Risk factor analysis and establishment of a predictive model for epilepsy comorbid with depression Article Swipe
Yiming Sun
,
Zhenhua Wu
,
Liang Guo
,
Wei Wang
,
Xiaowen Ma
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0331441
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0331441
Objective This study aims to utilize our hospital’s existing Stereo Electroencephalography (SEEG) examination results combined with other clinical data to systematically analyze the risk factors for epilepsy comorbid with depression, and to establish a model for predicting the risk of developing depression in epilepsy patients. Clinically, this model can be used to predict the risk of comorbid depression in epilepsy patients, thereby enhancing the identification of this condition and providing a theoretical basis for proactive intervention in depressive symptoms among epilepsy patients. Methods A retrospective analysis was conducted on the clinical data of patients diagnosed with epilepsy in the Department of Neurosurgery at Tongde Hospital Of Zhejiang Province from 01/01/2020–31/12/2024, all of whom underwent Electroencephalography (EEG) examinations. According to the C-NDDI-E scores and clinical manifestations, the epilepsy patients were divided into an epilepsy with comorbid depression group (study group) and epilepsy without depression group (control group). Univariate analysis was performed using SPSS 26.0 software to screen for potential factors contributing to depression comorbid with epilepsy. Variables with a univariate P ≤ 0.05 were entered into a linear Lasso regression analysis. Those with statistical significance were then used to construct a nomogram model for predicting the risk of depression comorbid with epilepsy using R software. Results A total of 152 epilepsy patients were enrolled, including 43 in the study group and 109 in the control group. Univariate analysis showed statistically significant (P < 0.05) differences between the groups in terms of age, employment status, marital status, age of onset, frequency of epileptic seizures, type of drug treatment, scalp EEG-determined epileptogenic zone, SEEG-determined epileptogenic zone, and Activities of Daily Living (ADL) score. Lasso regression analysis revealed that marital status (p = 0.0008), Enrollment age (OR = 0.9152, P = 0.0003, 95% CI: 0.8673–0.9562), frequency of epileptic seizures (OR =5.9946, P = 0.0030, 95% CI: 1.8952–20.6541), type of drug treatment (OR = 44.4062, P = 0.0157, 95% CI: 1.3629–15.6702), SEEG results indicating the epileptogenic zone (hippocampal onset: OR = 12.3489, P = 0.0026, 95% CI: 2.5902–70.9811), and ADL score (OR = 0.9358, P = 0.0314, 95% CI: 0.8785–0.9930) were independent risk factors for depression comorbid with epilepsy. The area under the ROC curve (AUC) was 0.895, indicating strong discriminative ability and high predictive accuracy. Conclusion Independent risk factors for depression comorbid with epilepsy include: hippocampal origin of epilepsy as identified by SEEG, unstable marital status, younger age at the time of enrollment, higher frequency of epileptic seizures (>4 times/month), use of specific anti-seizure medications (such as topiramate, phenobarbital, levetiracetam, and perampanel), and lower activities of daily living (ADL) scores. The nomogram model established based on these factors performs well in relatively accurately predicting the risk of depression comorbid with epilepsy. This facilitates early identification of high-risk patients in clinical practice, enabling timely interventions to prevent the severe consequences of depressive episodes, improving patient adherence to epilepsy treatment, and emphasizing the link between psychological and neuroscientific aspects in epilepsy management to foster interdisciplinary collaboration for more comprehensive patient care.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0331441
- https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0331441&type=printable
- OA Status
- gold
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413928256
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413928256Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0331441Digital Object Identifier
- Title
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Risk factor analysis and establishment of a predictive model for epilepsy comorbid with depressionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-09-02Full publication date if available
