Kobayashi Yuta, Peng Yu-Chung, Yu Evan, Bush Brian, Jung Youn-Hoa, Murphy Zachary, Goeddel Lee, Whitman Glenn, Venkataraman Archana, Brown Charles H
Johns Hopkins University, Baltimore, MD, United States.
Department of Anesthesiology & Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States.
Front Med (Lausanne). 2023 Sep 14;10:1165912. doi: 10.3389/fmed.2023.1165912. eCollection 2023.
Although conventional prediction models for surgical patients often ignore intraoperative time-series data, deep learning approaches are well-suited to incorporate time-varying and non-linear data with complex interactions. Blood lactate concentration is one important clinical marker that can reflect the adequacy of systemic perfusion during cardiac surgery. During cardiac surgery and cardiopulmonary bypass, minute-level data is available on key parameters that affect perfusion. The goal of this study was to use machine learning and deep learning approaches to predict maximum blood lactate concentrations after cardiac surgery. We hypothesized that models using minute-level intraoperative data as inputs would have the best predictive performance.
Adults who underwent cardiac surgery with cardiopulmonary bypass were eligible. The primary outcome was maximum lactate concentration within 24 h postoperatively. We considered three classes of predictive models, using the performance metric of mean absolute error across testing folds: (1) static models using baseline preoperative variables, (2) augmentation of the static models with intraoperative statistics, and (3) a dynamic approach that integrates preoperative variables with intraoperative time series data.
2,187 patients were included. For three models that only used baseline characteristics (linear regression, random forest, artificial neural network) to predict maximum postoperative lactate concentration, the prediction error ranged from a median of 2.52 mmol/L (IQR 2.46, 2.56) to 2.58 mmol/L (IQR 2.54, 2.60). The inclusion of intraoperative summary statistics (including intraoperative lactate concentration) improved model performance, with the prediction error ranging from a median of 2.09 mmol/L (IQR 2.04, 2.14) to 2.12 mmol/L (IQR 2.06, 2.16). For two modelling approaches (recurrent neural network, transformer) that can utilize intraoperative time-series data, the lowest prediction error was obtained with a range of median 1.96 mmol/L (IQR 1.87, 2.05) to 1.97 mmol/L (IQR 1.92, 2.05). Intraoperative lactate concentration was the most important predictive feature based on Shapley additive values. Anemia and weight were also important predictors, but there was heterogeneity in the importance of other features.
Postoperative lactate concentrations can be predicted using baseline and intraoperative data with moderate accuracy. These results reflect the value of intraoperative data in the prediction of clinically relevant outcomes to guide perioperative management.
尽管针对外科手术患者的传统预测模型常常忽略术中的时间序列数据，但深度学习方法非常适合纳入具有复杂相互作用的时变和非线性数据。血乳酸浓度是一种重要的临床指标，可反映心脏手术期间全身灌注的充足程度。在心脏手术和体外循环期间，可获得影响灌注的关键参数的分钟级数据。本研究的目的是使用机器学习和深度学习方法预测心脏手术后的最大血乳酸浓度。我们假设，将分钟级术中数据作为输入的模型具有最佳预测性能。
接受体外循环心脏手术的成年患者符合条件。主要结局为术后24小时内的最大乳酸浓度。我们考虑了三类预测模型，使用各测试折的平均绝对误差作为性能指标：（1）使用术前基线变量的静态模型，（2）通过术中统计数据增强静态模型，以及（3）一种将术前变量与术中时间序列数据相结合的动态方法。
纳入2187例患者。对于仅使用基线特征（线性回归、随机森林、人工神经网络）来预测术后最大乳酸浓度的三种模型，预测误差范围为中位数2.52 mmol/L（四分位间距2.46，2.56）至2.58 mmol/L（四分位间距2.54，2.60）。纳入术中汇总统计数据（包括术中乳酸浓度）可改善模型性能，预测误差范围为中位数2.09 mmol/L（四分位间距2.04，2.14）至2.12 mmol/L（四分位间距2.06，2.16）。对于两种可利用术中时间序列数据的建模方法（循环神经网络、Transformer），获得的最低预测误差范围为中位数1.96 mmol/L（四分位间距1.87，2.05）至1.97 mmol/L（四分位间距1.92, 2.05）。根据Shapley加性值，术中乳酸浓度是最重要的预测特征。贫血和体重也是重要的预测因素，但其他特征的重要性存在异质性。
使用基线和术中数据可以中等准确度预测术后乳酸浓度。这些结果反映了术中数据在预测临床相关结局以指导围手术期管理方面的价值。
