Predicting mood decline following temporal lobe epilepsy surgery in adults

De novo depression following temporal lobe epilepsy surgery

Surgical removal of the mesial temporal lobe can effectively treat drug-resistant epilepsy but may lead to mood disorders. This fact is of particular interest in patients without a prior psychiatric history. The study investigates the relationship between Temporal Lobe Epilepsy (TLE), mood disorders, and the functional connectivity of the Hippocampus (Hipp) and Nucleus Accumbens (NAcc). In this case control study, twenty-seven TLE patients and 18 control subjects participated, undergoing structural and functional magnetic resonance imaging (MRI) scans before and after surgery. Post-surgery, patients were categorized into those developing de novo depression (DnD) within the first year and those without depression (nD). Functional connectivity maps between NAcc and the whole brain were generated, and connectivity strength between the to-be-resected Hipp area and NAcc was compared. Within the first year post-surgery, 7 out of 27 patients developed DnD. Most patients (88.8 %) exhibited a significant reduction in NAcc-Hipp connectivity compared to controls. The DnD group showed notably lower connectivity values than the nD group, with statistically significant disparities. Receiver Operating Characteristic (ROC) curve analysis identified a potential biomarker threshold (Crawford-T value of -2.08) with a sensitivity of 0.83 and specificity of 0.76. The results suggest that functional connectivity patterns within the reward network could serve as a potential biomarker for predicting de novo mood disorders in TLE patients undergoing surgery. This insight may assist in identifying individuals at a higher risk of developing DnD after surgery, enhancing therapeutic guidance and clinical decision-making.

Predictive models of epilepsy outcomes

PURPOSE OF REVIEW

Multiple complex medical decisions are necessary in the course of a chronic disease like epilepsy. Predictive tools to assist physicians and patients in navigating this complexity have emerged as a necessity and are summarized in this review.

RECENT FINDINGS

Nomograms and online risk calculators are user-friendly and offer individualized predictions for outcomes ranging from safety of antiseizure medication withdrawal (accuracy 65-73%) to seizure-freedom, naming, mood, and language outcomes of resective epilepsy surgery (accuracy 72-81%). Improving their predictive performance is limited by the nomograms' inability to ingest complex data inputs. Conversely, machine learning offers the potential of multimodal and expansive model inputs achieving human-expert level accuracy in automated scalp electroencephalogram (EEG) interpretation but lagging in predictive performance or requiring validation for other applications.

SUMMARY

Good to excellent predictive models are now available to guide medical and surgical epilepsy decision-making with nomograms offering individualized predictions and user-friendly tools, and machine learning approaches offering the potential of improved performance. Future research is necessary to bridge the two approaches for optimal translation to clinical care.

Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study

BACKGROUND AND OBJECTIVES

Epilepsy surgery is often delayed. We previously developed machine learning (ML) models to identify candidates for resective epilepsy surgery earlier in the disease course. In this study, we report the prospective validation.

METHODS

In this multicenter, prospective, longitudinal cohort study, random forest models were validated at a pediatric epilepsy center consisting of 2 hospitals and 14 outpatient neurology clinic sites and an adult epilepsy center with 2 hospitals and 27 outpatient neurology clinic sites. The models used neurology visit notes, EEG and MRI reports, visit patterns, hospitalizations, and medication, laboratory, and procedure orders to identify candidates for surgery. The models were trained on historical data up to May 10, 2019. Patients with an ICD-10 diagnosis of epilepsy who visited from May 11, 2019, to May 10, 2020, were screened by the algorithm and assigned surgical candidacy scores. The primary outcome was area under the curve (AUC), which was calculated by comparing scores from patients who underwent epilepsy surgery before November 10, 2020, against scores from nonsurgical patients. Nonsurgical patients' charts were reviewed to determine whether patients with high scores were more likely to be missed surgical candidates. Delay to surgery was defined as the time between the first visit that a surgical candidate was identified by the algorithm and the date of the surgery.

RESULTS

A total of 5,285 pediatric and 5,782 adult patients were included to train the ML algorithms. During the study period, 41 children and 23 adults underwent resective epilepsy surgery. In the pediatric cohort, AUC was 0.91 (95% CI 0.87-0.94), positive predictive value (PPV) was 0.08 (0.05-0.10), and negative predictive value (NPV) was 1.00 (0.99-1.00). In the adult cohort, AUC was 0.91 (0.86-0.97), PPV was 0.07 (0.04-0.11), and NPV was 1.00 (0.99-1.00). The models first identified patients at a median of 2.1 years (interquartile range [IQR]: 1.2-4.9 years, maximum: 11.1 years) before their surgery and 1.3 years (IQR: 0.3-4.0 years, maximum: 10.1 years) before their presurgical evaluations.

