Materiały źródłowe

• #1 High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid iEEG marker | Scientific Reports

  https://www.nature.com/articles/s41598-024-56827-3

  Accurately identification of the seizure onset zone (SOZ) is pivotal for successful surgery in patients with medically refractory epilepsy. […] This study suggests that the hybrid iEEG marker can improve the performance of model predicting the epilepsy surgical outcomes, and validates the effectiveness of the GNN in characterizing and analyzing complex relationships between clinical data variables. […] By accurately identifying the SOZ, physicians can surgically target and remove or disrupt the brain regions generating these abnormal activities, effectively reducing the frequency and severity of seizures. […] Therefore, for patients with drug-resistant epilepsy, precise localization of the SOZ is fundamental and a critical step in surgical treatment. […] A series of comparisons revealed superior performance of the GNN over traditional machine learning models when utilizing both hybrid and single markers, and improved performance when hybrid markers was employed various machine learning models relative to single markers.

• #2 Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography | Scientific Reports

  https://www.nature.com/articles/s41598-024-72249-7

  Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. […] There is a critical need for accurate prediction tools to identify patients likely to have recurrent postoperative seizures. […] Using a dataset of 294 patients who underwent temporal lobe resection for seizures, we show that machine learning classifiers can make accurate predictions of postoperative seizure outcome using 5 min of peri-ictal scalp EEG data that is part of universal presurgical evaluation (AUC 0.98, out-of-group testing accuracy 90%). […] This is the first approach to seizure outcome prediction that employs a routine non-invasive preoperative study (scalp EEG) with accuracy range likely to translate into a clinical tool.

• #3 Prognosis of adults and children following a first unprovoked seizure

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9869434/

  Fiftyeight studies (involving 54 cohorts), with a total of 12,160 participants (median 147, range 31 to 1443), met the inclusion criteria for the review. […] We found an estimated overall seizure recurrence of all included studies at six months of 27% (95% CI 24% to 31%), 36% (95% CI 33% to 40%) at one year and 43% (95% CI 37% to 44%) at two years, with slightly lower estimates for adult subgroup analysis and slightly higher estimates for paediatric subgroup analysis. […] Despite the limitations of the data (moderate-certainty of evidence), mainly relating to clinical and methodological heterogeneity we have provided summary estimates for the likely risk of seizure recurrence at six months, one year and two years for both children and adults. […] This provides information that is likely to be useful for the clinician counselling patients (or their parents) on the probable risk of further seizures in the short-term whilst acknowledging the paucity of long-term recurrence data, particularly beyond 10 years.

• #4 Prognosis of adults and children following a first unprovoked seizure

  https://pmc.ncbi.nlm.nih.gov/articles/PMC9869434/

  The cumulative incidence of a single unprovoked epileptic seizure in the general population is approximately 3% to 4% by the time one reaches 85 years of age. […] It is imperative that accurate prognostic data are available so that clinicians can reliably counsel people on the risk of further seizures, and factors that predict the recurrence of seizures and therefore the development of epilepsy.

• #5 Clinical Prediction Models for Treatment Outcomes in Newly-diagnosed Epilepsy | medRxiv

  https://www.medrxiv.org/content/10.1101/2024.01.12.24301215v1.full-text

  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 individuals quality of life. […] 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. […] Prognostic factors associated with treatment outcomes are reported. […] 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.

• #6 Clinical Prediction Models for Treatment Outcomes in Newly-diagnosed Epilepsy | medRxiv

  https://www.medrxiv.org/content/10.1101/2024.01.12.24301215v1.full-text

  Currently, outcome prediction models for NDE demonstrate a high risk of bias. […] 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. […] Early seizure control has been indicated to be crucial for ensuring optimal treatment outcomes in NDE, putatively due to the prevention of further disruptions to seizure-related networks. […] Predicting treatment outcomes such as seizure remission, refractoriness, and pharmacoresistance is non-trivial, also requiring the consideration of factors like age at onset (and the related epilepsy duration), the number of pretreatment seizures, EEG/imaging abnormalities, intellectual impairments, aetiology, and semiology to inform trajectories.

