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.
- CT Angiography
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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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.
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