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Outcome Prediction in Postanoxic Coma With Deep Learning

Tjepkema-Cloostermans, Marleen C., PhD; da Silva Lourenço, Catarina, BSc2,3; Ruijter, Barry J., MD, PhD2; Tromp, Selma C., MD, PhD4; Drost, Gea, MD, PhD5; Kornips, Francois H. M., MD6; Beishuizen, Albertus, MD, PhD7; Bosch, Frank H., MD, PhD8; Hofmeijer, Jeannette, MD, PhD2,9; van Putten, Michel J. A. M., MD, PhD1,2

doi: 10.1097/CCM.0000000000003854
Clinical Investigation: PDF Only

Objectives: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve similar or even better performance, being more objective and consistent.

Design: Prospective cohort study.

Setting: Medical ICU of five teaching hospitals in the Netherlands.

Patients: Eight-hundred ninety-five consecutive comatose patients after cardiac arrest.

Interventions: None.

Measurements and Main Results: Continuous electroencephalogram was recorded during the first 3 days after cardiac arrest. Functional outcome at 6 months was classified as good (Cerebral Performance Category 1–2) or poor (Cerebral Performance Category 3–5). We trained a convolutional neural network, with a VGG architecture (introduced by the Oxford Visual Geometry Group), to predict neurologic outcome at 12 and 24 hours after cardiac arrest using electroencephalogram epochs and outcome labels as inputs. Output of the network was the probability of good outcome. Data from two hospitals were used for training and internal validation (n = 661). Eighty percent of these data was used for training and cross-validation, the remaining 20% for independent internal validation. Data from the other three hospitals were used for external validation (n = 234). Prediction of poor outcome was most accurate at 12 hours, with a sensitivity in the external validation set of 58% (95% CI, 51–65%) at false positive rate of 0% (CI, 0–7%). Good outcome could be predicted at 12 hours with a sensitivity of 48% (CI, 45–51%) at a false positive rate of 5% (CI, 0–15%) in the external validation set.

Conclusions: Deep learning of electroencephalogram signals outperforms any previously reported outcome predictor of coma after cardiac arrest, including visual electroencephalogram assessment by trained electroencephalogram experts. Our approach offers the potential for objective and real time, bedside insight in the neurologic prognosis of comatose patients after cardiac arrest.

1Department of Clinical Neurophysiology and Neurology, Medisch Spectrum Twente, Enschede, The Netherlands.

2Department of Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands.

3Biomedical Engineering, Universidade do Porto, Porto, Portugal.

4Department of Clinical Neurophysiology, St. Antonius Hospital, Nieuwegein, The Netherlands.

5Department of Neurology and Neurosurgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

6Department of Neurology, VieCuri Medical Center, Venlo, The Netherlands.

7Intensive Care Center, Medisch Spectrum Twente, Enschede, The Netherlands.

8Department of Intensive Care, Rijnstate hospital, Arnhem, The Netherlands.

9Department of Neurology, Rijnstate hospital, Arnhem, The Netherlands.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal’s website (http:/

Dr. van Putten is co-founder of Clinical Science Systems, a supplier of electroencephalogram systems for Medisch Spectrum Twente. The remaining authors have disclosed that they do not have any conflicts of interest.

This work was performed in Medisch Spectrum Twente, Rijnstate Hospital, St. Antonius Hospital, University Medical Center Groningen and VieCuri Medical Center, The Netherlands.

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