1 Division of Anaesthesia, Intensive Care and Pain Management, John Hunter Hospital, Hunter New England Area Health Service, NSW, Australia. 2 Centre for Translational Neuroscience and Mental Health (CTNMH), University of Newcastle, NSW, Australia. 3 Department of Consultation-Liaison Psychiatry, Calvary Mater Newcastle Hospital, NSW, Australia. 4 Address correspondence to: Jorge Brieva, FCICM, PGDip Echo, Division of Anaesthesia, Intensive Care and Pain Management John Hunter Hospital, Hunter New England Area Health Service, NSW, Australia.
Transplantation. 2014 Nov 27;98(10):1112-8. doi: 10.1097/TP.0000000000000186.
Given the stable number of potential organ donors after brain death, donors after circulatory death have been an increasing source of organs procured for transplant. Among the most important considerations for donation after circulatory death (DCD) is the prediction that death will occur within a reasonable period of time after the withdrawal of cardiorespiratory support (WCRS). Accurate prediction of time to death is necessary for the procurement process. We aimed to develop simple predictive rules for death in less than 60 min and test the accuracy of these rules in a pool of potential DCD donors.
A multicenter prospective longitudinal cohort design of DCD eligible patients (n=318), with the primary binary outcome being death in less than 60 min after withdrawal of cardiorespiratory support conducted in 28 accredited intensive care units (ICUs) in Australia. We used a random split-half method to produce two samples, first to develop the predictive classification rules and then to estimate accuracy in an independent sample.
The best classification model used only three simple classification rules to produce an overall efficiency of 0.79 (0.72-0.85), sensitivity of 0.82 (0.73-0.90), and a positive predictive value of 0.80 (0.70-0.87) in the independent sample. Using only intensive care unit specialist prediction (a single classification rule) produced comparable efficiency 0.80 (0.73-0.86), sensitivity 0.87 (0.78-0.93), and positive predictive value 0.78 (0.68-0.86).
This best predictive model missed only 18% of all potential donors. A positive prediction would be incorrect on only 20% of occasions, meaning there is an acceptable level of lost opportunity costs involved in the unnecessary assembly of transplantation teams and theatres.
在脑死亡后,潜在的器官供体数量稳定,因此循环死亡后的供体成为获取移植器官的一个不断增加的来源。在循环死亡后捐献(DCD)中最重要的考虑因素之一是,预计在心肺支持(WCRS)撤除后,在合理的时间内将发生死亡。准确预测死亡时间对于获取过程是必要的。我们旨在为 60 分钟内死亡制定简单的预测规则,并在潜在的 DCD 供体池中测试这些规则的准确性。
在澳大利亚 28 个认可的重症监护病房(ICU)中,对 DCD 合格患者(n=318)进行了多中心前瞻性纵向队列设计,主要的二项结果是 WCRS 后 60 分钟内死亡。我们使用随机半分割方法生成两个样本,首先用于开发预测分类规则,然后用于估计独立样本中的准确性。
最佳分类模型仅使用三个简单的分类规则,在独立样本中产生了 0.79(0.72-0.85)的总体效率、0.82(0.73-0.90)的敏感性和 0.80(0.70-0.87)的阳性预测值。仅使用重症监护专家预测(单一分类规则)产生了可比较的效率 0.80(0.73-0.86)、敏感性 0.87(0.78-0.93)和阳性预测值 0.78(0.68-0.86)。
该最佳预测模型仅错过了 18%的所有潜在供体。阳性预测只有 20%的几率不正确,这意味着在不必要的移植团队和手术室组建中涉及可接受的机会成本损失。