Clinical predictors of major infections after cardiac surgery

被引:270
作者
Fowler, VG
O'Brien, SM
Muhlbaier, LH
Corey, GR
Ferguson, TB
Peterson, ED
机构
[1] Duke Univ, Med Ctr, Dept Med, Durham, NC 27710 USA
[2] Duke Univ, Med Ctr, Dept Biostat, Durham, NC 27710 USA
[3] Duke Univ, Med Ctr, Duke Clin Res Inst, Durham, NC 27710 USA
[4] Louisiana State Univ, Hlth Sci Ctr, New Orleans, LA USA
关键词
infection; diagnosis; bypass; surgery;
D O I
10.1161/CIRCULATIONAHA.104.525790
中图分类号
R5 [内科学];
学科分类号
1002 [临床医学]; 100201 [内科学];
摘要
Background-Major infections are infrequent but important complications of cardiac surgery. Predicting their occurrence is essential for future prevention. The objective of the current investigation was to create and validate a bedside scoring system to estimate patient risk for major infection (mediastinitis, thoracotomy or vein harvest site infection, or septicemia) after coronary artery bypass grafting. Methods and Results-Using the Society of Thoracic Surgeons National Cardiac Database, we analyzed 331429 coronary artery bypass grafting cases from January 1, 2002, to December 31, 2003, to identify risk factors for major infection. Using logistic regression, 2 models were generated and validated using split-sample validation: (1) One limited to preoperative characteristics (preop model) and (2) one model including both preoperative and intraoperative characteristics (combined model). Major infection occurred in 11636 patients (3.51%) (25.1% mediastinitis, 32.6% saphenous harvest site, 35.0% septicemia, 0.5% thoracotomy, 6.8% multiple sites). Patients with major infection had significantly higher mortality (17.3% versus 3.0%, P < 0.0001) and postoperative length of stay > 14 days (47.0% versus 5.9%, P < 0.0001) than patients without major infection. Both the preop model (c-index 0.697) and combined model (c-index: 0.708) successfully discriminated between high- and low-risk patients. A simplified risk scoring system of 12 variables accurately predicted risk for major infection. Conclusions-We identified and validated a model that can identify patients undergoing cardiac surgery who are at high risk for major infection. These high-risk patients may be targeted for perioperative intervention strategies to reduce rates of major infection.
引用
收藏
页码:I358 / I365
页数:8
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