1 Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, School of Public Health, Monash University, Melbourne, Australia.
Am J Respir Crit Care Med. 2014 Jun 1;189(11):1374-82. doi: 10.1164/rccm.201311-2023OC.
Increasing use of extracorporeal membrane oxygenation (ECMO) for acute respiratory failure may increase resource requirements and hospital costs. Better prediction of survival in these patients may improve resource use, allow risk-adjusted comparison of center-specific outcomes, and help clinicians to target patients most likely to benefit from ECMO.
To create a model for predicting hospital survival at initiation of ECMO for respiratory failure.
Adult patients with severe acute respiratory failure treated by ECMO from 2000 to 2012 were extracted from the Extracorporeal Life Support Organization (ELSO) international registry. Multivariable logistic regression was used to create the Respiratory ECMO Survival Prediction (RESP) score using bootstrapping methodology with internal and external validation.
Of the 2,355 patients included in the study, 1,338 patients (57%) were discharged alive from hospital. The RESP score was developed using pre-ECMO variables independently associated with hospital survival on logistic regression, which included age, immunocompromised status, duration of mechanical ventilation before ECMO, diagnosis, central nervous system dysfunction, acute associated nonpulmonary infection, neuromuscular blockade agents or nitric oxide use, bicarbonate infusion, cardiac arrest, PaCO2, and peak inspiratory pressure. The receiver operating characteristics curve analysis of the RESP score was c = 0.74 (95% confidence interval, 0.72-0.76). External validation, performed on 140 patients, exhibited excellent discrimination (c = 0.92; 95% confidence interval, 0.89-0.97).
The RESP score is a relevant and validated tool to predict survival for patients receiving ECMO for respiratory failure.
体外膜肺氧合(ECMO)在急性呼吸衰竭中的应用日益增多,可能会增加资源需求和医院成本。更好地预测这些患者的生存率可能会改善资源利用,允许对特定中心结局进行风险调整比较,并帮助临床医生针对最有可能从 ECMO 中获益的患者。
为预测呼吸衰竭患者启动 ECMO 时的院内生存率建立模型。
从 2000 年至 2012 年接受 ECMO 治疗的严重急性呼吸衰竭的成年患者中,从体外生命支持组织(ELSO)国际登记处提取数据。使用 bootstrap 方法进行多变量逻辑回归,创建呼吸 ECMO 生存预测(RESP)评分,并进行内部和外部验证。
在纳入的 2355 例患者中,1338 例(57%)出院时存活。RESP 评分是使用与逻辑回归中与医院生存率相关的 ECMO 前变量开发的,这些变量包括年龄、免疫功能低下状态、ECMO 前机械通气时间、诊断、中枢神经系统功能障碍、急性相关非肺部感染、神经肌肉阻滞剂或一氧化氮使用、碳酸氢盐输注、心脏骤停、PaCO2 和吸气峰压。RESP 评分的受试者工作特征曲线分析 c = 0.74(95%置信区间,0.72-0.76)。在 140 例患者中进行的外部验证显示出良好的判别能力(c = 0.92;95%置信区间,0.89-0.97)。
RESP 评分是预测接受 ECMO 治疗呼吸衰竭患者生存率的一个相关且经过验证的工具。