EEG Markers of Treatment Resistance in Idiopathic Generalized Epilepsy: From Standard EEG Findings to Advanced Signal Analysis

Irelli, Emanuele Cerulli; Leodori, Giorgio; Morano, Alessandra; Bonaventura, Carlo Di

doi:10.3390/biomedicines10102428

Cited by 7 publications

(5 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…46 For example, several studies have investigated the relationship between EEG abnormalities and outcomes, often providing conflicting or incongruous evidence; whilst it is probable that some association exists, it is likely that EEG patterns and features influence/are mediated by external factors, and further multivariable research is required to determine how. 47 To facilitate application and future evaluation (as in with systematic reviews) it is recommended that prediction models be designed and reported in a systematic manner, such as is outlined by Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines. 48 Adherence to a predefined set of guidelines, such as TRIPOD, helps to ensure that the risk of bias (RoB; systematic error) and applicability concerns (AC) in the resultant study are kept to a minimum.…”

Section: Second-line Therapiesmentioning

confidence: 99%

Clinical Prediction Models for Treatment Outcomes in Newly-diagnosed Epilepsy

Ratcliffe,

Pradeep,

Marson

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: Up to 35% of individuals diagnosed with epilepsy proceed to develop pharmacoresistant epilepsy, leading to persistent uncontrolled seizure activity that can directly, or indirectly, significantly degrade an individual's quality of life. The factors underlying pharmacoresistance are unclear, but it has been hypothesised that repeated ictogenic activity is conducive to the development of a more robust epileptogenic network. To ensure that the most effective treatment choices are made and ictogenic activity is minimised, accurate outcome modelling at the point of diagnosis is key. Objectives: This review therefore aims to identify demographic, clinical, physiological (e.g. EEG), and imaging (e.g. MRI) factors that may be predictive of treatment outcomes in patients with newly diagnosed epilepsy (NDE). Data sources, study eligibility criteria, participants, and interventions: MEDLINE and EMBASE were searched for prediction models of treatment outcomes in patients with newly diagnosed epilepsy and any non-surgical treatment plan. Study appraisal and synthesis methods: Study characteristics were extracted and subjected to assessment of risk of bias (and applicability concerns) using the PROBAST tool. Prognostic factors associated with treatment outcomes are reported. Results: After screening, 48 models were identified in 32 studies, which generally scored low for concerns of applicability, but universally high for susceptibility to bias. Outcomes reported were heterogenous, but fit broadly into four categories: pharmacoresistance, short-term treatment response, seizure remission, and mortality. Prognostic factors were also heterogenous, but the predictors that were commonly significantly associated with outcomes were those related to seizure characteristics (semiology), epilepsy history, and age at onset. ASM response was often included as a prognostic factor, potentially obscuring factor relationships at baseline. Conclusions: Currently, outcome prediction models for NDE demonstrate a high risk of bias. Model development could be improved with a stronger adherence to recommended TRIPOD practices, and by avoiding including response to treatment as a prognostic factor. Implications of key findings: This review identified semiology, epilepsy history, and age at onset as factors associated with treatment outcome prognosis, suggesting that future prediction model studies should focus on these factors in their models. Furthermore, we outline actionable changes to common practices that are intended to improve the overall quality of prediction model development in NDE. Keywords: newly diagnosed epilepsy, seizures, prognosis, intractability, outcomes, treatment

show abstract

Section: Second-line Therapiesmentioning

confidence: 99%

Clinical Prediction Models for Treatment Outcomes in Newly-diagnosed Epilepsy

Ratcliffe,

Pradeep,

Marson

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…33 For example, several studies have investigated the relationship between EEG abnormalities and outcomes, often providing conflicting or incongruous evidence; although it is probable that some association exists, it is likely that EEG patterns and features influence/are mediated by external factors, and further multivariable research is required to determine how. 34 To facilitate application and future evaluation (as in with systematic reviews) it is recommended that prediction models be designed and reported in a systematic manner, such as is outlined by Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines. 35 Adherence to a predefined set of guidelines, such as TRIPOD, helps to ensure that the risk of bias (RoB; systematic error) and amount of applicability concerns in the resultant study are kept to a minimum.…”

Section: Treatment Outcomesmentioning

confidence: 99%

“…Single biomarkers (quantifiable properties indicative of normal biological processes) in epilepsy are thought to lack the granularity and robustness necessary to allow for clinical application 33 . For example, several studies have investigated the relationship between EEG abnormalities and outcomes, often providing conflicting or incongruous evidence; although it is probable that some association exists, it is likely that EEG patterns and features influence/are mediated by external factors, and further multivariable research is required to determine how 34 . To facilitate application and future evaluation (as in with systematic reviews) it is recommended that prediction models be designed and reported in a systematic manner, such as is outlined by Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines 35 .…”

