1Division of Critical Care Medicine, Cincinnati Children's Hospital Medical Center and Cincinnati Children's Hospital Research Foundation, Cincinnati, OH. 2Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH. 3Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, OH. 4Intensive Care Units, Division of Anaesthesia and Intensive Care Medicine, Department of Surgery, Helsinki University Central Hospital, Helsinki, Finland. 5Pulmonary, Allergy, and Critical Care Division, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA. 6University of British Columbia, Vancouver, BC, Canada. 7Critical Care Research Laboratories, Centre for Heart Lung Innovation, St. Paul's Hospital, Vancouver, BC, Canada. 8Department of Intensive Care Medicine, Tampere University Hospital, Tampere, Finland. 9Department of Intensive Care Medicine, Kuopio University Hospital, Kuopio, Finland. 10Department of Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA.
Crit Care Med. 2014 Apr;42(4):781-9. doi: 10.1097/CCM.0000000000000106.
Clinical trials in septic shock continue to fail due, in part, to inequitable and sometimes unknown distribution of baseline mortality risk between study arms. Investigators advocate that interventional trials in septic shock require effective outcome risk stratification. We derived and tested a multibiomarker-based approach to estimate mortality risk in adults with septic shock.
Previous genome-wide expression studies identified 12 plasma proteins as candidates for biomarker-based risk stratification. The current analysis used banked plasma samples and clinical data from existing studies. Biomarkers were assayed in plasma samples obtained from 341 subjects with septic shock within 24 hours of admission to the ICU. Classification and regression tree analysis was used to generate a decision tree predicting 28-day mortality based on a combination of both biomarkers and clinical variables. The derived tree was first tested in an independent cohort of 331 subjects, then calibrated using all subjects (n = 672), and subsequently validated in another independent cohort (n = 209).
Multiple ICUs in Canada, Finland, and the United States.
Eight hundred eighty-one adults with septic shock or severe sepsis.
None.
The derived decision tree included five candidate biomarkers, admission lactate concentration, age, and chronic disease burden. In the derivation cohort, sensitivity for mortality was 94% (95% CI, 87-97), specificity was 56% (50-63), positive predictive value was 50% (43-57), and negative predictive value was 95% (89-98). Performance was comparable in the test cohort. The calibrated decision tree had the following test characteristics in the validation cohort: sensitivity 85% (76-92), specificity 60% (51-69), positive predictive value 61% (52-70), and negative predictive value 85% (75-91).
We have derived, tested, calibrated, and validated a risk stratification tool and found that it reliably estimates the probability of mortality in adults with septic shock.
部分由于研究组间基线死亡率风险分配不均等甚至未知，脓毒性休克的临床试验仍在持续失败。研究人员主张，脓毒性休克的干预性试验需要有效的结局风险分层。我们提出并检验了一种基于多生物标志物的方法来估计患有脓毒性休克的成年人的死亡率风险。
先前的全基因组表达研究确定了 12 种血浆蛋白作为基于生物标志物的风险分层的候选物。本分析使用了来自现有研究的储存血浆样本和临床数据。对 341 例脓毒性休克患者入院后 24 小时内获得的血浆样本进行了生物标志物检测。使用分类和回归树分析来生成一个基于生物标志物和临床变量组合预测 28 天死亡率的决策树。首先在 331 例独立队列中测试生成的树，然后使用所有受试者（n = 672）进行校准，随后在另一个独立队列（n = 209）中进行验证。
加拿大、芬兰和美国的多个 ICU。
881 例脓毒性休克或严重脓毒症的成年人。
无。
生成的决策树包括 5 个候选生物标志物、入院时的乳酸浓度、年龄和慢性疾病负担。在推导队列中，死亡率的敏感性为 94%（95%CI，87-97），特异性为 56%（50-63），阳性预测值为 50%（43-57），阴性预测值为 95%（89-98）。在测试队列中，性能相当。校准后的决策树在验证队列中的测试特征如下：敏感性 85%（76-92），特异性 60%（51-69），阳性预测值 61%（52-70），阴性预测值 85%（75-91）。
我们已经提出、测试、校准和验证了一种风险分层工具，并发现它能够可靠地估计患有脓毒性休克的成年人的死亡率概率。
