Abstract
Objective
Prediction of malignant middle cerebral artery infarction (MMI) could identify patients
for early intervention. We trained and internally validated a ML model that predicts
MMI following mechanical thrombectomy (MT) for ACLVO.
Methods
All patients who underwent MT for ACLVO between 2015 – 2021 at a single institution
were reviewed. Data was divided into 80% training and 20% test sets. 10 models were
evaluated on the training set. The top 3 models underwent hyperparameter tuning using
grid search with nested 5-fold CV to optimize the area under the receiver operating
curve (AUROC). Tuned models were evaluated on the test set and compared to logistic
regression.
Results
A total of 381 patients met the inclusion criteria. There were 50 (13.1%) patients
who developed MMI. Out of the 10 ML models screened on the training set, the top 3
performing were neural network (median AUROC 0.78, IQR 0.72 – 0.83), support vector
machine ([SVM] median AUROC 0.77, IQR 0.72 – 0.83), and random forest (median AUROC
0.75, IQR 0.68 – 0.81). On the test set, random forest (median AUROC 0.78, IQR 0.73
– 0.83) and neural network (median AUROC 0.78, IQR 0.73 – 0.83) were the top performing
models, followed by SVM (median AUROC 0.77, IQR 0.70 – 0.83). These scores were significantly
better than those for logistic regression (AUROC 0.72, IQR 0.66 – 0.78), individual
risk factors, and the Malignant Brain Edema score (p < 0.001 for all).
Conclusion
ML models predicted MMI with good discriminative ability. They outperformed standard
statistical techniques and individual risk factors.
Keywords
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Article info
Publication history
Published online: January 16, 2023
Accepted:
January 9,
2023
Received in revised form:
January 8,
2023
Received:
November 12,
2022
Identification
DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2023.106989
Copyright
© 2023 Elsevier Inc. All rights reserved.
