gms | German Medical Science

68th Annual Meeting of the German Society of Neurosurgery (DGNC)
7th Joint Meeting with the British Neurosurgical Society (SBNS)

German Society of Neurosurgery (DGNC)

14 - 17 May 2017, Magdeburg

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Predicting functional outcome after aneurysmal subarachnoid hemorrhage using machine learning

Meeting Abstract

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  • Christian Rubbert - Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Deutschland
  • Kerim Beseoglu - Klinik für Neurochirurgie, Universitätsklinik Düsseldorf, Düsseldorf, Deutschland
  • Christian Mathys - Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Deutschland
  • Rebecca May - Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Deutschland
  • Benjamin Sigl - Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Deutschland
  • Bernd Turowski - Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Deutschland
  • Julian Caspers - Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Deutschland

Deutsche Gesellschaft für Neurochirurgie. Society of British Neurological Surgeons. 68. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), 7. Joint Meeting mit der Society of British Neurological Surgeons (SBNS). Magdeburg, 14.-17.05.2017. Düsseldorf: German Medical Science GMS Publishing House; 2017. DocDI.02.01

doi: 10.3205/17dgnc184, urn:nbn:de:0183-17dgnc1844

Published: June 9, 2017

© 2017 Rubbert et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.

Outline

Text

Objective: The pathogenesis leading to poor functional outcome after aneursysmal subarachnoidal hemorrhage (aSAH) is multifactorial and not fully understood. Microcirculatory dysfunction, assumed to play a key role, can be indirectly quantified with CT perfusion (CTP) imaging. We evaluated a machine learning approach, based on easily determinable features on admission, including CTP, to predict functional outcome.

Methods: Out of 614 consecutive subarachnoid hemorrhage patients (2009-2015), 351 were included (aSAH, ≥1 CTP within 72h of ictus and documented modified Rankin scale (mRS) scores after 6 months; n=5 excluded due to corrupt CTP datasets). The dataset was split in a training (n=264) and test dataset (n=87). A Random Forest model was trained on age, sex, WFNS and modified Fisher scores on admission, bihemispheric MTT and rCBF maximum, mean, maximum-mean and standard deviation (sd) to predict dichotomized mRS (≤2; >2). Feature importance was derived from the trained model. The model performance was evaluated on the test dataset and Receiver-Operator-Characteristic (ROC) analysis was performed.

Results: Sensitivity and specificity when predicting a mRS score >2 in the test dataset were 68.8% and 81.8%, respectively. Accuracy was 77.0%. Positive and negative predictive value was 68.8% and 81.8%, respectively. The area under the ROC curve was 0.83. MTTSD, WFNS and age were found to be most important.

Conclusion: A Random Forest model trained on easily determinable features on admission can predict functional outcome after aSAH with good accuracy, sensitivity and specificity. Our results underline the role of CTP. A novel finding is the importance of MTTSD.