1 Department of Medicine III (Interdisciplinary Medical Intensive Care), University of Freiburg, Germany.
2 Department of Cardiology and Angiology I, Heart Center Freiburg University, Germany.
Eur Heart J Acute Cardiovasc Care. 2019 Jun;8(4):350-359. doi: 10.1177/2048872618789052. Epub 2018 Jul 13.
Several scoring systems have been introduced for prognostication after initiating venoarterial extracorporeal membrane oxygenation (VA-ECMO) therapy. However, static scores offer limited guidance once VA-ECMO is implanted, although continued allocation of healthcare resources is critical. Patients requiring continued VA-ECMO support are extremely unstable, with minimal heart function and multi-organ failure in most cases. The aim of the present study was to develop and validate a dynamic prognostic model for patients treated with VA-ECMO.
A derivation cohort included 205 all-comers undergoing VA-ECMO implantation at a tertiary referral hospital (51% received VA-ECMO during resuscitation and 43% had severe shock). Two prediction models based on point-of-care biomarkers were developed using penalised logistic regression in an elastic net approach. A validation cohort was recruited from an independent tertiary referral hospital. Comparators for the prediction of hospital survival were the SAVE score (area under the receiver operation characteristic curve (AUC) of 0.686), the SAPS score (AUC 0.679), the APACHE score (AUC 0.662) and the SOFA score (AUC 0.732) in 6-hour survivors. The 6-hour PREDICT VA-ECMO score (based on lactate, pH and standard bicarbonate concentration) outperformed the comparator scores with an AUC of 0.823. The 12-hour PREDICT VA-ECMO integrated lactate, pH and standard bicarbonate concentration at 1 hour, 6 hours and 12 hours after ECMO insertion allowed even better prognostication (AUC 0.839). Performance of the scores in the external validation cohort was good (AUCs 0.718 for the 6-hour score and 0.735 for the 12-hour score, respectively).
In patients requiring VA-ECMO therapy, a dynamic score using three point-of-care biomarkers predicts hospital mortality with high reliability. Furthermore, the PREDICT scores are the first scores for extracorporeal cardiopulmonary resuscitation patients.
已经引入了几种评分系统来预测启动静脉动脉体外膜肺氧合(VA-ECMO)治疗后的预后。然而,一旦植入 VA-ECMO,静态评分提供的指导有限,尽管继续分配医疗资源至关重要。需要持续 VA-ECMO 支持的患者极其不稳定,大多数情况下心脏功能极小,多器官衰竭。本研究旨在为接受 VA-ECMO 治疗的患者开发和验证一种动态预后模型。
一个推导队列包括在一家三级转诊医院接受 VA-ECMO 植入的 205 名所有患者(51%在复苏期间接受 VA-ECMO,43%有严重休克)。使用弹性网络方法中的惩罚逻辑回归开发了两种基于即时生物标志物的预测模型。验证队列从另一家独立的三级转诊医院招募。医院生存率的预测比较器是 SAVE 评分(接受者操作特征曲线下面积(AUC)为 0.686)、SAPS 评分(AUC 为 0.679)、APACHE 评分(AUC 为 0.662)和 SOFA 评分(AUC 为 0.732)在 6 小时幸存者中。6 小时 PREDICT VA-ECMO 评分(基于乳酸、pH 值和标准碳酸氢盐浓度)的 AUC 为 0.823,优于比较器评分。12 小时 PREDICT VA-ECMO 在 ECMO 插入后 1 小时、6 小时和 12 小时集成乳酸、pH 值和标准碳酸氢盐浓度,可实现更好的预后预测(AUC 为 0.839)。评分在外部验证队列中的表现良好(6 小时评分的 AUC 为 0.718,12 小时评分的 AUC 为 0.735)。
在需要 VA-ECMO 治疗的患者中,使用三种即时生物标志物的动态评分可高度可靠地预测医院死亡率。此外,PREDICT 评分是体外心肺复苏患者的第一个评分。