Factors predictive of late remission in a cohort of Chinese patients with newly diagnosed epilepsy

Seizure Remission and Its Predictors Among Epileptic Patients on Follow-Up at Public Hospitals in Eastern Ethiopia: A Retrospective Cohort Study

Background

Epilepsy is one of the common chronic neurological disorders with varying therapeutic responses. Despite the high prevalence of epilepsy and the significant treatment gaps in developing nations, such as Ethiopia, there is a dearth of data on seizure remission and its predictors in Eastern Ethiopia in particular.

Objective

This study aimed to determine seizure remission and its predictors among epileptic patients on follow-up in Hiwot Fana Specialized University Hospital (HFSUH) and Dilchora Referral Hospital (DCRH), from July 2 to 31, 2021.

Methods

A retrospective cohort study was conducted among 418 newly diagnosed epilepsy patients receiving anti-seizure drugs (ASDs) between July 1, 2014, and July 31, 2019, in two public hospitals in Eastern Ethiopia. Relevant data were collected for all patients with a minimum follow-up period of two years. Data were analyzed using SPSS Version 21. Cox proportional hazards model was performed to identify predictors of seizure remission.

Results

Overall, 252 (60.3%) of the study participants have achieved seizure remission for at least one year. The mean time to achieve seizure remission was 1.9 ± 0.87 years. Regarding the seizure remission pattern, 171 (40.9%) patients achieved early remission, 81 (19.4%) achieved late remission, and 166 (39.7%) achieved no remission. Shorter pre-treatment duration (AHR = 2.36, 95% CI: 1.28-4.37); good adherence to ASDs (AHR = 2.40, 95% CI: 1.33-4.34); and monotherapy (AHR = 0.56, 95% CI: 0.32-0.98) were predictors of seizure remission.

Conclusion

We observed that less than two-thirds of epileptic patients had achieved seizure remission. A shorter pre-treatment duration, good adherence to ASDs, and monotherapy were predictors of seizure remission. Therefore, we recommend the requirement of an integrated effort from different health disciplines that increases patients' adherence to ASDs, promotes early visits to medical facilities, and improves the health-seeking behavior of epileptic patients.

Treatment Outcomes of Newly Diagnosed Epilepsy: A Systematic Review and Meta-analysis

BACKGROUND AND OBJECTIVES

Understanding the multi-faceted treatment outcomes of newly diagnosed epilepsy is critical for developing rational therapeutic strategies. A meta-analysis was conducted to derive pooled estimates of a range of seizure outcomes in children and adults with newly diagnosed epilepsy commenced on antiseizure medication treatment, and to identify factors associated with different outcomes.

METHODS

PubMed/EMBASE were screened for eligible articles between 1 January, 1995 and 1 May, 2021 to include unselected cohort studies with a ≥ 12-month follow-up of seizure outcomes. Proportions of patients seizure free at different follow-up timepoints and their characteristics at the study population level were extracted. The patients were group-wise aggregated using a random-effects model. Primary outcomes were proportions of patients with cumulative 1-year seizure freedom (C1YSF), and 1-year and 5-year terminal seizure freedom (T1YSF and T5YSF). Secondary outcomes included the proportions of patients with early sustained seizure freedom, drug-resistant epilepsy and seizure-free off antiseizure medication at the last follow-up (off antiseizure medications). A separate random-effects meta-analysis was performed for nine predictors of importance.

RESULTS

In total, 39 cohorts (total n = 21,139) met eligibility criteria. They included 15 predominantly adult cohorts (n = 12,024), 19 children (n = 6569), and 5 of mixed-age groups (n = 2546). The pooled C1YSF was 79% (95% confidence interval [CI] 74-83). T1YSF was 68% (95% CI 63-72) and T5YSF was 69% (95% CI 62-75). Children had higher C1YSF (85% vs 68%, p < 0.001) and T1YSF than adult cohorts (74% vs 61%, p = 0.007). For secondary outcomes, 33% (95% CI 27-39) of patients achieved early sustained seizure freedom, 17% (95% CI 13-21) developed drug resistance, and 39% (95% CI 30-50) were off antiseizure medications at the last follow-up. Studies with a longer follow-up duration correlated with higher C1YSF (p < 0.001) and being off antiseizure medications (p = 0.045). Outcomes were not associated with study design (prospective vs retrospective), cohort size, publication year, or the earliest date of recruitment. Predictors of importance in newly diagnosed epilepsy include etiology, epilepsy type, abnormal diagnostics (neuroimaging, examination, and electroencephalogram findings), number of seizure types, and pre-treatment seizure burden.

