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Risk factor analysis and establishment of a predictive model for epilepsy comorbid with depression
Yiming Sun1, Zhangyi Wu1, Liang Guo1
1Department of Neurosurgery, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China.
Plos One
|September 2, 2025
Summary
Epilepsy patients with hippocampal onset, frequent seizures, specific medications, and lower daily living scores are at higher risk for depression. A predictive model aids early detection and intervention for comorbid depression in epilepsy.
Area of Science:
- Neurology
- Psychiatry
- Medical Informatics
Background:
- Epilepsy is frequently comorbid with depression, impacting patient quality of life.
- Identifying risk factors for comorbid depression is crucial for timely intervention.
Purpose of the Study:
- To analyze risk factors for depression in epilepsy patients using clinical and Stereo Electroencephalography (SEEG) data.
- To establish a predictive model for comorbid depression in epilepsy.
Main Methods:
- Retrospective analysis of 152 epilepsy patients' clinical data.
- Univariate and Lasso regression analyses to identify independent risk factors.
- Development of a nomogram model for risk prediction.
Main Results:
- Independent risk factors identified: hippocampal epilepsy origin (SEEG), marital status, younger enrollment age, seizure frequency, specific anti-seizure medications, and lower ADL scores.
- The predictive model demonstrated strong discriminative ability (AUC = 0.895).
Conclusions:
- A nomogram model effectively predicts depression risk in epilepsy patients.
- Early identification of high-risk individuals enables proactive interventions.
- Highlights the link between neurological and psychological aspects in epilepsy management.
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