Silva Stein, Ait Aissa Dalinda, Cocquet Pierre, Hoarau Lucille, Ruiz Jean, Ferre Fabrice, Rousset David, Mora Michel, Mari Arnaud, Fourcade Olivier, Riu Béatrice, Jaber Samir, Bataille Bénoît
From the Critical Care Unit (S.S., D.A.A., L.H., F.F., D.R., A.M., B.R.) and Critical Care and Anaesthesiology Department (S.S., D.A.A., L.H., J.R., F.F., D.R., A.M., O.F., B.R.), University Teaching Hospital of Purpan, Toulouse, France; French National Institute of Health and Medical Research U1214, University Teaching Hospital of Purpan, Toulouse, France (S.S.); Critical Care Unit, Hopital Dieu Hospital, Narbonne, France (P.C., M.M., B.B.); Critical Care Unit, University Cancer Institute Hospital of Toulouse, France (J.R.); and Intensive Care Unit and Transplantation, Department of Anaesthesiology and Critical Care B, Saint Eloi Hospital, Montpellier, France (S.J.).
Anesthesiology. 2017 Oct;127(4):666-674. doi: 10.1097/ALN.0000000000001773.
Recent studies suggest that isolated sonographic assessment of the respiratory, cardiac, or neuromuscular functions in mechanically ventilated patients may assist in identifying patients at risk of postextubation distress. The aim of the present study was to prospectively investigate the value of an integrated thoracic ultrasound evaluation, encompassing bedside respiratory, cardiac, and diaphragm sonographic data in predicting postextubation distress.
Longitudinal ultrasound data from 136 patients who were extubated after passing a trial of pressure support ventilation were measured immediately after the start and at the end of this trial. In case of postextubation distress (31 of 136 patients), an additional combined ultrasound assessment was performed while the patient was still in acute respiratory failure. We applied machine-learning methods to improve the accuracy of the related predictive assessments.
Overall, integrated thoracic ultrasound models accurately predict postextubation distress when applied to thoracic ultrasound data immediately recorded before the start and at the end of the trial of pressure support ventilation (learning sample area under the curve: start, 0.921; end, 0.951; test sample area under the curve: start, 0.972; end, 0.920). Among integrated thoracic ultrasound data, the recognition of lung interstitial edema and the increased telediastolic left ventricular pressure were the most relevant predictive factors. In addition, the use of thoracic ultrasound appeared to be highly accurate in identifying the causes of postextubation distress.
The decision to attempt extubation could be significantly assisted by an integrative, dynamic, and fully bedside ultrasonographic assessment of cardiac, lung, and diaphragm functions.
最近的研究表明，对机械通气患者的呼吸、心脏或神经肌肉功能进行单独的超声评估，可能有助于识别拔管后出现不适的风险患者。本研究的目的是前瞻性地研究综合胸部超声评估的价值，该评估包括床边呼吸、心脏和膈肌超声数据，以预测拔管后不适。
对136例在压力支持通气试验通过后拔管的患者，在该试验开始时和结束后立即测量其纵向超声数据。对于拔管后出现不适的患者（136例中的31例），在患者仍处于急性呼吸衰竭时进行了额外的联合超声评估。我们应用机器学习方法来提高相关预测评估的准确性。
总体而言，当将综合胸部超声模型应用于压力支持通气试验开始前和结束时立即记录的胸部超声数据时，能够准确预测拔管后不适（学习样本曲线下面积：开始时为0.921；结束时为0.951；测试样本曲线下面积：开始时为0.972；结束时为0.920）。在综合胸部超声数据中，肺间质水肿的识别和舒张末期左心室压力的增加是最相关的预测因素。此外，胸部超声的应用在识别拔管后不适的原因方面似乎高度准确。
对心脏、肺和膈肌功能进行综合、动态且完全床边超声评估，可显著辅助做出拔管尝试的决策。
