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Machine Learning Analysis of Matricellular Proteins and Clinical Variables for Early Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

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Abstract

Although delayed cerebral ischemia (DCI) is a well-known complication after subarachnoid hemorrhage (SAH), there are no reliable biomarkers to predict DCI development. Matricellular proteins (MCPs) have been reported relevant to DCI and expected to become biomarkers. As machine learning (ML) enables the classification of various input data and the result prediction, the aim of this study was to construct early prediction models of DCI development with clinical variables and MCPs using ML analyses. Early-stage clinical data of 95 SAH patients in a prospective cohort were analyzed and applied to a ML algorithm, random forest, to construct three prediction models: (1) a model with only clinical variables on admission, (2) a model with only plasma levels of MCP (periostin, osteopontin, and galectin-3) at post-onset days 1–3, and (3) a model with both clinical variables on admission and MCP values at days 1–3. The prediction accuracy of the development of DCI, angiographic vasospasm, or cerebral infarction and the importance of each feature were computed. The prediction accuracy of DCI development was 93.9% in model 1, 87.2% in model 2, and 95.1% in model 3, but that of angiographic vasospasm or cerebral infarction was lower. The three most important features in model 3 for DCI were periostin, osteopontin, and galectin-3, followed by aneurysm location. All of the early-stage prediction models of DCI development constructed by ML worked with high accuracy and sensitivity. One-time early-stage measurement of plasma MCPs served for reliable prediction of DCI development, suggesting their potential utility as biomarkers.

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Acknowledgements

We thank Dr. Nobuhisa Kashiwagi (the Institute of Statistical Mathematics, Tokyo, Japan) for review of statistical analyses.

Funding

This work was supported by a grant-in-aid for Scientific Research from Japan Society for the Promotion of Science to Dr. HS (grant number 17K10825).

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Authors and Affiliations

  1. Department of Neurosurgery, Mie Chuo Medical Center, 2158-5 Myojin-cho, Hisai, Tsu, Mie, 514-1101, Japan

    Satoru Tanioka, Fujimaro Ishida & Fumi Nakano

  2. Department of Neurosurgery, Mie University Graduate School of Medicine, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan

    Fumihiro Kawakita, Hideki Kanamaru & Hidenori Suzuki

  3. Department of Neurosurgery, Suzuka Kaisei Hospital, 112-1 Ko-cho, Suzuka, Mie, 513-8505, Japan

    Yoshinari Nakatsuka & Hirofumi Nishikawa

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  1. Satoru Tanioka
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Correspondence to Satoru Tanioka.

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Tanioka, S., Ishida, F., Nakano, F. et al. Machine Learning Analysis of Matricellular Proteins and Clinical Variables for Early Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage. Mol Neurobiol 56, 7128–7135 (2019). https://doi.org/10.1007/s12035-019-1601-7

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