DISCUSSION

ML algorithms can identify surgical candidates earlier in the disease course. Even at specialized epilepsy centers, there is room to shorten the time to surgery.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that a machine learning algorithm can accurately distinguish patients with epilepsy who require resective surgery from those who do not.

Expert opinion on diagnosis and management of epilepsy-associated comorbidities

Apart from seizure freedom, the presence of comorbidities related to neurological, cardiovascular, or psychiatric disorders is the largest determinant of a reduced health-related quality of life in people with epilepsy (PwE). However, comorbidities are often underrecognized and undertreated, and clinical management of comorbid conditions can be challenging. The focus of a comprehensive treatment regimen should maximize seizure control while optimizing clinical management of treatable comorbidities to improve a person's quality of life and overall health. A panel of four European epileptologists with expertise in their respective fields of epilepsy-related comorbidities combined the latest available scientific evidence with clinical expertise and collaborated to provide consensus practical advice to improve the identification and management of comorbidities in PwE. This review provides a critical evaluation for the diagnosis and management of sleep-wake disorders, cardiovascular diseases, cognitive dysfunction, and depression in PwE. Whenever possible, clinical data have been provided. The PubMed database was the main search source for the literature review. The deleterious pathophysiological processes underlying neurological, cardiovascular, or psychiatric comorbidities in PwE interact with the processes responsible for generating seizures to increase cerebral and physiological dysfunction. This can increase the likelihood of developing drug-resistant epilepsy; therefore, early identification of comorbidities and intervention is imperative. The practical evidence-based advice presented in this article may help clinical neurologists and other specialist physicians responsible for the care and management of PwE.

The relationship between mood and anxiety and cognitive phenotypes in adults with pharmacoresistant temporal lobe epilepsy

OBJECTIVE

Patients with temporal lobe epilepsy (TLE) are often at a high risk for cognitive and psychiatric comorbidities. Several cognitive phenotypes have been identified in TLE, but it is unclear how phenotypes relate to psychiatric comorbidities, such as anxiety and depression. This observational study investigated the relationship between cognitive phenotypes and psychiatric symptomatology in TLE.

METHODS

A total of 826 adults (age = 40.3, 55% female) with pharmacoresistant TLE completed a neuropsychological evaluation that included at least two measures from five cognitive domains to derive International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) cognitive phenotypes (i.e., intact, single-domain impairment, bi-domain impairment, generalized impairment). Participants also completed screening measures for depression and anxiety. Psychiatric history and medication data were extracted from electronic health records. Multivariable proportional odds logistic regression models examined the relationship between IC-CoDE phenotypes and psychiatric variables after controlling for relevant covariates.

RESULTS

Patients with elevated depressive symptoms had a greater odds of demonstrating increasingly worse cognitive phenotypes than patients without significant depressive symptomatology (odds ratio [OR] = 1.123-1.993, all corrected p's < .05). Number of psychotropic (OR = 1.584, p < .05) and anti-seizure medications (OR = 1.507, p < .001), use of anti-seizure medications with mood-worsening effects (OR = 1.748, p = .005), and history of a psychiatric diagnosis (OR = 1.928, p < .05) also increased the odds of a more severe cognitive phenotype, while anxiety symptoms were unrelated.

SIGNIFICANCE

This study demonstrates that psychiatric factors are not only associated with function in specific cognitive domains but also with the pattern and extent of deficits across cognitive domains. Results suggest that depressive symptoms and medications are strongly related to cognitive phenotype in adults with TLE and support the inclusion of these factors as diagnostic modifiers for cognitive phenotypes in future work. Longitudinal studies that incorporate neuroimaging findings are warranted to further our understanding of the complex relationships between cognition, mood, and seizures and to determine whether non-pharmacologic treatment of mood symptoms alters cognitive phenotype.

Machine learning using multimodal clinical, electroencephalographic, and magnetic resonance imaging data can predict incident depression in adults with epilepsy: A pilot study

OBJECTIVE

This study was undertaken to develop a multimodal machine learning (ML) approach for predicting incident depression in adults with epilepsy.

