ABSTRACT
Objective:To study the early-onset epilepsy of intracerebral hemorrhage and build a prediction model to evaluate its prediction efficiency.Methods:A cross-sectional investigation was conducted to construct a specialized optimized prediction model. The prediction model was converted into a visual optimized scoring scale, so as to quantify the probability of secondary epilepsy after intracerebral hemorrhage. Based on the current prediction model of acute cerebral infraction and post-stroke seizure (AIS-PSS), the evaluation efficacy of optimized score for secondary epilepsy after hemorrhagic stroke was explored.Results:① After sample size calculation and sufficient inclusion and exclusion, 159 patients with cerebral hemorrhage were continuously selected as the model group of this cross-sectional study. A total of 29 patients with early-onset epilepsy and 130 patients without secondary epilepsy were enrolled. The time span was from January 2021 to August 2021. In addition, 77 patients with acute cerebral hemorrhage from August 2021 to February 2022 were selected as the verification group, among which 12 patients had early-onset epilepsy and 65 patients had not any secondary epilepsy. ② There were significant differences in demographic characteristics such as diabetes history, cerebral infarction history, smoking history, National Institutes of Health Stroke Scale (NIHSS) score, intracerebral hemorrhage hematoma volume, serum creatinine (SCr), neuron-specific enolase (NSE), S-100 protein and intracerebral hemorrhage site between the two model groups with different prognosis (all P < 0.05). ③ The above indexes were included in univariate and multivariate Poisson regression analysis, and the results showed that the duration of diabetes [relative risk ( RR) = 1.229, 95% confidence interval (95% CI) was 1.065-1.896, P = 0.036], smoking history ( RR = 1.419, 95% CI was 1.133-2.160, P = 0.030), history of cerebral infarction ( RR = 1.634, 95% CI was 1.128-2.548, P = 0.041), hematoma volume of cerebral hemorrhage ( RR = 1.222, 95% CI was 1.024-2.052, P = 0.041), NES content ( RR = 1.146, 95% CI was 1.041-1.704, P = 0.032), were independent influencing factors to constitute the prediction model. The prediction model was converted into a visual optimized scoring scale in the form of a line diagram to obtain the prediction probability corresponding to the corresponding score. ④ Receiver operator characteristic curve (ROC curve) was used to test the evaluation efficiency of optimized score and AIS-PSS score for early-onset cerebral hemorrhage epilepsy. Relevant data of patients in the verification group were extracted according to the information of two scores, and the final score of each patient in the verification group was obtained. The score and prognosis were put into the ROC curve to evaluate the predictive ability of different prediction models. The results showed that the cut-off value of the optimized score and the AIS-PSS score were 144 points and 7 points, respectively, and the area under the ROC curve (AUC) and the Yoden index of the optimized score were slightly lower than the AIS-PSS score. However, compared with AIS-PSS score, there was no significant difference in the evaluation efficiency of optimized score for early-onset epilepsy ( Z = 1.874, P > 0.05). Conclusion:This study constructed a specific early-onset epilepsy prediction model for patients with hemorrhagic stroke, and transformed it into an optimized score that is easy for clinical use, and its evaluation efficiency is reliable.
ABSTRACT
Early-onset epilepsy is a neurological abnormality in childhood, and it is especially common in the first 2 years after birth. Seizures in early life mostly result from structural or metabolic disorders in the brain, and the genetic causes of idiopathic seizures have been extensively investigated. In this study, we identified four missense mutations in the SETD1A gene (SET domain-containing 1A, histone lysine methyltransferase): three de novo mutations in three individuals and one inherited mutation in a four-generation family. Whole-exome sequencing indicated that all four of these mutations were responsible for the seizures. Mutations of SETD1A have been implicated in schizophrenia and developmental disorders, so we examined the role of the four mutations (R913C, Q269R, G1369R, and R1392H) in neural development. We found that their expression in mouse primary cortical neurons affected excitatory synapse development. Moreover, expression of the R913C mutation also affected the migration of cortical neurons in the mouse brain. We further identified two common genes (Neurl4 and Usp39) affected by mutations of SETD1A. These results suggested that the mutations of SETD1A play a fundamental role in abnormal synaptic function and the development of neurons, so they may be pathogenic factors for neurodevelopmental disorders.