Abstract
Background
Seizures are a harmful complication of acute intracerebral hemorrhage (ICH). “Early” seizures in the first week after ICH are a risk factor for deterioration, later seizures, and herniation. Ideally, seizure medications after ICH would only be administered to patients with a high likelihood to have seizures. We developed and validated machine learning (ML) models to predict early seizures after ICH.
Methods
We used two large datasets to train and then validate our models in an entirely independent test set. The first model (“CAV”) predicted early seizures from a subset of variables of the CAVE score (a prediction rule for later seizures)—cortical hematoma location, age less than 65 years, and hematoma volume greater than 10 mL—whereas early seizure was the dependent variable. We attempted to improve on the “CAV” model by adding anticoagulant use, antiplatelet use, Glasgow Coma Scale, international normalized ratio, and systolic blood pressure (“CAV + ”). For each model we used logistic regression, lasso regression, support vector machines, boosted trees (Xgboost), and random forest models. Final model performance was reported as the area under the receiver operating characteristic curve (AUC) using receiver operating characteristic models for the test data. The setting of the study was two large academic institutions: institution 1, 634 patients; institution 2, 230 patients. There were no interventions.
Results
Early seizures were predicted across the ML models by the CAV score in test data, (AUC 0.72, 95% confidence interval 0.62–0.82). The ML model that predicted early seizure better in the test data was Xgboost (AUC 0.79, 95% confidence interval 0.71–0.87, p = 0.04) compared with the CAV model AUC.
Conclusions
Early seizures after ICH are predictable. Models using cortical hematoma location, age less than 65 years, and hematoma volume greater than 10 mL had a good accuracy rate, and performance improved with more independent variables. Additional methods to predict seizures could improve patient selection for monitoring and prophylactic seizure medications.
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Data Availability
Anonymized data and associated documentation will be made available by request from any qualified investigator for the purposes of reproducing the analysis.
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Acknowledgements
J.M. is supported in part by T32 LM012203, R.F. is supported by a K23 NS101124, and A.M.N. is supported in part by R01 NS110779 and U01 NS110772.
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Bunney and Murphy performed analysis and wrote and edited the article. Colton, Wang, and Shin edited the article. Faigle supplied data and edited the article. Naidech provided data, performed analysis, and edited the article. The final manuscript was approved by all authors.
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Bunney, G., Murphy, J., Colton, K. et al. Predicting Early Seizures After Intracerebral Hemorrhage with Machine Learning. Neurocrit Care 37 (Suppl 2), 322–327 (2022). https://doi.org/10.1007/s12028-022-01470-x
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DOI: https://doi.org/10.1007/s12028-022-01470-x