• #7 Frontiers | Prediction model for long-term seizure and developmental outcomes among children with infantile epileptic spasms syndrome

  https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1195252/full

  Conclusion: Our prediction model may be useful for predicting the long-term prognosis of seizures and their development after 3 years. Understanding the long-term prognosis during the initial treatment may facilitate the selection of appropriate treatment. […] Seizure control and cognitive status are considered major outcome parameters in any study on IESS, and several reports have demonstrated the long-term clinical prognosis of IESS. Regarding seizure prognosis, underlying etiology, persistent ES, and the appearance of epileptic discharges after ACTH therapy are considered the prognostic factors. Regarding developmental prognosis, underlying etiology, early treatment initiation, persistent ES, and the presence of focal seizures and developmental delay before the onset of spasms are considered the prognostic factors.

• #8 Multimodal prognostic features of seizure freedom in epilepsy surgery | Journal of Neurology, Neurosurgery & Psychiatry

  https://jnnp.bmj.com/content/93/5/499

  Accurate preoperative predictions of seizure freedom following surgery for focal drug resistant epilepsy remain elusive. […] Our list of multimodal population-invariant prognostic features and proposed structural causal model may serve as an objective foundation for statistical adjustments of plausible confounders for use in high-dimensional models. […] Seizure-free outcomes have not improved over decades of epilepsy surgery and despite a multitude of models, none prognosticate accurately. […] This study defines a list of Essential Prognostic Features that were consistently prognostic across 38 evaluated meta-analyses of epilepsy surgery that had seizure-freedom as the primary outcome. […] Our list of essential prognostic features might be especially useful in machine learning models of big-data on postsurgical seizure freedom.

• #9 Predicting Seizure-Free Status for Temporal Lobe Epilepsy Patients Undergoing Surgery: Prognostic Value of Quantifying Maximal Metabolic Asymmetry Extending over a Specified Proportion of the Temporal Lobe | Journal of Nuclear Medicine

  https://jnm.snmjournals.org/content/48/5/776

  Conventional visual analysis of brain 18F-FDG PET scans is useful for predicting postsurgical improvement for temporal lobe epilepsy (TLE) patients, but prognostic value for identifying patients who will achieve seizure-free status is considerably lower. […] We aimed to develop an approach with which to quantitatively assess prognostically pertinent aspects of metabolic asymmetry in presurgical PET scans for forecasting postsurgical seizure-free clinical outcomes. […] Of the 75 patients studied, 42 became seizure free after surgery, whereas 33 continued to seize beyond the immediate postoperative period, during a mean 3.8-y follow-up interval. […] The specified proportion of temporal pixels with which to assess maximal asymmetry that provided the highest prognostic value with respect to achieving seizure-free status was 20%.

• #10 Predicting Seizure-Free Status for Temporal Lobe Epilepsy Patients Undergoing Surgery: Prognostic Value of Quantifying Maximal Metabolic Asymmetry Extending over a Specified Proportion of the Temporal Lobe | Journal of Nuclear Medicine

  https://jnm.snmjournals.org/content/48/5/776

  Overall, those patients with greater maximal asymmetry, as indexed by higher T-AI20 values, had a significantly decreased chance of achieving seizure-free status after surgery than those with lower degrees of asymmetry (P = 0.017). […] A quantifying approach to assessing maximal temporal asymmetry over a specified proportion of the temporal lobe may help to predict whether patients will likely be free of seizures during the years after neurosurgical resection of epileptogenic tissue. […] The clinical implication of these findings is that TLE patients with greater maximal temporal asymmetries (T-AI20 0.40 in the first and second PET patient series) who undergo surgery will less likely respond to treatment by becoming seizure free, relative to patients with a lower maximal asymmetry of temporal lobes. […] Thus, this quantitative approach could potentially be useful for helping to identify in advance those patients who would be more likely to achieve seizure-free outcome after initial surgical treatment of TLE.

