Predicting Early Seizures After Intracerebral Hemorrhage with Machine Learning.
Predicting Early Seizures After Intracerebral Hemorrhage with Machine Learning.
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DOI：
10.1007/s12028-022-01470-x
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发表时间：
2022-08
影响因子：
3.5
通讯作者：
中科院分区：
文献类型：
--
作者：
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. 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, while early seizure was the dependent variable. We attempted to improve upon the “CAV” model by adding anti-coagulant use, anti-platelet use, Glasgow Coma Scale, international normalized ratio, and systolic blood pressure (“CAV+”). For each model we utilized logistic regression, lasso regression (regularized), support vector machines, boosted trees (Xgboost), and Random Forest models. Final model performance was reported as the area under the curve using receiver operating characteristic models for the test data. Two large academic institutions. 864 survivors of ICH – 634 from Institution 1 and 230 from Institution 2. None. Early seizures were predicted similarly across the ML models by the CAV score in test data, (AUC 0.72, 95% CI 0.62–0.82). CAV+ had both the greatest and significantly improved performance for Xgboost compared to CAV (AUC 0.79, 95% CI 0.71–0.87, p=0.04 compared to CAV model AUC). Early seizures after ICH are predictable. Models utilizing cortical hematoma location, age less than 65 years, and hematoma volume greater than 10 mL had very good accuracy, 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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影响因子：
8.3
作者：
Hemphill, JC;Bonovich, DC;Johnston, SC
通讯作者：
Johnston, SC
影响因子：
8.3
作者：
Naidech, Andrew M.;Garg, Rajeev K.;Batjer, H. Hunt
通讯作者：
Batjer, H. Hunt
影响因子：
4.9
作者：
Sakamoto, Yuki;Koga, Masatosi;Toyoda, Kazunori
通讯作者：
Toyoda, Kazunori
影响因子：
9.9
作者：
Claassen, J.;Jette, N.;Hirsch, L. J.
通讯作者：
Hirsch, L. J.
影响因子：
9.9
作者：
De Herdt, V.;Dumont, F.;Cordonnier, C.
通讯作者：
Cordonnier, C.