Bohman J Kyle, Hyder Joseph A, Iyer Vivek, Pannu Sonal R, Moreno Franco Pablo, Seelhammer Troy G, Schenck Louis A, Schears Gregory J
Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic, Rochester, MN.
Department of Anesthesiology, Division of Critical Care Medicine, Mayo Clinic, Rochester, MN.
J Crit Care. 2016 Jun;33:125-31. doi: 10.1016/j.jcrc.2016.01.021. Epub 2016 Jan 27.
Appropriately identifying and triaging patients with newly diagnosed acute respiratory distress syndrome (ARDS) who may progress to severe ARDS is a common clinical challenge without any existing tools for assistance.
Using a retrospective cohort, a simple prediction score was developed to improve early identification of ARDS patients who were likely to progress to severe ARDS within 7 days. A broad array of comorbidities and physiologic variables were collected for the 12-hour period starting from intubation for ARDS. Extracorporeal membrane oxygenation (ECMO) eligibility was determined based on published criteria from recent ECMO guidelines and clinical trials. Separate data-driven and expert opinion approaches to prediction score creation were completed.
The study included 767 patients with moderate or severe ARDS who were admitted to the intensive care unit between January 1, 2005, and December 31, 2010. In the data-driven approach, incorporating the ARDS index (a novel variable incorporating oxygenation index and estimated dead space), aspiration, and change of Pao2/fraction of inspired oxygen ratio into a simple prediction model yielded a c-statistic (area under the receiver operating characteristic curve) of 0.71 in the validation cohort. The expert opinion-based prediction score (including oxygenation index, change of Pao2/fraction of inspired oxygen ratio, obesity, aspiration, and immunocompromised state) yielded a c-statistic of 0.61 in the validation cohort.
The data-driven early prediction ECMO eligibility for severe ARDS score uses commonly measured variables of ARDS patients within 12 hours of intubation and could be used to identify those patients who may merit early transfer to an ECMO-capable medical center.
准确识别和分流新诊断的急性呼吸窘迫综合征(ARDS)患者中可能进展为重度ARDS的患者是一项常见的临床挑战,目前尚无任何辅助工具。
采用回顾性队列研究,开发了一个简单的预测评分,以改善对可能在7天内进展为重度ARDS的ARDS患者的早期识别。从ARDS插管开始的12小时内收集了一系列共病情况和生理变量。根据近期体外膜肺氧合(ECMO)指南和临床试验公布的标准确定ECMO的适用性。分别完成了基于数据驱动和专家意见的预测评分创建方法。
该研究纳入了2005年1月1日至2010年12月31日期间入住重症监护病房的767例中度或重度ARDS患者。在数据驱动方法中,将ARDS指数(一个包含氧合指数和预计死腔的新变量)、误吸以及动脉血氧分压/吸入氧分数比的变化纳入一个简单的预测模型,在验证队列中得到的c统计量(受试者操作特征曲线下面积)为0.71。基于专家意见的预测评分(包括氧合指数、动脉血氧分压/吸入氧分数比的变化、肥胖、误吸和免疫功能低下状态)在验证队列中的c统计量为0.61。
基于数据驱动的重度ARDS早期预测ECMO适用性评分使用了ARDS患者插管后12小时内常用的测量变量,可用于识别那些可能值得早期转至具备ECMO能力的医疗中心的患者。