Wise Eric Stephen, Stonko David P, Glaser Zachary A, Garcia Kelly L, Huang Jennifer J, Kim Justine S, Kallos Justiss A, Starnes Joseph R, Fleming Jacob W, Hocking Kyle M, Brophy Colleen M, Eagle Susan S
Department of Surgery, Vanderbilt University, Nashville, TN, USA.
Vanderbilt University School of Medicine, Nashville, TN, USA.
Heart Surg Forum. 2017 Feb 24;20(1):E007-E014. doi: 10.1532/hsf.1566.
The need for mechanical ventilation 24 hours after coronary artery bypass grafting (CABG) is considered a morbidity by the Society of Thoracic Surgeons. The purpose of this investigation was twofold: to identify simple preoperative patient factors independently associated with prolonged ventilation and to optimize prediction and early identification of patients prone to prolonged ventilation using an artificial neural network (ANN).
Using the institutional Adult Cardiac Database, 738 patients who underwent CABG since 2005 were reviewed for preoperative factors independently associated with prolonged postoperative ventilation. Prediction of prolonged ventilation from the identified variables was modeled using both "traditional" multiple logistic regression and an ANN. The two models were compared using Pearson r2 and area under the curve (AUC) parameters.
Of 738 included patients, 14% (104/738) required mechanical ventilation ≥ 24 hours postoperatively. Upon multivariate analysis, higher body-mass index (BMI; odds ratio [OR] 1.10 per unit, P < 0.001), lower ejection fraction (OR 0.97 per %, P = 0.01) and use of cardiopulmonary bypass (OR 2.59, P = 0.02) were independently predictive of prolonged ventilation. The Pearson r2 and AUC of the multivariate nominal logistic regression model were 0.086 and 0.698 ± 0.05, respectively; analogous statistics of the ANN model were 0.159 and 0.732 ± 0.05, respectively.BMI, ejection fraction and cardiopulmonary bypass represent three simple factors that may predict prolonged ventilation after CABG. Early identification of these patients can be optimized using an ANN, an emerging paradigm for clinical outcomes modeling that may consider complex relationships among these variables.
冠状动脉旁路移植术（CABG）后24小时需要机械通气被胸外科医师协会视为一种发病率。本研究的目的有两个：确定与长时间通气独立相关的简单术前患者因素，并使用人工神经网络（ANN）优化对易于长时间通气患者的预测和早期识别。
利用机构成人心脏数据库，对自2005年以来接受CABG的738例患者进行回顾，以确定与术后长时间通气独立相关的术前因素。使用“传统”多元逻辑回归和人工神经网络对从确定变量预测长时间通气进行建模。使用Pearson r2和曲线下面积（AUC）参数比较这两种模型。
在纳入的738例患者中，14%（104/738）术后需要机械通气≥24小时。多变量分析显示，较高的体重指数（BMI；每单位比值比[OR]为1.10，P<0.001）、较低的射血分数（每百分比OR为0.97，P=0.01）和使用体外循环（OR为2.59，P=0.02）可独立预测长时间通气。多元名义逻辑回归模型的Pearson r2和AUC分别为0.086和0.698±0.05；人工神经网络模型的类似统计值分别为0.159和0.732±0.05。BMI、射血分数和体外循环是可能预测CABG后长时间通气的三个简单因素。使用人工神经网络可优化对这些患者的早期识别，人工神经网络是一种新兴的临床结局建模范式，可考虑这些变量之间复杂的关系。
