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Kostev K, Wu T, Wang Y, et al. Predicting the risk of stroke in patients with late-onset epilepsy: A machine learning approach. Epilepsy Behav. 2021;122:108211doi: 10.1016/j.yebeh.2021.108211.
Kostev, K., Wu, T., Wang, Y., Chaudhuri, K., & Tanislav, C. (2021). Predicting the risk of stroke in patients with late-onset epilepsy: A machine learning approach. Epilepsy & behavior : E&B, 122108211. https://doi.org/10.1016/j.yebeh.2021.108211
Kostev, Karel, et al. "Predicting the risk of stroke in patients with late-onset epilepsy: A machine learning approach." Epilepsy & behavior : E&B vol. 122 (2021): 108211. doi: https://doi.org/10.1016/j.yebeh.2021.108211
Kostev K, Wu T, Wang Y, Chaudhuri K, Tanislav C. Predicting the risk of stroke in patients with late-onset epilepsy: A machine learning approach. Epilepsy Behav. 2021 Sep;122:108211. doi: 10.1016/j.yebeh.2021.108211. Epub 2021 Jul 26. PMID: 34325155.
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Epilepsy Behav. 2021 Sep;122:108211. doi: 10.1016/j.yebeh.2021.108211. Epub 2021 Jul 26.

Predicting the risk of stroke in patients with late-onset epilepsy: A machine learning approach.

Epilepsy & behavior : E&B

Karel Kostev, Tong Wu, Yue Wang, Kal Chaudhuri, Christian Tanislav

Affiliations

  1. Epidemiology, IQVIA, Frankfurt am Main, Germany. Electronic address: [email protected].
  2. Advanced Analytics, IQVIA, Plymouth Meeting, USA.
  3. Global Consulting, IQVIA, Plymouth Meeting, PA, USA.
  4. Department of Geriatrics and Neurology, Diakonie Hospital Jung Stilling, Siegen, Germany.

PMID: 34325155 DOI: 10.1016/j.yebeh.2021.108211

Abstract

BACKGROUND: The goal of this cohort study was to estimate the predictors for ischemic stroke in patients with epilepsy in a large database containing data from general practitioners in Germany using machine learning methods.

METHODS: This retrospective cohort study included 11,466 patients aged ≥ 60 years with an initial diagnosis of epilepsy in 1182 general practices in Germany between January 2010 and December 2018 from the IQVIA Disease Analyzer database. The Sub-Population Optimization and Modeling Solutions (SOMS) tool was used to identify subgroups at a higher risk of stroke than the overall population with epilepsy based on 37 different variables.

RESULTS: A total of seven variables were considered important. Four co-diagnoses (diabetes, hypertension, heart failure, and alcohol dependence) were by far the strongest predictors with a combined predictive ability of more than 90%, whereby diabetes (41.4%) was the strongest predictor, followed by hypertension (35.0%) and heart failure (11.8%). The predictive importance of male gender was only 1.5%, and age was not recognized as an important predictor. Finally, the prescribed AEDs levetiracetam, with a predictive importance of 5.0%, and valproate, with 2.7%, were found to be weak predictors.

CONCLUSION: The stroke risk in patients with epilepsy was relatively high and could be predicted based on comorbidities such as diabetes mellitus, hypertension, heart failure, and alcohol dependence. Knowing and addressing these factors may help reduce the risk of stroke in patients with epilepsy.

Copyright © 2021 Elsevier Inc. All rights reserved.

Keywords: Epilepsy; Machine learning; Prediction analytics; Stroke

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