Clinico-psychosocial prediction for seizure emergencies: A multicenter derivation-validation study

Area of Science:

• Neurology
• Public Health
• Healthcare Management

Background:

• Epilepsy affects millions globally, with recurrent seizures leading to significant healthcare burdens.
• Emergency hospitalizations for epilepsy are often preventable with timely interventions.
• Predictive models are needed to identify patients at high risk of seizure recurrence and hospitalization.

Purpose of the Study:

• To develop and validate a multidimensional predictive model for emergency hospitalization due to recurrent epileptic seizures.
• To improve patient outcomes and reduce healthcare system strain through early identification and intervention.
• To assess the predictive performance of clinical, clinico-social, and clinico-psychosocial factors.

Main Methods:

• Retrospective and prospective multicenter cohort studies (n=230 derivation, n=505 validation).
• Analysis of clinical, social, and psychological factors associated with seizure worsening.
• Development and validation of three predictive models: clinical, clinico-social, and clinico-psychosocial, using ROC analysis.

Main Results:

• Focal epilepsy, high seizure frequency, psychogenic non-epileptic seizures, mood disorders, and living alone predicted seizure worsening.
• The clinico-psychosocial model demonstrated superior predictive performance (AUC=0.96) compared to the clinical model (AUC=0.87).
• Key predictors in the best model included drug-resistant focal epilepsy, social isolation, and psychological distress.

Conclusions:

• The clinico-psychosocial model accurately predicts emergency hospitalization for recurrent epileptic seizures.
• This model aids clinicians in identifying high-risk individuals for targeted interventions.
• Early identification can potentially decrease seizure recurrence and associated social consequences.