CONCLUSIONS

Seizure freedom is achieved with currently available antiseizure medications in most patients with newly diagnosed epilepsy, yet this is often not immediate, may not be sustainable, and has not improved over recent decades. Symptomatic etiology, abnormal neuro-diagnostics, and increased pre-treatment seizure burden and seizure types are important predictors for unfavorable outcomes in newly diagnosed epilepsy. The study findings may be used as a quantitative benchmark on the efficacy of future antiseizure medication therapy for this patient population.

Seizure outcome-related factors in autoimmune encephalitis: A systematic review and meta-analysis

Background

Identifying the predictors for seizure outcome in autoimmune encephalitis (AE) and investigating how to prevent persistent seizures would have major clinical benefits effectively. Thus, we aimed to perform a systematic review and meta-analysis to examine seizure outcome-related factors in AE patients.

Methods

PubMed and EMBASE were systematically searched from inception to 10 June 2022 for studies investigating seizure outcome-related factors in AE. The pooled effect estimates, including standardized mean differences (SMDs) and odds ratios (ORs) with 95% confidence intervals (CIs), were calculated to estimate the effect of each included factor on the seizure outcome.

Results

A total of 10 studies were included in the meta-analysis. Our pooled results of this meta-analysis showed that five factors were found to increase the risk of persistent seizures in AE patients, including onset with seizures (OR = 2.106, 95% CI = 1.262–3.514, p = 0.004), status epilepticus (OR = 3.017, 95% CI = 1.995–4.563, p &lt; 0.001), EEG abnormalities (OR = 1.581, 95% CI = 1.016–2.46, p = 0.042), MRI abnormalities (OR = 1.554, 95% CI = 1.044–2.283, p = 0.03), and longer time from clinical onset to immunotherapy (SMD = 1.887, 95% CI = 0.598–3.156, p = 0.004).

Conclusion

Our meta-analysis indicated that onset with seizures, status epilepticus, EEG abnormalities, MRI abnormalities, and longer time from clinical onset to immunotherapy were risk factors for persistent seizures in AE patients.

Quality of life and its associated factors among epileptic patients attending public hospitals in North Wollo Zone, Northeast Ethiopia: A cross-sectional study

BACKGROUND

Epilepsy is thought to be caused by witchcraft, evil spirit, and God's punishment for sins in many developing countries. As a result, people with epilepsy and their families usually suffer from stigma, discrimination, depression, and other psychiatric problems. Thus, this study aimed to assess the quality of life and its associated factors among epileptic patients attending public hospitals in North Wollo Zone, Northeast Ethiopia.

METHODS

An institution-based cross-sectional study design was employed in this study. A simple random sampling technique was utilized. Health-related quality of life was measured based on the total score of the Quality of Life in Epilepsy Inventory (QOLIE-31) instrument. Data were entered into Epi-data 3.1 statistical package and exported to SPSS Version 20 for further analysis. Linear regression models were used to assess the relationship between quality of life and the independent variables. Statistically significant values were declared at a P-value of < 0.05.

