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Original research
Preprocedural determination of an occlusion pathomechanism in endovascular treatment of acute stroke: a machine learning-based decision
  1. Jang-Hyun Baek1,2,
  2. Byung Moon Kim3,
  3. Dong Joon Kim3,
  4. Ji Hoe Heo2,
  5. Hyo Suk Nam2,
  6. Young Dae Kim2,
  7. Myung Ho Rho4,
  8. Pil-Wook Chung1,
  9. Yu Sam Won5,
  10. Yeongu Chung5
  1. 1Department of Neurology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
  2. 2Department of Neurology, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
  3. 3Interventional Neuroradiology, Department of Radiology, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
  4. 4Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
  5. 5Department of Neurosurgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
  1. Correspondence to Dr Byung Moon Kim, Department of Radiology, Yonsei University College of Medicine, Seodaemun-gu, Seoul, Korea (the Republic of); bmoon21{at}hanmail.net

Abstract

Objective To evaluate whether an occlusion pathomechanism can be accurately determined by common preprocedural findings through a machine learning-based prediction model (ML-PM).

Methods A total of 476 patients with acute stroke who underwent endovascular treatment were retrospectively included to derive an ML-PM. For external validation, 152 patients from another tertiary stroke center were additionally included. An ML algorithm was trained to classify an occlusion pathomechanism into embolic or intracranial atherosclerosis. Various common preprocedural findings were entered into the model. Model performance was evaluated based on accuracy and area under the receiver operating characteristic curve (AUC). For practical utility, a decision flowchart was devised from an ML-PM with a few key preprocedural findings. Accuracy of the decision flowchart was validated internally and externally.

Results An ML-PM could determine an occlusion pathomechanism with an accuracy of 96.9% (AUC=0.95). In the model, CT angiography-determined occlusion type, atrial fibrillation, hyperdense artery sign, and occlusion location were top-ranked contributors. With these four findings only, an ML-PM had an accuracy of 93.8% (AUC=0.92). With a decision flowchart, an occlusion pathomechanism could be determined with an accuracy of 91.2% for the study cohort and 94.7% for the external validation cohort. The decision flowchart was more accurate than single preprocedural findings for determining an occlusion pathomechanism.

Conclusions An ML-PM could accurately determine an occlusion pathomechanism with common preprocedural findings. A decision flowchart consisting of the four most influential findings was clinically applicable and superior to single common preprocedural findings for determining an occlusion pathomechanism.

  • Atherosclerosis
  • CT Angiography
  • Embolic
  • Stroke
  • Thrombectomy

Data availability statement

Data are available upon reasonable request. The relevant anonymized data are available from the first author upon reasonable request.

Statistics from Altmetric.com

Data availability statement

Data are available upon reasonable request. The relevant anonymized data are available from the first author upon reasonable request.

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Footnotes

  • Contributors J-HB is responsible for the overall content as the guarantor. J-HB and BMK had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: J-HB. Acquisition of clinical data: J-HB, BMK, DJK, JHH, HSN, YDK, MHR, P-WC, YSW, and YC. Analysis and interpretation of data: J-HB. Drafting of the manuscript: J-HB. Critical revision of the manuscript for important intellectual content: J-HB and BMK. Statistical analysis: J-HB. Final approval of the version to be published: J-HB, BMK, DJK, JHH, HSN, YDK, MHR, P-WC, YSW, and YC.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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