West China School of Nursing, Sichuan University, Chengdu 610041, China.
Department of Nursing, West China Hospital, Sichuan University, Chengdu 610041, China.
Exp Biol Med (Maywood). 2020 Oct;245(16):1513-1517. doi: 10.1177/1535370220945290. Epub 2020 Jul 26.
The ability to predict surgical site infections (SSIs) early would be advantageous. Previous studies have investigated the use of inflammatory factors in fluids drained from surgical sites to predict SSI, but the diagnostic efficacy of this method requires improvement. Baseline levels of inflammatory factors vary between individuals, but this variation tends to differ in patients with and without SSIs. Therefore, we standardized subsequently acquired concentrations of interleukin 6 and C-reactive protein in fluids drained from surgical sites by dividing them by the concentrations determined at day 1 to preclude the confounding effects of differences in baseline levels. The standardized concentrations had higher predictive efficacy than the absolute concentrations. Standardizing the data rendered SSI prediction more precise and practical in a diverse group of real patients. This translational study suggests that inflammatory factors in fluid drained from injury sites are promising tools for the prediction of SSI in the clinic.
能够早期预测手术部位感染(SSI)将是有利的。先前的研究已经探讨了使用从手术部位引流的液体中的炎症因子来预测 SSI,但该方法的诊断效果需要提高。炎症因子的基线水平在个体之间存在差异,但在有和没有 SSI 的患者中,这种差异往往不同。因此,我们通过将第 1 天确定的浓度除以随后从手术部位引流的液体中获得的白细胞介素 6 和 C 反应蛋白的浓度来标准化,以排除基线水平差异的混杂影响。标准化浓度比绝对浓度具有更高的预测效果。数据标准化使 SSI 预测在多样化的真实患者群体中更加精确和实用。这项转化研究表明,损伤部位引流液中的炎症因子是预测临床 SSI 的有前途的工具。