RESULTS

A total of 395 patients participated in the study making the response rate 98.5%. The mean age of the participants was 32.39 ±10.71 years. More than half, 199 (50.4%) of epileptic patients had an overall weighted average health related quality of life score of mean and above. Male sex (B = 4.34, 95%CI, 0.41, 8.27, P = 0.03), higher educational status (B = 7.18, 95%CI, 1.39, 13.00, P = 0.015) and age at onset of epilepsy (B = 0.237, 95%CI, 0.02, 0.45, P = 0.035) were associated with increased health related quality of life score. On the other hand, family history of epilepsy (B = -4.78, 95%CI,-9.24,-0.33, P = 0.035), uncontrolled seizure (B = -11.08, 95%CI,-15.11,-7.05, P < 0.001), more than 5 pre-treatment number of seizures (B = -4.86, 95%CI,-8.91,-0.81, P = 0.019), poor drug adherence (B = -11.65, 95%CI,-16.06,-7.23, P < 0.001), having moderate (B = -4.526, 95%CI,-8.59,-0.46, P = 0.029) to sever (B = -12.84, 95%CI,-18.30,-7.37, P < 0.001) anxiety and depression, believing that epilepsy is caused by evil spirit (B = -7.04, 95%CI,-11.46,-2.61, P = 0.002), drinking alcohol (B = -5.42, 95%CI,-10.72,-0.13, P = 0.045), and having other co-morbidities (B = -9.35, 95%CI,-14.35,-4.36, P < 0.001) were significantly negatively associated with the health related quality of life score among epileptic patients.

CONCLUSIONS

Only around half of the epileptic patients have a good health-related quality of life. In addition, multiple variables including family history, uncontrolled seizure, and poor drug adherence were associated with quality of life among epileptic patients. Hence, targeting these variables in epilepsy management is recommended.

Seizures and risk of epilepsy in anti-NMDAR, anti-LGI1, and anti-GABAB R encephalitis

Background

Accumulating data have suggested seizures occur frequently in patients with neuronal surface antibody‐mediated autoimmune encephalitis. We aimed to evaluate seizure outcomes and potential factors associated with the development of epilepsy in patients with anti‐N‐methyl‐D‐aspartate receptor (NMDAR), anti‐leucine‐rich glioma‐inactivated 1 (LGI1), and anti‐gamma‐aminobutyric‐acid B receptor (GABA_BR) encephalitis.

Methods

Patients with anti‐NMDAR, anti‐LGI1, and anti‐GABA_BR encephalitis were prospectively recruited from 2014 to June 2019, with a median follow‐up period of 30.5 months (range 8–67 months). Seizure outcomes were assessed and risk factors of epilepsy were analyzed.

Results

A total of 119 patients with anti‐NMDAR, anti‐LGI1, and anti‐GABA_BR encephalitis were included, and 83 (69.7%) of them developed new‐onset seizures. By the end of follow‐up, 17 (21.3%) of 80 patients had seizure relapses after intermittent seizure remission or exhibited uncontrolled seizure episodes, contributing to epilepsy. Immunotherapy delay and interictal epileptic discharges (IEDs) were identified to be associated with the development of epilepsy in patients with anti‐NMDAR, anti‐LGI1, and anti‐GABA_BR encephalitis, particularly anti‐NMDAR encephalitis. Furthermore, multivariate logistic regression analysis demonstrated that immunotherapy delay was an independent predictor for epilepsy.

Conclusion

Our study suggested that immunotherapy delay and IEDs were associated with the development of epilepsy in patients with anti‐NMDAR, anti‐LGI1, and anti‐GABA_BR encephalitis. Early diagnosis and treatment were required, and particular consideration should be given to patients with these risk factors.

Treatment response and predictors in patients with newly diagnosed epilepsy in Ethiopia: a retrospective cohort study

Epilepsy is a chronic neurological disease with a variable therapeutic response. To design effective treatment strategies for epilepsy, it is important to understand treatment responses and predictive factors. However, limited data are available in Africa, including Ethiopia. The aim of this study was therefore to assess treatment response and identify prognostic predictors among patients with epilepsy at Jimma university medical center, Ethiopia. We conducted a retrospective cohort study of 404 newly diagnosed adult epilepsy patients receiving antiepileptic treatment between May 2010 and May 2015. Demographic, clinical, and outcome data were collected for all patients with a minimum follow-up of two years. Cox proportional hazards model was used to identify predictors of poor seizure remission. Overall, 261 (64.6%) of the patients achieved seizure remission for at least one year. High number of pre-treatment seizures (adjusted hazard ratios (AHR) = 0.64, 95% CI: 0.49–0.83) and poor adherence (AHR = 0.57, 95% CI: 0.44–0.75) were significant predictors of poor seizure remission. In conclusion, our study showed that only about two-thirds of patients had achieved seizure remission. The high number of pre-treatment seizures and non-adherence to antiepileptic medications were predictors of poor seizure remission. Patients with these characteristics should be given special attention.

Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning

OBJECTIVE

The objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy.

METHODS

We collected information from 287 patients with newly diagnosed epilepsy between 2009 and 2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at least 3 years. A number of features, including demographic features, medical history, and auxiliary examinations (electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission is further divided into early remission and late remission. Five classical machine learning algorithms, i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and trained by our dataset to get classification models.

RESULTS

Our study shows that 1) compared with the other four algorithms, the XGBoost algorithm based machine learning model achieves the best prediction performance of the AED treatment outcomes between remission and never remission patients with an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best discriminative factor for remission and never remission patients is higher number of seizures before treatment (>3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the highest dependence to the categories of early and late remission patients.

SIGNIFICANCES

Our XGBoost-based machine learning classifier accurately predicts the most probable AED treatment outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The classifier's prediction result could help disease guide counseling and eventually improve treatment strategies.

Expression of plasma microRNA-145-5p and its correlation with clinical features in patients with refractory epilepsy

PURPOSE

The potential of microRNAs (miRNAs) as biomarkers has been explored in various brain diseases, including epilepsy. In this study, we are aiming to analyze the aberrant expression of miRNA-145-5p in patients with refractory epilepsy, and to further explore the correlation with clinical features.

METHODS

The study cohort comprised 40 patients with refractory epilepsy and 42 healthy controls. MiRNA-145-5p expression levels in plasma were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR). Data analysis was performed using IBM SPSS Statistics 22.0.

RESULTS

Compared with healthy controls, the expression of miRNA-145-5p in plasma was downregulated significantly in the patients with refractory epilepsy (1.180 ± 1.036 vs. 1.541 ± 0.936, p = 0.033) and mesial temporal lobe epilepsy (MTLE) (0.517 ± 0.483 vs. 1.541 ± 0.936, p = 0.004). ROC analysis showed that the area under the curve (AUC) was 0.632 (95%CI: 0.508-0.755; P = 0.040) in refractory epilepsy and 0.829 (95%CI: 0.702-0.955; P = 0.001) in MTLE. Furthermore, the expression of miRNA-145-5p was positively correlated with earlier age at epilepsy onset, more frequent seizures and past history.

CONCLUSIONS

We suggested that decreased expression of miRNA-145-5p could be a potential non-invasive biomarker for early detection and clinical evaluation of refractory epilepsy. However, further studies are still required.

Treatment outcome and associated factors among patients with epilepsy

Epilepsy is a major public health problem worldwide. Despite multiple drug therapies, people with epilepsy continue to have frequent seizures. There is a dearth of data on epilepsy treatment outcome and associated factors in our setting. Therefore, the aim of this was to assess treatment outcome and associated factors among epileptic patients on follow up at the neurologic clinic of Ayder comprehensive specialized hospital, Ethiopia. A cross-sectional study was conducted on randomly selected epileptic patients. Data were collected through patient interview and review of medical records. Epilepsy treatment outcome was evaluated in terms of seizure control status in the last one year follow up period. Binary logistic regression analysis was performed to identify predictors of treatment outcome. A total of 270 patients were included. Of whom, 46.6% had controlled seizures. Whereas, 38.5%, 8.8%, and 5.9% had experienced seizure attacks 1-5 times, 6-10 times, and greater than 10 times, respectively. Alcohol consumption [adjusted odds ratio [(AOR): 14.87, 95% confidence interval (CI): 3.25-68.11], negative medication belief [AOR: 3.0, 95%CI: 1.31-6.71], low medication adherence [AOR:11.52, 95%CI: 3.25-40.82], and presence of comorbidities [AOR: 10.35, 95%CI: 4.40-24.40] were predictors of uncontrolled seizure. Our finding revealed that more than half of the epileptic patients had uncontrolled seizure. Epileptic patients with a negative medication belief, comorbidities, low medication adherence, and those who consume alcohol were more likely to have uncontrolled seizure. Therefore, more emphasis should be given to these patients.