METHODS

We randomly selected 200 patients from the Calgary Comprehensive Epilepsy Program registry and linked their registry-based clinical data to their first-available clinical electroencephalogram (EEG) and magnetic resonance imaging (MRI) study. We excluded patients with a clinical or Neurological Disorders Depression Inventory for Epilepsy (NDDI-E)-based diagnosis of major depression at baseline. The NDDI-E was used to detect incident depression over a median of 2.4 years of follow-up (interquartile range [IQR] = 1.5-3.3 years). A ReliefF algorithm was applied to clinical as well as quantitative EEG and MRI parameters for feature selection. Six ML algorithms were trained and tested using stratified threefold cross-validation. Multiple metrics were used to assess model performances.

RESULTS

Of 200 patients, 150 had EEG and MRI data of sufficient quality for ML, of whom 59 were excluded due to prevalent depression. Therefore, 91 patients (41 women) were included, with a median age of 29 (IQR = 22-44) years. A total of 42 features were selected by ReliefF, none of which was a quantitative MRI or EEG variable. All models had a sensitivity > 80%, and five of six had an F1 score ≥ .72. A multilayer perceptron model had the highest F1 score (median = .74, IQR = .71-.78) and sensitivity (84.3%). Median area under the receiver operating characteristic curve and normalized Matthews correlation coefficient were .70 (IQR = .64-.78) and .57 (IQR = .50-.65), respectively.

SIGNIFICANCE

Multimodal ML using baseline features can predict incident depression in this population. Our pilot models demonstrated high accuracy for depression prediction. However, overall performance and calibration can be improved. This model has promise for identifying those at risk for incident depression during follow-up, although efforts to refine it in larger populations along with external validation are required.

More extensive structural damage in temporal lobe epilepsy with hippocampal sclerosis type 1

OBJECTIVE

To explore clinical and structural differences between mesial temporal lobe epilepsy (mTLE) patients with different hippocampal sclerosis (HS) subtypes.

METHODS

High-resolution T1-weighted MRI and diffusion tensor imaging data were obtained in 41 refractory mTLE patients and 52 age- and sex-matched healthy controls. Postoperative histopathological examination confirmed HS type 1 in 30 patients and HS type 2 in eleven patients. Clinical features, postoperative seizure outcomes, hippocampal subfields volumes, fractional anisotropy (FA) values of white matter regions and graph theory parameters were explored and compared between the HS type 1 and HS type 2 groups.

RESULTS

No significant differences in clinical features and postsurgical seizure outcomes were found between the HS type 1 and type 2 groups. However, the HS type 1 group showed extra atrophy in ipsilateral parasubiculum than healthy controls and more severe atrophy in contralateral hippocampal fissure than the HS type 2 group. More extensive FA decrease were also observed in the HS type 1 group, involving ipsilateral optic radiation, superior fronto-occipital fasciculus, contralateral uncinate fasciculus, tapetum, bilateral hippocampal cingulum, corona radiata, etc. Furthermore, in spite of similar impairments in characteristic path length, global efficiency and local efficiency in two HS groups, the HS type 1 group showed additional decrease of clustering coefficient than healthy controls.

CONCLUSIONS

HS type 1 and 2 groups had similar clinical characteristics and postoperative seizure outcomes. More widespread neuronal cell loss in the HS type 1 group contributed to more extensive structural damage and connectivity abnormality. These results shed new light on the imaging correlates of different HS pathology.

Using machine learning approach to predict depression and anxiety among patients with epilepsy in China: A cross-sectional study

BACKGROUND

Anxiety and depression are the most prevalent comorbidities among epilepsy patients. The screen and diagnosis of anxiety and depression are quite important for the management of patients with epilepsy. In that case, the method for accurately predicting anxiety and depression needs to be further explored.

METHODS

A total of 480 patients with epilepsy (PWE) were enrolled in our study. Anxiety and Depressive symptoms were evaluated. Six machine learning models were used to predict anxiety and depression in patients with epilepsy. Receiver operating curve (ROC), decision curve analysis (DCA) and moDel Agnostic Language for Exploration and eXplanation (DALEX) package were used to evaluate the accuracy of machine learning models.

RESULTS

For anxiety, the area under the ROC curve was not significantly different between models. DCA revealed that random forest and multilayer perceptron has the largest net benefit within different probability threshold. DALEX revealed that random forest and multilayer perceptron were models with best performance and stigma had the highest feature importance. For depression, the results were much the same.

CONCLUSIONS

Methods created in this study may offer much help identifying PWE with high risk of anxiety and depression. The decision support system may be valuable for the everyday management of PWE. Further study is needed to test the outcome of applying this system to clinical settings.

