分(C-INDEX=0.687)。加入患者协变量后，C指数得到改善：STS-EACTS评分(C-指数=0.816)、STS-EACTS类别(C-指数=0.812)、RACHS-1类别(C-指数=0.802)和亚里士多德基本复杂性评分(C-指数=0.795)。结论：所提出的风险评分和类别对死亡率的预测具有很高的区分性，是对现有基于共识的方法的改进。纳入这些措施的风险模型可用于比较具有不同病例组合的机构的死亡率结果。
Objective: Analysis of congenital heart surgery results requires a reliable method of estimating the risk of adverse outcomes. Two major systems in current use are based on projections of risk or complexity that were predominantly subjectively derived. Our goal was to create an objective, empirically based index that can be used to identify the statistically estimated risk of in-hospital mortality by procedure and to group procedures into risk categories.Methods: Mortality risk was estimated for 148 types of operative procedures using data from 77,294 operations entered into the European Association for Cardiothoracic Surgery (EACTS) Congenital Heart Surgery Database (33,360 operations) and the Society of Thoracic Surgeons (STS) Congenital Heart Surgery Database (43,934 patients) between 2002 and 2007. Procedure-specific mortality rate estimates were calculated using a Bayesian model that adjusted for small denominators. Each procedure was assigned a numeric score (the STS-EACTS Congenital Heart Surgery Mortality Score [2009]) ranging from 0.1 to 5.0 based on the estimated mortality rate. Procedures were also sorted by increasing risk and grouped into 5 categories (the STS-EACTS Congenital Heart Surgery Mortality Categories [2009]) that were chosen to be optimal with respect to minimizing within-category variation and maximizing between-category variation. Model performance was subsequently assessed in an independent validation sample (n = 27,700) and compared with 2 existing methods: Risk Adjustment for Congenital Heart Surgery (RACHS-1) categories and Aristotle Basis Complexity scores.Results: Estimated mortality rates ranged across procedure types from 0.3% (atrial septal defect repair with patch) to 29.8% (truncus plus interrupted aortic arch repair). The proposed STS-EACTS score and STS-EACTS categories demonstrated good discrimination for predicting mortality in the validation sample (C-index = 0.784 and 0.773, respectively). For procedures with more than 40 occurrences, the Pearson correlation coefficient between a procedure's STS-EACTS score and its actual mortality rate in the validation sample was 0.80. In the subset of procedures for which RACHS-1 and Aristotle Basic Complexity scores are defined, discrimination was highest for the STS-EACTS score (C-index = 0.787), followed by STS-EACTS categories (C-index = 0.778), RACHS-1 categories (C-index = 0.745), and Aristotle Basic Complexity scores (C-index = 0.687). When patient covariates were added to each model, the C-index improved: STS-EACTS score (C-index = 0.816), STS-EACTS categories (C-index = 0.812), RACHS-1 categories (C-index = 0.802), and Aristotle Basic Complexity scores (C-index = 0.795).Conclusion: The proposed risk scores and categories have a high degree of discrimination for predicting mortality and represent an improvement over existing consensus-based methods. Risk models incorporating these measures may be used to compare mortality outcomes across institutions with differing case mixes.