Morisson Louis, Duceau Baptiste, Do Rego Hermann, Lancelot Aymeric, Hariri Geoffroy, Charfeddine Ahmed, Laferrière-Langlois Pascal, Richebé Philippe, Lebreton Guillaume, Provenchère Sophie, Bouglé Adrien
Department of Anesthesiology and Critical Care Medicine, La Pitié-Salpêtrière Hospital, Paris, France. Sorbonne University, GRC 29, AP-HP, DMU DREAM.
Department of Anesthesiology and Critical Care Medicine, La Pitié-Salpêtrière Hospital, Paris, France. Sorbonne University, GRC 29, AP-HP, DMU DREAM.
Anaesth Crit Care Pain Med. 2023 Feb;42(1):101172. doi: 10.1016/j.accpm.2022.101172. Epub 2022 Nov 11.
Post-cardiotomy low cardiac output syndrome (PC-LCOS) is a life-threatening complication after cardiac surgery involving a cardiopulmonary bypass (CPB). Mechanical circulatory support with veno-arterial membrane oxygenation (VA-ECMO) may be necessary in the case of refractory shock. The objective of the study was to develop a machine-learning algorithm to predict the need for VA-ECMO implantation in patients with PC-LCOS.
Patients were included in the study with moderate to severe PC-LCOS (defined by a vasoactive inotropic score (VIS) > 10 with clinical or biological markers of impaired organ perfusion or need for mechanical circulatory support after cardiac surgery) from two university hospitals in Paris, France. The Deep Super Learner, an ensemble machine learning algorithm, was trained to predict VA-ECMO implantation using features readily available at the end of a CPB. Feature importance was estimated using Shapley values.
Between January 2016 and December 2019, 285 patients were included in the development dataset and 190 patients in the external validation dataset. The primary outcome, the need for VA-ECMO implantation, occurred respectively, in 16% (n = 46) and 10% (n = 19) in the development and the external validation datasets. The Deep Super Learner algorithm achieved a 0.863 (0.793-0.928) ROC AUC to predict the primary outcome in the external validation dataset. The most important features were the first postoperative arterial lactate value, intraoperative VIS, the absence of angiotensin-converting enzyme treatment, body mass index, and EuroSCORE II.
We developed an explainable ensemble machine learning algorithm that could help clinicians predict the risk of deterioration and the need for VA-ECMO implantation in moderate to severe PC-LCOS patients.
心脏切开术后低心排血量综合征（PC-LCOS）是心脏手术（包括体外循环（CPB））后一种危及生命的并发症。在难治性休克的情况下，可能需要采用静脉-动脉体外膜肺氧合（VA-ECMO）进行机械循环支持。本研究的目的是开发一种机器学习算法，以预测PC-LCOS患者是否需要植入VA-ECMO。
纳入了来自法国巴黎两家大学医院的中度至重度PC-LCOS患者（定义为血管活性药物评分（VIS）>10，并伴有器官灌注受损的临床或生物学标志物，或心脏手术后需要机械循环支持）。深度超级学习器是一种集成机器学习算法，通过使用CPB结束时易于获得的特征进行训练，以预测VA-ECMO植入情况。使用Shapley值估计特征重要性。
在2016年1月至2019年12月期间，开发数据集中纳入了285例患者，外部验证数据集中纳入了190例患者。主要结局，即VA-ECMO植入需求，在开发数据集和外部验证数据集中分别有16%（n = 46）和10%（n = 19）的患者出现。深度超级学习器算法在外部验证数据集中预测主要结局的ROC曲线下面积（AUC）为0.863（0.793 - 0.928）。最重要的特征是术后首次动脉乳酸值、术中VIS、未接受血管紧张素转换酶治疗、体重指数和欧洲心脏手术风险评估系统（EuroSCORE）II。
我们开发了一种可解释的集成机器学习算法，该算法可帮助临床医生预测中度至重度PC-LCOS患者病情恶化的风险以及VA-ECMO植入需求。
