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A New Prediction System of Sepsis: A Retrospective, Clinical Study

机译:脓毒症的新预测系统:回顾性临床研究

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Objective: Analyzing 6 biomarkers, such as procalcitonin (PCT), C-reactive protein (CRP), fibrinogen (Fib), lactate concentration (Lac), D-dimer (D-d), neutrophil ratio (NEUT%) to figure out several sensitive indicators and establish a new prediction system of sepsis, which could achieve a higher sensitivity and specificity to predict sepsis. Methods: We collected 113 SIRS patients in ICU. According to their prognosis, all the patients were divided into two groups named sepsis and non-sepsis group according to the diagnostic criteria of sepsis. We recorded the general information and detected the plasma levels of the 6 biomarkers. Results: The plasma levels of NEUT% and Fib between the two groups had no significant difference. PCT had the highest prediction accuracy of sepsis compared with other biomarkers. A predictive model was established, in which Lac, PCT, CRP were enrolled. The final prediction model was: logit(P) = 0.314 + 0.105 × Lac(mmol/l) + 0.099 × PCT(ng/mL) + 0.012 × CRP(mg/L). The area under the curve of the prediction model was 0.893, which was higher than every single biomarker involved in this study. Conclusion: The three serum biomarkers of Lac, PCT, CRP are used to establish a prediction model of sepsis: logit(P) = 0.314 + 0.105 × Lac(mmol/l) + 0.099 × PCT(ng/mL) + 0.012 × CRP(mg/L), which can better predict the occurrence of sepsis compared with other biomarkers.
机译:目的:分析降钙素原(PCT),C反应蛋白(CRP),纤维蛋白原(Fib),乳酸浓度(Lac),D-二聚体(Dd),中性白细胞比(NEUT%)等6种生物标志物,以找出几种敏感的标志物。指标,建立了新的败血症预测系统,可以较高的敏感性和特异性来预测败血症。方法:我们在ICU中收集了113例SIRS患者。根据其预后,根据脓毒症的诊断标准将其分为脓毒症和非败血症两组。我们记录了一般信息并检测了6种生物标志物的血浆水平。结果:两组血浆NEUT%和Fib水平无明显差异。与其他生物标记相比,PCT具有最高的败血症预测准确性。建立了预测模型,其中纳入了Lac,PCT,CRP。最终预测模型为:logit(P)= 0.314 + 0.105×Lac(mmol / l)+ 0.099×PCT(ng / mL)+ 0.012×CRP(mg / L)。预测模型曲线下的面积为0.893,高于参与本研究的每个生物标志物。结论:使用Lac,PCT,CRP的三种血清生物标志物建立脓毒症的预测模型:logit(P)= 0.314 + 0.105×Lac(mmol / l)+ 0.099×PCT(ng / mL)+ 0.012×CRP (mg / L),与其他生物标记相比可以更好地预测败血症的发生。

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