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      Deep-learning model for predicting 30-day postoperative mortality

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          Abstract

          Postoperative mortality occurs in 1–2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. We sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. We constructed a multipath convolutional neural network model using patient characteristics, co-morbid conditions, preoperative laboratory values, and intraoperative numerical data from patients undergoing surgery with tracheal intubation at a single medical centre. Data for 60 min prior to a randomly selected time point were utilised. Model performance was compared with a deep neural network, a random forest, a support vector machine, and a logistic regression using predetermined summary statistics of intraoperative data. Of 95 907 patients, 941 (1%) died within 30 days. The multipath convolutional neural network predicted postoperative 30-day mortality with an area under the receiver operating characteristic curve of 0.867 (95% confidence interval [CI]: 0.835–0.899). This was higher than that for the deep neural network (0.825; 95% CI: 0.790–0.860), random forest (0.848; 95% CI: 0.815–0.882), support vector machine (0.836; 95% CI: 0.802–870), and logistic regression (0.837; 95% CI: 0.803–0.871). A deep-learning time-series model improves prediction compared with models with simple summaries of intraoperative data. We have created a model that can be used in real time to detect dynamic changes in a patient's risk for postoperative mortality.

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          Author and article information

          Journal
          British Journal of Anaesthesia
          British Journal of Anaesthesia
          Elsevier BV
          00070912
          September 2019
          September 2019
          Article
          10.1016/j.bja.2019.07.025
          6993109
          31558311
          09cbd63c-907f-42a9-ba1d-5920d4e6a332
          © 2019

          https://www.elsevier.com/tdm/userlicense/1.0/

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