- Authors
-
Yiming Sun, Zhenhua Wu, Liang Guo, Wei Wang, Xiaowen MaList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0331441Publisher landing page
- PDF URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0331441&type=printableDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0331441&type=printableDirect OA link when available
- Concepts
-
Epilepsy, Depression (economics), Univariate analysis, Medicine, Comorbidity, Risk factor, Nomogram, Marital status, Stereoelectroencephalography, Internal medicine, Epilepsy surgery, Psychiatry, Multivariate analysis, Population, Macroeconomics, Environmental health, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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39Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Enrollment | 280 |
| abstract_inverted_index.Univariate | 146, 225 |
| abstract_inverted_index.accurately | 438 |
| abstract_inverted_index.activities | 420 |
| abstract_inverted_index.depression | 41, 57, 135, 142, 161, 197, 350, 376, 443 |
| abstract_inverted_index.depressive | 77, 466 |
| abstract_inverted_index.developing | 40 |
| abstract_inverted_index.employment | 241 |
| abstract_inverted_index.identified | 386 |
| abstract_inverted_index.indicating | 318, 363 |
| abstract_inverted_index.management | 485 |
| abstract_inverted_index.predicting | 36, 193, 439 |
| abstract_inverted_index.predictive | 369 |
| abstract_inverted_index.regression | 178, 271 |
| abstract_inverted_index.relatively | 437 |
| abstract_inverted_index.treatment, | 255, 473 |
| abstract_inverted_index.univariate | 168 |
| abstract_inverted_index.Clinically, | 45 |
| abstract_inverted_index.Independent | 372 |
| abstract_inverted_index.depression, | 29 |
| abstract_inverted_index.differences | 233 |
| abstract_inverted_index.emphasizing | 475 |
| abstract_inverted_index.enrollment, | 398 |
| abstract_inverted_index.established | 429 |
| abstract_inverted_index.examination | 12 |
| abstract_inverted_index.facilitates | 448 |
| abstract_inverted_index.hippocampal | 381 |
| abstract_inverted_index.independent | 346 |
| abstract_inverted_index.medications | 410 |
| abstract_inverted_index.significant | 229 |
| abstract_inverted_index.statistical | 182 |
| abstract_inverted_index.theoretical | 71 |
| abstract_inverted_index.topiramate, | 413 |
| abstract_inverted_index.(hippocampal | 322 |
| abstract_inverted_index.Neurosurgery | 101 |
| abstract_inverted_index.anti-seizure | 409 |
| abstract_inverted_index.consequences | 464 |
| abstract_inverted_index.contributing | 159 |
| abstract_inverted_index.hospital’s | 7 |
| abstract_inverted_index.intervention | 75 |
| abstract_inverted_index.perampanel), | 417 |
| abstract_inverted_index.significance | 183 |
| abstract_inverted_index.collaboration | 489 |
| abstract_inverted_index.comprehensive | 492 |
| abstract_inverted_index.epileptogenic | 258, 261, 320 |
| abstract_inverted_index.examinations. | 116 |
| abstract_inverted_index.interventions | 459 |
| abstract_inverted_index.psychological | 479 |
| abstract_inverted_index.retrospective | 84 |
| abstract_inverted_index.statistically | 228 |
| abstract_inverted_index.times/month), | 405 |
| abstract_inverted_index.EEG-determined | 257 |
| abstract_inverted_index.discriminative | 365 |
| abstract_inverted_index.identification | 64, 450 |
| abstract_inverted_index.levetiracetam, | 415 |
| abstract_inverted_index.phenobarbital, | 414 |
| abstract_inverted_index.systematically | 20 |
| abstract_inverted_index.SEEG-determined | 260 |
| abstract_inverted_index.manifestations, | 124 |
| abstract_inverted_index.neuroscientific | 481 |
| abstract_inverted_index.0.8785–0.9930) | 344 |
| abstract_inverted_index.0.8673–0.9562), | 290 |
| abstract_inverted_index.interdisciplinary | 488 |
| abstract_inverted_index.1.3629–15.6702), | 315 |
| abstract_inverted_index.1.8952–20.6541), | 302 |
| abstract_inverted_index.2.5902–70.9811), | 332 |
| abstract_inverted_index.Electroencephalography | 10, 114 |
| abstract_inverted_index.01/01/2020–31/12/2024, | 109 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5101219151, https://openalex.org/A5118969723, https://openalex.org/A5102986738, https://openalex.org/A5038796794, https://openalex.org/A5100611600 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I4210097783, https://openalex.org/I4210114400 |
| citation_normalized_percentile.value | 0.47188065 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |