Validation of the riboleukogram to detect ventilator-associated pneumonia after severe injury.
Validation of the riboleukogram to detect ventilator-associated pneumonia after severe injury.
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DOI：
10.1097/sla.0b013e3181b8fbd5
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发表时间：
2009-10
影响因子：
9
通讯作者：
Maier RV
中科院分区：
文献类型：
--
作者：
Cobb JP;Moore EE;Hayden DL;Minei JP;Cuschieri J;Yang J;Li Q;Lin N;Brownstein BH;Hennessy L;Mason PH;Schierding WS;Dixon DJ;Tompkins RG;Warren HS;Schoenfeld DA;Maier RV
我们假设循环白细胞RNA谱或“核糖白细胞图”检测钝性创伤后呼吸机相关性肺炎。对11例呼吸机相关性肺炎（VAP）患者的初步微阵列研究表明，85个白细胞基因可用于诊断VAP。使用来自独立患者队列的数据测试了该基因集检测VAP的验证。共有158例插管钝性创伤患者在5个中心入组，其中57例（36%）发生VAP。患者年龄为34.2 ± 11.1岁; 65%为男性。在损伤后0.5、1、4、7、14、21和28天测量循环白细胞GeneChip U133 2.0表达值。使用重复测量逻辑回归分析DChip标准化的白细胞转录谱。对基于患者训练亚组中白细胞基因转录谱的复合协变量模型进行了测试，以确定测试亚组中临床诊断前4天VAP的预测准确性。使用FDR <0.05时每个研究日测量的基因表达值，85个基因中的27个（32%）与诊断前1至4天的VAP诊断相关。然而，基于这85个基因的复合协变量模型并不能比偶然更好地预测测试队列中的VAP（P = 0.27）。相比之下，基于158例患者的从头转录分析的复合协变量模型预测VAP优于诊断前4天的机会，灵敏度为57%，特异性为69%。我们的研究结果验证了在一项初步研究中所描述的那些，证实了核糖白细胞图与临床诊断前几天VAP的发展相关。同样，在158例患者的较大队列中测试的核糖体白细胞图预测模型在临床诊断前预测VAP天数方面优于随机预测。
We hypothesized that circulating leukocyte RNA profiles or “riboleukograms” detect ventilator-associated pneumonia after blunt trauma. A pilot microarray study of 11 ventilator-associated pneumonia (VAP) patients suggested that 85 leukocyte genes can be used to diagnose VAP. Validation of this gene set to detect VAP was tested using data from an independent patient cohort. A total of 158 intubated blunt trauma patients were enrolled at 5 centers, where 57 (36%) developed VAP. Patient age was 34.2 ± 11.1 years; 65% were male. Circulating leukocyte GeneChip U133 2.0 expression values were measured at time 0.5, 1, 4, 7, 14, 21, and 28 days after injury. DChip normalized leukocyte transcriptional profiles were analyzed using repeated measures logistic regression. A compound covariate model based on leukocyte gene transcriptional profiles in a training subset of patients was tested to determine predictive accuracy for VAP 4 days prior to clinical diagnosis in the test subset. Using gene expression values measured on each study day at an FDR <0.05, 27 (32%) of the 85 genes were associated with the diagnosis of VAP 1 to 4 days before diagnosis. However, the compound covariate model based on these 85-genes did not predict VAP in the test cohort better than chance (P = 0.27). In contrast, a compound covariate model based upon de novo transcriptional analysis of the 158 patients predicted VAP better than chance 4 days before diagnosis with a sensitivity of 57% and a specificity of 69%. Our results validate those described in a pilot study, confirming that riboleukograms are associated with the development of VAP days prior to clinical diagnosis. Similarly, a riboleukogram predictive model tested on a larger cohort of 158 patients was better than chance at predicting VAP days prior to clinical diagnosis.