Section: Introductionmentioning

confidence: 99%

Clinical prediction models for treatment outcomes in newly diagnosed epilepsy: A systematic review

Ratcliffe,

Pradeep,

Marson

et al. 2024

Epilepsia

View full text Add to dashboard Cite

Up to 35% of individuals diagnosed with epilepsy continue to have seizures despite treatment, commonly referred to as drug‐resistant epilepsy. Uncontrolled seizures can directly, or indirectly, negatively impact an individual's quality of life. To inform clinical management and life decisions, it is important to be able to predict the likelihood of seizure control. Those likely to achieve seizure control will be able to return sooner to their usual work and leisure activities and require less follow‐up, whereas those with a poor prognosis will need more frequent clinical attendance and earlier consideration of epilepsy surgery. This is a systematic review aimed at identifying demographic, clinical, physiological (e.g., electroencephalographic), and imaging (e.g., magnetic resonance imaging) factors that may be predictive of treatment outcomes in patients with newly diagnosed epilepsy (NDE). MEDLINE and Embase were searched for prediction models of treatment outcomes in patients with NDE. Study characteristics were extracted and subjected to assessment of risk of bias (and applicability concerns) using the PROBAST (Prediction Model Risk of Bias Assessment Tool) tool. Baseline variables associated with treatment outcomes are reported as prognostic factors. After screening, 48 models were identified in 32 studies, which generally scored low for concerns of applicability, but universally scored high for susceptibility to bias. Outcomes reported fit broadly into four categories: drug resistance, short‐term treatment response, seizure remission, and mortality. Prognostic factors were also heterogenous, but the predictors that were commonly significantly associated with outcomes were those related to seizure characteristics/types, epilepsy history, and age at onset. Antiseizure medication response was often included as a baseline variable, potentially obscuring other factor relationships at baseline. Currently, outcome prediction models for NDE demonstrate a high risk of bias. Model development could be improved with a stronger adherence to recommended TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) practices. Furthermore, we outline actionable changes to common practices that are intended to improve the overall quality of prediction model development in NDE.

show abstract

“…Numerous studies in the field of epilepsy research have focused on analyzing the EEG power spectrum (Clemens et al, 2021 ; Irelli et al, 2022 ; Zhong et al, 2022 ). Among these, special attention has been given to the alpha rhythm, which is influenced by cortico-thalamic interactions.…”

Section: Introductionmentioning

confidence: 99%

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns

Guerrero-Aranda,

Ramírez-Ponce,

Ramos-Quezada

et al. 2023

Front. Hum. Neurosci.

View full text Add to dashboard Cite

A typical absence seizure is a generalized epileptic event characterized by a sudden, brief alteration of consciousness that serves as a hallmark for various generalized epilepsy syndromes. Distinguishing between similar interictal and ictal electroencephalographic (EEG) epileptiform patterns poses a challenge. However, quantitative EEG, particularly spectral analysis focused on EEG rhythms, shows potential for differentiation. This study was designed to investigate discernible differences in EEG spectral dynamics and entropy patterns during the pre-ictal and post-ictal periods compared to the interictal state. We analyzed 20 EEG ictal patterns from 11 patients with confirmed typical absence seizures, and assessed recordings made during the pre-ictal, post-ictal, and interictal intervals. Power spectral density (PSD) was used for the quantitative analysis that focused on the delta, theta, alpha, and beta bands. In addition, we measured EEG signal regularity using approximate (ApEn) and multi-scale sample entropy (MSE). Findings demonstrate a significant increase in delta and theta power in the pre-ictal and post-ictal intervals compared to the interictal interval, especially in the posterior brain region. We also observed a notable decrease in entropy in the pre-ictal and post-ictal intervals, with a more pronounced effect in anterior brain regions. These results provide valuable information that can potentially aid in differentiating epileptiform patterns in typical absence seizures. The implications of our findings are promising for precision medicine approaches to epilepsy diagnoses and patient management. In conclusion, our quantitative analysis of EEG data suggests that PSD and entropy measures hold promise as potential biomarkers for distinguishing ictal from interictal epileptiform patterns in patients with confirmed or suspected typical absence seizures.

show abstract

EEG Markers of Treatment Resistance in Idiopathic Generalized Epilepsy: From Standard EEG Findings to Advanced Signal Analysis

Cited by 7 publications

References 92 publications

Clinical Prediction Models for Treatment Outcomes in Newly-diagnosed Epilepsy

Clinical Prediction Models for Treatment Outcomes in Newly-diagnosed Epilepsy

Clinical prediction models for treatment outcomes in newly diagnosed epilepsy: A systematic review

Quantitative EEG analysis in typical absence seizures: unveiling spectral dynamics and entropy patterns

Contact Info

Product

Resources

About