• #11 Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography | Scientific Reports

  https://www.nature.com/articles/s41598-024-72249-7

  Decision curve analysis (DCA) shows that compared to the prevalent clinical-variable based nomogram, use of the EEG-augmented approach could decrease the rate of unsuccessful brain resections by 20%. […] The task of identifying DRE patients who are likely to experience recurrent seizures after resection is vital but challenging; there is thus a critical need for accurate evidence-based prediction tools. […] Seizure outcome prediction tools for epilepsy surgery are available but accuracy is modest. […] The predictive power of the nomogram is limited in large part because the patient-specific granularity in terms of physiological data that is key to robust outcome prediction is lost when the patient is described in simplified categories. […] We share the results of machine learning model-building experiments in a large sample (n=294 surgical patients) to demonstrate that by using 5 min of peri-ictal scalp EEG data, it is possible to build a predictive framework of post-operative seizure outcome with accuracies significantly higher than those obtained from earlier approaches (90%) and within a range that is likely to be translatable into a clinically useful tool.

• #12 Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography | Scientific Reports

  https://www.nature.com/articles/s41598-024-72249-7

  The approach that we document in this report (i.e. the use of peri-ictal scalp EEG derived features in a machine-learning enabled predictive framework) overcomes both of these limitations and thus has the potential to achieve clinical translation into a useful presurgical tool. […] We have utilized Decision Curve Analysis (DCA) to evaluate the likely clinical usefulness of our approach. […] Given that over 15% of patients who undergo brain resection for seizures experience a neurologic complication (with 4.7% experiencing a major complication, such as hemiparesis, language difficulties, or memory difficulties), such a reduction in avoidable brain resections is particularly significant. […] There remains a critical need for accurate seizure outcome prediction models to identify patients that are likely to have seizure recurrence after brain resection. […] A predictive framework that uses a combination of clinical variables and peri-ictal scalp EEG data may provide high accuracy predictions in this context.

• #13 Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography | Scientific Reports

  https://www.nature.com/articles/s41598-024-72249-7

  The focus on the peri-ictal window (in our case, 2 min pre-ictal and 3 min post-ictal) is grounded in the existing science of seizure generation and propagation. […] The value of peri-ictal EEG becomes particularly clear when the results of our present report are compared with those of earlier studies wherein we attempted to use inter-ictal EEG to predict postoperative seizure outcomes in DRE patients. […] This comparison suggests that future electrophysiological investigations in DRE, particularly in terms of understanding differential responses to surgical therapies, should focus on the peri-ictal window. […] We present a novel approach to machine-learning enabled postoperative seizure control prediction in the context of DRE that is both highly accurate and makes use of data captured noninvasively and inexpensively during routine presurgical evaluation.

• #14 Prediction model for long-term seizure and developmental outcomes among children with infantile epileptic spasms syndrome

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10382128/

  Children with infantile epileptic spasms syndrome (IESS) are likely to experience poor outcomes. Researchers have investigated the factors related to its long-term prognosis; however, none of them developed a predictive model. […] This study aimed to clarify the factors that influence the long-term prognosis of seizures and their development and to create a prediction model for IESS. […] Data were obtained from 44 patients with IESS (19 female patients and 25 male patients). Three years after epileptic spasms (ES) onset, seizure and development were the poor outcomes in 15 (34.9%) and 27 (61.4%) patients, respectively. […] Seven years after the onset of ES, seizures and development were the poor outcomes in 9 (45.0%) and 13 (72.2%) patients, respectively. […] Our prediction model may be useful for predicting the long-term prognosis of seizures and their development after 3 years.

