Risk Factors for Acute Symptomatic Seizures in Patients with Viral Encephalitis and Construction of a Predictive Model

Shenhong LI; Li YANG; MengYuan XU; Zhao Li; XinTian WU; Rujiang ZHANG; Wei RONG

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Shenhong LI, Li YANG, MengYuan XU, Zhao Li, XinTian WU, Rujiang ZHANG, Wei RONG. Risk Factors for Acute Symptomatic Seizures in Patients with Viral Encephalitis and Construction of a Predictive Model[J]. Journal of Kunming Medical University.

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Risk Factors for Acute Symptomatic Seizures in Patients with Viral Encephalitis and Construction of a Predictive Model

1.
Kunming Medical University Haiyuan College，Kunming Yunnan 650106
2.
School of Basic Medical Sciences，Kunming Medical University，Kunming Yunnan 650500
3.
Neurology Department，Second Affiliated Hospital of Kunming Medical University，Kunming Yunnan 650032
4.
School of Nursing，Yunnan University of Chinese Medicine Kunming Yunnan 650500，China

Received Date: 2026-03-23

Abstract

Abstract

Objective To investigate the risk factors for acute symptomatic seizures （ASS） in patients with viral encephalitis （VE）, and to construct and validate a risk prediction model for early identification of high-risk patients. Methods A retrospective cohort study design was employed. Data from 249 VE patients admitted to a tertiary hospital in Yunnan Province between January 1, 2020, and December 31, 2023, who met the inclusion and exclusion criteria, were collected as the modeling cohort. The occurrence of ASS during hospitalization was set as the outcome indicator. Univariate logistic regression was used for initial screening of the modeling cohort. Subsequently, LASSO regression （least absolute shrinkage and selection operator） combined with 10-fold cross-validation was applied for variable selection. A multivariable logistic regression model was constructed using the selected variables, and a nomogram prediction model was developed based on this model. The discriminative ability, calibration, and clinical utility of the model were evaluated using the area under the receiver operating characteristic curve （AUC）, calibration curve, and decision curve analysis. Additionally, an external validation was performed using 60 VE patients from another tertiary hospital in Yunnan Province between January 1, 2024, and September 1, 2024. Results Multivariate analysis showed that decreased serum sodium concentration （OR = 0.883, 95%CI: 0.789～0.989）, frontal or temporal lobe involvement （OR = 2.387, 95%CI: 1.138～5.007）, moderate to severe EEG abnormalities （moderate: OR = 6.179, 95%CI: 2.030～18.806; severe: OR = 7.875, 95%CI: 1.081～57.393）, and moderate to severe disturbances of consciousness （GCS≤12; moderate: OR = 4.282, 95%CI: 1.482～12.378; severe: OR = 6.228, 95%CI: 1.099～35.289） were independent risk factors for ASS in VE patients. The predictive model constructed based on these factors showed an AUC of 0.836 （95%CI: 0.782～0.891, sensitivity 0.886, specificity 0.565） in the modeling cohort and an AUC of 0.784 （95%CI: 0.665～0.904, sensitivity 0.848, specificity 0.711） in the validation cohort. The Brier score was 0.145 and calibration slope was 1.023 in the modeling cohort; Brier score was 0.172 and calibration slope was 0.912 in the validation cohort. Decision curve analysis demonstrated that within the threshold probability range of 0.1–0.6, the net benefit provided by the model exceeded both the "treat all" and "treat none" strategies. When the threshold probability ranged from 0.15 to 0.40, the model's net benefit advantage was most pronounced, providing higher clinical decision-making value. Conclusion This study successfully established and validated a risk prediction model for ASS following VE that incorporates four readily obtainable clinical indicators. The model demonstrates predictive capability and provides an intuitive, quantitative assessment tool for early clinical identification of high-risk patients and implementation of stratified management and intervention.
- Viral encephalitis,
- Acute symptomatic seizure,
- Risk factors,
- Nomogram model

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References(24)

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