Autophagy-related protein expression was associated with BRAF V600E mutation in epilepsy associated glioneuronal tumors

PURPOSE

The aim of this study was to explore the expression level of autophagy-related proteins in epileptic patients with glioneuronal tumors (GNTs) and evaluate the association with clinicopathological features.

MATERIALS AND METHODS

We obtained the brain specimens from 33 patients with GNTs, including 22 gangliogliomas (GGs) and 11 dysembryoplastic neuroepithelial tumors (DNTs). The expression of two autophagy-related proteins (LC3B and Beclin-1) was evaluated by immunohistochemistry, and BRAF V600E mutation was examined by DNA sequencing.

RESULTS

Among 33 epileptic patients with GNTs, the frequency of high expression of LC3B was 36.4% (12/33), and that of Beclin-1 was 39.4% (13/33). High expression of LC3B and Beclin-1 proteins was significantly associated with BRAF V600E mutation in GNTs (P=0.008; P=0.018), and LC3B overexpression was also correlated with temporal location of GNTs (P=0.002). In GGs alone, high expression of LC3B revealed significant correlation with BRAF V600E mutation and temporal location (P=0.020; P=0.015), while Beclin-1 showed no correlation with them (P>0.05). Furthermore, autophagy-related proteins did not show any association with other studied clinicopathological features, such as gender, age at seizure onset, epilepsy duration and postoperative seizure outcome.

CONCLUSIONS

Our observations demonstrated that impaired autophagy may be associated with BRAF V600E mutation. However, large sample studies with long-term follow-up were required.

BRAF V600E mutation in epilepsy-associated glioneuronal tumors: Prevalence and correlation with clinical features in a Chinese population

PURPOSE

Glioneuronal tumors (GNTs) are the most common histological type of brain tumors in patients who received epilepsy surgery, and part of them presented with BRAF V600E mutation. We aimed to verify the presence of the BRAF V600E mutation in epilepsy-associated GNTs from Chinese population and evaluate the association with clinical features.

METHODS

Data from 35 patients diagnosed with GNTs, including 24 gangliogliomas and 11 dysembryoplastic neuroepithelial tumors, were retrospectively collected. DNA was extracted from GNTs tissues and BRAF V600E mutation was examined by DNA sequencing. The correlations between BRAF V600E mutation and clinical features were analyzed.

RESULTS

Totally, BRAF V600E mutations were detected in 11 patients with GNTs, the rate of mutation were 33.3% and 27.3% in GGs (8/24) and DNTs (3/11), respectively. The probability of BRAF V600E mutation in females (7/12, 58.3%) was significantly higher than that in males (4/23, 17.4%) (P=0.022). Moreover, patients with BRAF-mutated GNTs had a significantly wider variety of seizure types compared to GNTs with BRAF wild-type status (P=0.027). However, no significant correlation between the BRAF status and certain clinical features, such as age of seizure onset, duration of epilepsy, age at surgery, location of the tumor and postoperative seizure free, were observed.

CONCLUSION

We demonstrated the presence of BRAF V600E mutation in Chinese epileptic patients with GNTs, which was significantly correlated with gender and multiple seizure types. Large sample studies and long-term follow-up are required for further confirmation.

Pressure-induced solid carbonates from molecular CO2 by computer simulation

A combination of ab initio molecular dynamic simulations and fully relaxed total energy calculations is used to predict that molecular CO2 should transform to nonmolecular carbonate phases based on CO4 tetrahedra at pressures in the range of 35 to 60 gigapascals. The simulation suggests a variety of competing phases, with a more facile transformation of the molecular phase at high temperatures. Thermodynamically, the most stable carbonate phase at high pressure is predicted to be isostructural to SiO2 alpha-quartz (low quartz). A class of carbonates, involving special arrangements of CO4 tetrahedra, is found to be more stable than all the other silica-like polymorphs.