• #15 Frontiers | Prediction model for long-term seizure and developmental outcomes among children with infantile epileptic spasms syndrome

  https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1195252/full

  Prediction model for long-term seizure and developmental outcomes among children with infantile epileptic spasms syndrome […] Introduction: Children with infantile epileptic spasms syndrome (IESS) are likely to experience poor outcomes. Researchers have investigated the factors related to its long-term prognosis; however, none of them developed a predictive model. […] Objective: This study aimed to clarify the factors that influence the long-term prognosis of seizures and their development and to create a prediction model for IESS. […] Results: Data were obtained from 44 patients with IESS (19 female patients and 25 male patients). Three years after epileptic spasms (ES) onset, seizure and development were the poor outcomes in 15 (34.9%) and 27 (61.4%) patients, respectively. The persistence of ES or tonic seizures (TS) after 90 days of onset, moderate or severe magnetic resonance imaging abnormalities, and developmental delay before IESS onset were significantly associated with poor outcomes. Seven years after the onset of ES, seizures and development were the poor outcomes in 9 (45.0%) and 13 (72.2%) patients, respectively. We found that no factor was significantly associated with poor seizure outcomes, and only developmental delay before IESS onset was significantly associated with poor developmental outcomes. Our prediction model demonstrated 86.7% sensitivity and 64.3% specificity for predicting poor seizure outcomes and 88.9% sensitivity and 100% specificity for predicting poor developmental outcomes.

• #16 Prediction model for long-term seizure and developmental outcomes among children with infantile epileptic spasms syndrome

  https://pmc.ncbi.nlm.nih.gov/articles/PMC10382128/

  Understanding the long-term prognosis during the initial treatment may facilitate the selection of appropriate treatment. […] Persisting ES/TS after 90 days of onset was an important predictor of both seizure and developmental outcomes. […] Moreover, moderate or severe MRI abnormalities are important predictors of both seizure and developmental prognoses. […] Developmental delay before ES onset is an important predictor of both seizures and developmental prognosis. […] Our scoring system may be useful for predicting the long-term prognosis after 3 and 7 years of ES onset. Understanding the long-term prognosis of seizures and their development during the initial treatment may facilitate appropriate treatment selection.

• #17 Frontiers | Prediction model for long-term seizure and developmental outcomes among children with infantile epileptic spasms syndrome

  https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1195252/full

  We clarified the factors that influence the long-term outcomes of seizures and development and developed a prediction model after 3 years of the ES onset. We could not develop the prediction model after 7 years of ES onset; however, all participants with poor seizures and development at 7 years also demonstrated poor seizures and development at 3 years. […] Persisting ES/TS after 90 days of onset was an important predictor of both seizure and developmental outcomes. Developmental delay before ES onset is an important predictor of both seizures and developmental prognosis. Our scoring system may be useful for predicting the long-term prognosis after 3 and 7 years of ES onset. Understanding the long-term prognosis of seizures and their development during the initial treatment may facilitate appropriate treatment selection.

• #18 High-performance prediction of epilepsy surgical outcomes based on the genetic neural networks and hybrid iEEG marker | Scientific Reports

  https://www.nature.com/articles/s41598-024-56827-3

  The GNN has the best performance with AUC=0.94, which is significantly higher than that of the suboptimal model. […] Our findings reveals that the GNN, underpinned by hybrid markers, achieves an impressive accuracy of over 94%, surpassing other models by more than 6.59%. […] Our study presents a comprehensive combination boxing approach with an impressive prediction accuracy of 94.30%. […] The predictive ability of the GNN based on hybrid markers remains unaffected by gender and the dominant hand. […] Our GNN model with hybrid markers demonstrate SOZ localization abilities that are comparable to those of clinicians and provided accurate predictions of surgical results.

• #19 Scale for predicting the outcomes of patients with epilepsy | IJGM

  https://www.dovepress.com/a-scale-for-predicting-the-outcomes-of-patients-with-epilepsy-a-study–peer-reviewed-fulltext-article-IJGM

  Objective: This study aimed to identify the factors relevant for developing a scale to estimate the prognosis of patients with epilepsy. […] The SEPE was able to distinguish between better and worse outcomes for the three groups. A score 3 on the SEPE predicted that a patient would become seizure-free without the use of AEDs, with a specificity of 67% and a sensitivity of 50%. A score 4 on the SEPE predicted that a patient may have a positive outcome; scores in this range were assigned to 97.9% of patients that were seizure-free without the use of AEDs and 65% of patients with pharmacoresponsive epilepsy, with a specificity of 80%, a sensitivity of 81%. Scores 6 on the SEPE predicted a poor outcome. […] Of the patients with a SEPE score 3, some were able to become seizure-free without the use of AEDs, while for other patients, it may be possible that AED use can be discontinued. Patients with a SEPE score 4 have the potential to achieve long-term remission. Patients with a SEPE score 6 are more likely to have pharmacoresistant epilepsy.

