A nomogram model for predicting seizure control in status epilepticus: development and risk factor analysis
TL;DR
The proposed nomogram, constructed based on four independent risk factors, exhibits acceptable discriminative ability and clinical benefit and may facilitate the early and timely identification of high-risk patients.
Abstract
Objectives To explore the risk factors impacting control of seizures in status epilepticus (SE) and to construct a valid nomogram for predicting the prognosis. Methods In this retrospective study, we analyzed the patients with SE who were hospitalized in Fujian Medical University Union Hospital from January 2017 to December 2022. Two separate groups of data are created: one for model development and the other for model validation. Results A total of 272 patients with SE were enrolled in this study, of which 89 had poorly-controlled seizures at discharge. Six potential risk predictors were identified through LASSO regression. After validation by univariate logistic regression (p < 0.01) and multivariate logistic regression (p < 0.05), four of the predictors: the classification of SE, the seizure type, respiratory complications, and circulatory complications, were confirmed as independent risk factors affecting seizure control in SE patients. The AUC of the predictive model was 0.829, that of validation was 0.710. Conclusion The proposed nomogram, constructed based on four independent risk factors, exhibits acceptable discriminative ability and clinical benefit. The risk of poorly-controlled seizures in individual SE patients can be predicted through the nomogram. The model may facilitate the early and timely identification of high-risk patients.