Department of Anaesthesiology and Intensive Care, Kulliyyah of Medicine, International Islamic University Malaysia, 25200 Kuantan, Pahang, Malaysia; Department of Anaesthesiology and Intensive Care, School of Medical Sciences, Universiti Sains Malaysia, 16150 Kubang Kerian, Kelantan, Malaysia.
Department of Anaesthesiology and Intensive Care, Kulliyyah of Medicine, International Islamic University Malaysia, 25200 Kuantan, Pahang, Malaysia.
J Crit Care. 2018 Feb;43:163-168. doi: 10.1016/j.jcrc.2017.09.009. Epub 2017 Sep 6.
To derive a prediction equation for 30-day mortality in sepsis using a multi-marker approach and compare its performance to the Sequential Organ Failure Assessment (SOFA) score.
This study included 159 septic patients admitted to an intensive care unit. Leukocytes count, procalcitonin (PCT), interleukin-6 (IL-6), and paraoxonase (PON) and arylesterase (ARE) activities of PON-1 were assayed from blood obtained on ICU presentation. Logistic regression was used to derive sepsis mortality score (SMS), a prediction equation describing the relationship between biomarkers and 30-day mortality.
The 30-day mortality rate was 28.9%. The SMS was [еlogit(p)/(1+еlogit(p))]×100; logit(p)=0.74+(0.004×PCT)+(0.001×IL-6)-(0.025×ARE)-(0.059×leukocytes count). The SMC had higher area under the receiver operating characteristic curve (95% Cl) than SOFA score [0.814 (0.736-0.892) vs. 0.767 (0.677-0.857)], but is not statistically significant. When the SMS was added to the SOFA score, prediction of 30-day mortality improved compared to SOFA score used alone [0.845 (0.777-0.899), p=0.022].
A sepsis mortality score using baseline leukocytes count, PCT, IL-6 and ARE was derived, which predicted 30-day mortality with very good performance and added significant prognostic information to SOFA score.
使用多标志物方法推导脓毒症 30 天死亡率的预测方程，并将其与序贯器官衰竭评估（SOFA）评分进行比较。
本研究纳入了 159 名入住重症监护病房的脓毒症患者。在入住 ICU 时采集血液，检测白细胞计数、降钙素原（PCT）、白细胞介素-6（IL-6）以及 PON-1 的对氧磷酶（PON）和芳基酯酶（ARE）活性。使用逻辑回归推导脓毒症死亡率评分（SMS），这是一个描述生物标志物与 30 天死亡率之间关系的预测方程。
30 天死亡率为 28.9%。SMS 为 [еlogit(p)/(1+еlogit(p))]×100；logit(p)=0.74+(0.004×PCT)+(0.001×IL-6)-(0.025×ARE)-(0.059×白细胞计数)。SMS 的受试者工作特征曲线下面积（95%置信区间）大于 SOFA 评分[0.814（0.736-0.892）与 0.767（0.677-0.857）]，但无统计学意义。与单独使用 SOFA 评分相比，将 SMS 添加到 SOFA 评分中可改善 30 天死亡率的预测[0.845（0.777-0.899），p=0.022]。
使用基线白细胞计数、PCT、IL-6 和 ARE 推导了一个脓毒症死亡率评分，该评分能够很好地预测 30 天死亡率，并为 SOFA 评分提供了重要的预后信息。
