Mikacenic Carmen, Price Brenda L, Harju-Baker Susanna, O'Mahony D Shane, Robinson-Cohen Cassianne, Radella Frank, Hahn William O, Katz Ronit, Christiani David C, Himmelfarb Jonathan, Liles W Conrad, Wurfel Mark M
1 Division of Pulmonary and Critical Care Medicine, Department of Medicine.
2 Department of Biostatistics.
Am J Respir Crit Care Med. 2017 Oct 15;196(8):1004-1011. doi: 10.1164/rccm.201611-2307OC.
Improving the prospective identification of patients with systemic inflammatory response syndrome (SIRS) and sepsis at low risk for organ dysfunction and death is a major clinical challenge.
To develop and validate a multibiomarker-based prediction model for 28-day mortality in critically ill patients with SIRS and sepsis.
A derivation cohort (n = 888) and internal test cohort (n = 278) were taken from a prospective study of critically ill intensive care unit (ICU) patients meeting two of four SIRS criteria at an academic medical center for whom plasma was obtained within 24 hours. The validation cohort (n = 759) was taken from a prospective cohort enrolled at another academic medical center ICU for whom plasma was obtained within 48 hours. We measured concentrations of angiopoietin-1, angiopoietin-2, IL-6, IL-8, soluble tumor necrosis factor receptor-1, soluble vascular cell adhesion molecule-1, granulocyte colony-stimulating factor, and soluble Fas.
We identified a two-biomarker model in the derivation cohort that predicted mortality (area under the receiver operator characteristic curve [AUC], 0.79; 95% confidence interval [CI], 0.74-0.83). It performed well in the internal test cohort (AUC, 0.75; 95% CI, 0.65-0.85) and the external validation cohort (AUC, 0.77; 95% CI, 0.72-0.83). We determined a model score threshold demonstrating high negative predictive value (0.95) for death. In addition to a low risk of death, patients below this threshold had shorter ICU length of stay, lower incidence of acute kidney injury, acute respiratory distress syndrome, and need for vasopressors.
We have developed a simple, robust biomarker-based model that identifies patients with SIRS/sepsis at low risk for death and organ dysfunction.
改善对全身炎症反应综合征（SIRS）和脓毒症患者发生器官功能障碍和死亡低风险的前瞻性识别是一项重大临床挑战。
开发并验证一种基于多种生物标志物的预测模型，用于预测患有SIRS和脓毒症的危重症患者的28天死亡率。
推导队列（n = 888）和内部测试队列（n = 278）取自一项对在一所学术医疗中心符合四项SIRS标准中的两项的危重症重症监护病房（ICU）患者的前瞻性研究，这些患者在24小时内采集了血浆。验证队列（n = 759）取自另一所学术医疗中心ICU登记的前瞻性队列，这些患者在48小时内采集了血浆。我们测量了血管生成素-1、血管生成素-2、白细胞介素-6、白细胞介素-8、可溶性肿瘤坏死因子受体-1、可溶性血管细胞黏附分子-1、粒细胞集落刺激因子和可溶性Fas的浓度。
我们在推导队列中确定了一个双生物标志物模型，该模型可预测死亡率（受试者工作特征曲线下面积[AUC]，0.79；95%置信区间[CI]，0.74 - 0.83）。它在内部测试队列（AUC，0.75；95% CI，0.65 - 0.85）和外部验证队列（AUC，0.77；95% CI，0.72 - 0.83）中表现良好。我们确定了一个模型评分阈值，该阈值显示出对死亡的高阴性预测值（0.95）。低于此阈值的患者除了死亡风险低之外，ICU住院时间更短，急性肾损伤、急性呼吸窘迫综合征的发生率更低，且对血管加压药的需求也更低。
我们开发了一种简单、可靠的基于生物标志物的模型，该模型可识别出患有SIRS/脓毒症且死亡和器官功能障碍风险低的患者。
