Major infection after pediatric cardiac surgery: a risk estimation model.
Major infection after pediatric cardiac surgery: a risk estimation model.
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DOI：
10.1016/j.athoracsur.2009.11.048
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发表时间：
2010-03
影响因子：
4.6
通讯作者：
Li, Jennifer S.
中科院分区：
文献类型：
--
作者：
Barker, Gregory M.;O'Brien, Sean M.;Welke, Karl F.;Jacobs, Marshall L.;Jacobs, Jeffrey P.;Benjamin, Daniel K., Jr.;Peterson, Eric D.;Jaggers, James;Li, Jennifer S.
在儿科心脏手术中，感染是发病率和死亡率的主要原因。我们创建了一个模型来预测这一人群中重大感染的风险。利用胸外科医师学会先天性心脏病手术数据库，我们建立了一个主要结果为主要感染(败血症、纵隔炎或心内膜炎)的多变量模型。候选自变量包括人口学特征、合并症、术前因素和心脏手术操作。我们通过反向选择创建了一个简化的模型，然后使用一个比例因子创建了一个整数评分系统，评分对应于感染风险的百分比。在48个中心的30078名儿童中，2.8%有重大感染(败血症2.6%，纵隔炎0.3%，心内膜炎0.09%)。有严重感染的患者的死亡率和术后住院时间更长(死亡率：22.2%比3.0%；住院时间21天：69.9%比10.7%)。年龄小、复杂性高、有心胸外科手术史、术前住院时间1天、术前呼吸机支持和基因异常与反向选择后发生重大感染有关(p<；0.001)。估计的感染风险在0.1%到13.3%之间；模型识别率很好(c指数0.79)。我们创造了一个简单的床边工具来识别心脏手术后严重感染的高危儿童。这些患者可能成为降低感染风险的干预措施的目标对象，并被纳入未来的临床试验。
In pediatric cardiac surgery, infection is a leading cause of morbidity and mortality. We created a model to predict risk of major infection in this population. Using the Society of Thoracic Surgeons Congenital Heart Surgery Database, we created a multivariable model in which the primary outcome was major infection (septicemia, mediastinitis, or endocarditis). Candidate independent variables included demographic characteristics, comorbid conditions, preoperative factors, and cardiac surgical procedures. We created a reduced model by backward selection and then created an integer scoring system using a scaling factor with scores corresponding to percent risk of infection. Of 30,078 children from 48 centers, 2.8% had major infection (2.6% septicemia, 0.3% mediastinitis, and 0.09% endocarditis). Mortality and postoperative length of stay were greater in those with major infection (mortality: 22.2% vs. 3.0%; length of stay > 21 days: 69.9% vs. 10.7%). Young age, high complexity, previous cardiothoracic operation, preoperative length of stay >1 day, preoperative ventilator support, and presence of a genetic abnormality were associated with major infection after backward selection (p<0.001). Estimated infection risk ranged from <0.1% to 13.3%; the model discrimination was good (c-index 0.79). We created a simple bedside tool to identify children at high risk for major infection after cardiac surgery. These patients may be targeted for interventions to reduce the risk of infection and for inclusion in future clinical trials.
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