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January 26, 2021; 96 (4) Article

Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage

View ORCID ProfileJude P.J. Savarraj, View ORCID ProfileGeorgene W. Hergenroeder, Liang Zhu, Tiffany Chang, Soojin Park, View ORCID ProfileMurad Megjhani, View ORCID ProfileFarhaan S. Vahidy, View ORCID ProfileZhongming Zhao, Ryan S. Kitagawa, View ORCID ProfileH. Alex Choi
First published November 12, 2020, DOI: https://doi.org/10.1212/WNL.0000000000011211
Jude P.J. Savarraj
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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Georgene W. Hergenroeder
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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Liang Zhu
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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Tiffany Chang
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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Soojin Park
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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Murad Megjhani
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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Farhaan S. Vahidy
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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Zhongming Zhao
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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Ryan S. Kitagawa
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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H. Alex Choi
From the Departments of Neurosurgery (J.P.J.S., G.W.H., T.C., R.S.K., A.C.), Internal Medicine (L.Z.), and Neurology (F.S.V.), McGovern Medical School, Center for Precision Health, School of Biomedical Informatics (Z.Z.), and Human Genetics Center, School of Public Health (Z.Z.), The University of Texas Health Science Center at Houston; and Department of Neurology (S.P., M.M.), Columbia University, NY.
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  • ORCID record for H. Alex Choi
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Citation
Machine Learning to Predict Delayed Cerebral Ischemia and Outcomes in Subarachnoid Hemorrhage
Jude P.J. Savarraj, Georgene W. Hergenroeder, Liang Zhu, Tiffany Chang, Soojin Park, Murad Megjhani, Farhaan S. Vahidy, Zhongming Zhao, Ryan S. Kitagawa, H. Alex Choi
Neurology Jan 2021, 96 (4) e553-e562; DOI: 10.1212/WNL.0000000000011211

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Abstract

Objective To determine whether machine learning (ML) algorithms can improve the prediction of delayed cerebral ischemia (DCI) and functional outcomes after subarachnoid hemorrhage (SAH).

Methods ML models and standard models (SMs) were trained to predict DCI and functional outcomes with data collected within 3 days of admission. Functional outcomes at discharge and at 3 months were quantified using the modified Rankin Scale (mRS) for neurologic disability (dichotomized as good [mRS ≤ 3] vs poor [mRS ≥ 4] outcomes). Concurrently, clinicians prospectively prognosticated 3-month outcomes of patients. The performance of ML, SMs, and clinicians were retrospectively compared.

Results DCI status, discharge, and 3-month outcomes were available for 399, 393, and 240 participants, respectively. Prospective clinician (an attending, a fellow, and a nurse) prognostication of 3-month outcomes was available for 90 participants. ML models yielded predictions with the following area under the receiver operating characteristic curve (AUC) scores: 0.75 ± 0.07 (95% confidence interval [CI] 0.64–0.84) for DCI, 0.85 ± 0.05 (95% CI 0.75–0.92) for discharge outcome, and 0.89 ± 0.03 (95% CI 0.81–0.94) for 3-month outcome. ML outperformed SMs, improving AUC by 0.20 (95% CI −0.02 to 0.4) for DCI, by 0.07 ± 0.03 (95% CI −0.0018 to 0.14) for discharge outcomes, and by 0.14 (95% CI 0.03–0.24) for 3-month outcomes and matched physician's performance in predicting 3-month outcomes.

Conclusion ML models significantly outperform SMs in predicting DCI and functional outcomes and has the potential to improve SAH management.

Glossary

ANN=
artificial neural network;
AUC=
area under the receiver operating characteristic curve;
CI=
confidence interval;
CV=
cross-validation;
DCI=
delayed cerebral ischemia;
EMR=
electronic medical record;
GB=
gradient boost;
HH=
Hunt-Hess scale;
IQR=
interquartile range;
IRB=
institutional review board;
IVH=
intraventricular hemorrhage;
LR=
logistic regression;
mFS=
modified Fisher Scale;
ML=
machine learning;
mRS=
modified Rankin Scale;
RF=
random forest;
ROC=
receiver operating characteristic;
SAH=
subarachnoid hemorrhage;
TCD=
transcranial Doppler;
WBC=
white blood cell

Footnotes

  • Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

  • Received August 26, 2019.
  • Accepted in final form September 21, 2020.
  • © 2020 American Academy of Neurology
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