Slotman G J
Department of Surgery, University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, Cooper Hospital/University Medical Center, Camden, New Jersey, USA.
Crit Care. 2000;4(5):319-26. doi: 10.1186/cc715. Epub 2000 Sep 8.
Clinically useful predictions of end-organ function and failure in severe sepsis may be possible through analyzing the interactions among demographics, physiologic parameters, standard laboratory tests, and circulating markers of inflammation. The present study evaluated the ability of such a methodology, the Systemic Mediator Associated Response Test (SMART), to predict the clinical course of septic surgery patients from a database of medical and surgical patients with severe sepsis and/or septic shock.
Three hundred and three patients entered into the placebo arm of a multi-institutional sepsis study were randomly assigned to a model-building cohort (n = 200; 119 surgical) or to a predictive cohort (n = 103; 55 surgical). Using baseline and baseline plus serial measurements of physiologic data, standard laboratory tests, and plasma levels of IL-6, IL-8, and granulocyte colony-stimulating factor (GCSF), multivariate models were developed that predicted the presence or absence of pulmonary edema on chest radiography, and respiratory, renal, coagulation, hepatobiliary, or central nervous system dysfunction and shock in individual patients. Twenty-eight-day survival was predicted also in baseline plus serial data models. These models were validated prospectively by inserting baseline raw data from the 55 surgical patients in the predictive cohort into the models built on the comprehensive training cohort, and calculating the area under the curve (AUC) of predicted versus observed receiver operator characteristic (ROC) plots.
SMART predictions of physiologic, respiratory, metabolic, hepatic, renal, and hematologic function indicators were validated prospectively, frequently at clinically useful levels of accuracy. ROC AUC values above 0.700 were achieved in 30 out of 49 (61%) of SMART baseline models in predicting shock and organ failure up to 7 days in advance, and in 30 out of 54 (56%) of baseline plus serial data models.
SMART multivariate models accurately predict pathophysiology, shock, and organ failure in individual septic surgical patients. These prognostications may facilitate early treatment of end-organ dysfunction in surgical sepsis.
通过分析人口统计学、生理参数、标准实验室检查以及循环炎症标志物之间的相互作用，有可能对严重脓毒症患者的终末器官功能和衰竭做出临床上有用的预测。本研究评估了一种名为全身介质相关反应测试（SMART）的方法，能否从患有严重脓毒症和/或脓毒性休克的内科及外科患者数据库中预测脓毒症手术患者的临床病程。
303名进入多机构脓毒症研究安慰剂组的患者被随机分配到模型构建队列（n = 200；119名外科患者）或预测队列（n = 103；55名外科患者）。利用生理数据、标准实验室检查以及白细胞介素-6、白细胞介素-8和粒细胞集落刺激因子（GCSF）血浆水平的基线及基线加连续测量值，建立了多变量模型，用于预测个体患者胸部X线片上是否存在肺水肿以及呼吸、肾脏、凝血、肝胆或中枢神经系统功能障碍和休克情况。还在基线加连续数据模型中预测了28天生存率。通过将预测队列中55名外科患者的基线原始数据插入基于综合训练队列构建的模型中，并计算预测与观察的受试者工作特征（ROC）曲线下面积（AUC），对这些模型进行了前瞻性验证。
SMART对生理、呼吸、代谢、肝脏、肾脏和血液学功能指标的预测得到了前瞻性验证，准确性常常达到临床上有用的水平。在预测休克和器官衰竭提前7天的情况时，49个SMART基线模型中有30个（61%）的ROC AUC值高于0.700，在基线加连续数据模型中，54个中有30个（56%）达到该值。
SMART多变量模型能准确预测个体脓毒症手术患者的病理生理学、休克和器官衰竭情况。这些预后评估可能有助于外科脓毒症终末器官功能障碍的早期治疗。