• #20 Scale for predicting the outcomes of patients with epilepsy | IJGM

  https://www.dovepress.com/a-scale-for-predicting-the-outcomes-of-patients-with-epilepsy-a-study–peer-reviewed-fulltext-article-IJGM

  A longer duration of epilepsy appears to correspond to a worse prognosis. […] Patients that experienced mental decay had a poor prognosis, and patients with a family history of epilepsy were also at risk of a poor prognosis. […] In this study, 141 patients that had been followed up for between four and nine years were divided into three groups, namely patients that were seizure-free without the use of AEDs (n = 48), patients with pharmacoresponsive epilepsy (n = 52), and patients with pharmacoresistant epilepsy (n = 41). We used statistical analysis to determine the factors that were predictive of patient outcomes, and we used these predictors to design the SEPE and assign scores to evaluate each patient’s prognosis. We drew the following conclusions: (i) Some patients with a SEPE score 3 will experience self-limiting epilepsy and become seizure-free without the use of AEDs; for other patients with a SEPE score 3, it may be possible that the use of AEDs can be discontinued. For patients with a score 3, the SEPE had a specificity of 67% and a sensitivity of 50%. (ii) If a patient has a SEPE score 4, it is possible that long-term remission will be achieved, with a specificity of 80%, a sensitivity of 81%, a positive predictive value of 91%, and a negative predictive value of 63%. (iii) Patients with a SEPE score 6 may experience pharmacoresistance.

• #21 Epilepsy Prediction Tools

  http://epilepsypredictiontools.info/

  The antiepileptic drug (AED) withdrawal risk lookup table provides the risk of a seizure in the next 2 and 5 years after AED withdrawal, for people with epilepsy who are seizure-free thanks to AEDs. It also shows the chance to be seizure-free after 10 years. […] The nomogram estimates the risk of seizure recurrence and long-term seizure outcome for people with epilepsy who are currently seizure-free, in case of withdrawal of anti-epileptic drugs with the purpose of discontinuing treatment. […] It estimates the risk of seizure recurrence and long-term seizure outcome for children with epilepsy who underwent epilepsy surgery, in case of withdrawal of antiepileptic drugs with the purpose of discontinuing treatment.

• #22 Multimodal prognostic features of seizure freedom in epilepsy surgery | Journal of Neurology, Neurosurgery & Psychiatry

  https://jnnp.bmj.com/content/93/5/499

  We identified 46 features from 38 meta-analyses on prognostication in epilepsy surgery, only 15 of which were in the 2019 Cochrane review. […] EPF is a minimum essential list based on current best-evidence. […] Personalised prognostication in epilepsy surgery outcomes has remained elusive and outcomes have not improved with time.

• #23 A Preliminary Study on Multivariate Prediction of Seizure Outcome after Epilepsy Surgery | OMICS International | Abstract

  https://www.omicsonline.org/peer-reviewed/a-preliminary-study-on-multivariate-prediction-of-seizure-outcome-after-epilepsy-surgery-21968.html

  Surgical outcomes of epilepsy surgery vary across patients, and clinicians need to estimate possible outcomes before surgery. […] The aim of this study was to identify predictors of seizure outcome one year after surgery for patients with drug-resistant epilepsy. […] Important outcome predictors identified included EEG lateralization score, icEEG lateralization score, and the presence of Hippocampal Sclerosis (HS). […] The results suggested that multivariate models could predict seizure outcome after TLE surgery with moderate accuracy. […] Further studies are needed to improve prediction accuracy and identify reliable predictors of seizure outcome.

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