Predicting Clinical Outcomes of Large Vessel Occlusion before Mechanical Thrombectomy Using Machine Learning

Hidehisa Nishi, Naoya Oishi, Akira Ishii, Isao Ono, Takenori Ogura, Tadashi Sunohara, Hideo Chihara, Ryu Fukumitsu, Masakazu Okawa, Norikazu Yamana, Hirotoshi Imamura, Nobutake Sadamasa, Taketo Hatano, Ichiro Nakahara, Nobuyuki Sakai, Susumu Miyamoto

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background and Purpose - The clinical course of acute ischemic stroke with large vessel occlusion (LVO) is a multifactorial process with various prognostic factors. We aimed to model this process with machine learning and predict the long-term clinical outcome of LVO before endovascular treatment and to compare our method with previously developed pretreatment scoring methods. Methods - The derivation cohort included 387 LVO patients, and the external validation cohort included 115 LVO patients with anterior circulation who were treated with mechanical thrombectomy. The statistical model with logistic regression without regularization and machine learning algorithms, such as regularized logistic regression, linear support vector machine, and random forest, were used to predict good clinical outcome (modified Rankin Scale score of 0-2 at 90 days) with standard and multiple pretreatment clinical variables. Five previously reported pretreatment scoring methods (the Pittsburgh Response to Endovascular Therapy score, the Stroke Prognostication Using Age and National Institutes of Health Stroke Scale index, the Totaled Health Risks in Vascular Events score, the Houston Intra-Arterial Therapy score, and the Houston Intra-Arterial Therapy 2 score) were compared with these models for the area under the receiver operating characteristic curve. Results - The area under the receiver operating characteristic curve of random forest, which was the worst among the machine learning algorithms, was significantly higher than those of the standard statistical model and the best model among the previously reported pretreatment scoring methods in the derivation (the area under the receiver operating characteristic curve were 0.85±0.07 for random forest, 0.78±0.08 for logistic regression without regularization, and 0.77±0.09 for Stroke Prognostication using Age and National Institutes of Health Stroke Scale) and validation cohorts (the area under the receiver operating characteristic curve were 0.87±0.01 for random forest, 0.56±0.07 for logistic regression without regularization, and 0.83±0.00 for Pittsburgh Response to Endovascular Therapy). Conclusions - Machine learning methods with multiple pretreatment clinical variables can predict clinical outcomes of patients with anterior circulation LVO who undergo mechanical thrombectomy more accurately than previously developed pretreatment scoring methods.

Original languageEnglish
Pages (from-to)2379-2388
Number of pages10
JournalStroke
Volume50
Issue number9
DOIs
Publication statusPublished - 01-09-2019

All Science Journal Classification (ASJC) codes

  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine
  • Advanced and Specialised Nursing

Fingerprint Dive into the research topics of 'Predicting Clinical Outcomes of Large Vessel Occlusion before Mechanical Thrombectomy Using Machine Learning'. Together they form a unique fingerprint.

  • Cite this

    Nishi, H., Oishi, N., Ishii, A., Ono, I., Ogura, T., Sunohara, T., Chihara, H., Fukumitsu, R., Okawa, M., Yamana, N., Imamura, H., Sadamasa, N., Hatano, T., Nakahara, I., Sakai, N., & Miyamoto, S. (2019). Predicting Clinical Outcomes of Large Vessel Occlusion before Mechanical Thrombectomy Using Machine Learning. Stroke, 50(9), 2379-2388. https://doi.org/10.1161/STROKEAHA.119.025411