Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality. (October 2018)

• Record Type: Journal Article
• Authors: Lee, Christine K.; Hofer, Ira; Gabel, Eilon; Baldi, Pierre; Cannesson, Maxime
• Is Part Of: Anesthesiology. Volume 129:Number 4(2018)
• Publication Date: 2018-10
• Subjects: Anesthesiology -- Periodicals; Anesthetics -- Periodicals; Anesthesia -- Periodicals; 617.9605
• DOI: 10.1097/ALN.0000000000002186
• Languages: English
• ISSNs: 0003-3022

Abstract

Editor's Perspective: What We Already Know about This Topic: Robust predictions are required to compare perioperative mortality among hospitals Deep neural network systems, a type of machine learning, can be used to develop highly nonlinear prediction models What This Article Tells Us That Is New: The authors' neural network model was comparable in accuracy to, but potentially more efficient at feature selection than logistic regression models Deep neural network–based machine learning provides an alternative to conventional multivariate regression Background: The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality. Methods: The data used to train and validate the algorithm consists of 59, 985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index. Results: In-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99). Conclusions: Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods. Abstract : The authors' neural network model was comparable in accuracy to, but potentially more efficient at feature selection than logistic regression models. Deep neural network–based machine learning provides an alternative to conventional multivariate regression.Supplemental Digital Content is available in the text.