Characterization and Prediction of Short-term Outcomes in Memory After Temporal Lobe Resection in Children With Epilepsy

BACKGROUND AND OBJECTIVES

The aim of this study was to characterize short-term outcomes in episodic memory, as assessed by the Children's Memory Scale (CMS), after temporal lobe resection in children with epilepsy using empirical methods for assessing cognitive change (i.e., reliable change indices [RCI] and standardized regression-based change scores [SRB]) and develop and internally validate clinically applicable models to predict postoperative memory decline.

METHODS

This retrospective cohort study included children aged 6-16 years who underwent resective epilepsy surgery that included the temporal lobe (temporal only: "temporal" and multilobar: "temporal plus") and who completed preoperative and postoperative neuropsychological assessments including the CMS. Change scores on the CMS delayed memory subtests (Faces, Stories, and Word Pairs) were classified as decline, no change, or improvement using epilepsy-specific RCI and SRB. Logistic regression models for predicting postoperative memory decline were developed and internally validated with bootstrapping.

RESULTS

Of the 126 children included, most of them demonstrated either no significant change (54%-69%) or improvement (8%-14%) in memory performance using RCI on individual measures at a median of 7 months after surgery. A subset of children (23%-33%) showed postoperative declines. Change distributions obtained using RCI and SRB were not statistically significantly different from each other. Preoperative memory test score, surgery side, surgery extent, and preoperative full-scale IQ were predictors of memory decline. Prediction models for memory decline included subsets of these variables with bias-corrected concordance statistics ranging from 0.70 to 0.75. The models were well calibrated although slightly overestimated the probability of verbal memory decline in high-risk patients.

DISCUSSION

This study used empiric methodology to characterize memory outcome in children after temporal lobe resection. Provided online calculator and nomograms may be used by clinicians to estimate the risk of postoperative memory decline for individual patients before surgery.

Epilepsy surgery for dominant-side mesial temporal lobe epilepsy without hippocampal sclerosis

Although anterior temporal lobectomy (ATL) is an established surgery for medically intractable mesial temporal lobe epilepsy (MTLE), it can harm memory function, especially in dominant-side MTLE patients without hippocampal sclerosis (HS). To avoid this complication, multiple hippocampal transection (MHT) was developed, but its efficacy has not been fully elucidated. We report the detailed treatment results of MHT compared with that of ATL. We retrospectively analysed the records of 30 patients who underwent surgery for dominant-side MTLE. ATL was completed for 23 patients with HS, and MHT was completed for 7 patients without HS. The seizure control status, number of anti-seizure medicines, neurocognitive function, and psychiatric disorders of each patient were reviewed. The mean follow-up period was 70 months. Seizure control of Engel class I was achieved in 16 patients (70%) in the ALT group versus 5 patients (71%) in the MHT group. The mean number of anti-seizure medicines administered in the ATL group changed significantly from 2.4 to 1.9 (p = 0.01), while that in the MHT group was unchanged (from 2.1 to 2.0, p = 0.77). Eleven patients (48%) in the ATL group developed psychiatric disorders during the postoperative follow-up period, whereas no psychological complications were observed in the MHT group. Neither group showed neurocognitive decline after the surgery in any of the WAIS-III or WMS-R subtests. In conclusion, MHT may achieve reasonable postoperative seizure reduction, preserve neurocognitive function, and reduce postoperative psychiatric complications. Therefore, it can be considered as a therapeutic option for dominant-side MTLE without HS.

Epileptic-network-based prediction and control of seizures in humans

Epilepsy is now conceptualized as a network disease. The epileptic brain network comprises structurally and functionally connected cortical and subcortical brain regions - spanning lobes and hemispheres -, whose connections and dynamics evolve in time. With this concept, focal and generalized seizures as well as other related pathophysiological phenomena are thought to emerge from, spread via, and be terminated by network vertices and edges that also generate and sustain normal, physiological brain dynamics. Research over the last years has advanced concepts and techniques to identify and characterize the evolving epileptic brain network and its constituents on various spatial and temporal scales. Network-based approaches further our understanding of how seizures emerge from the evolving epileptic brain network, and they provide both novel insights into pre-seizure dynamics and important clues for success or failure of measures for network-based seizure control and prevention. In this review, we summarize the current state of knowledge and address several important challenges that would need to be addressed to move network-based prediction and control of seizures closer to clinical translation.

Foundations of Neuropsychology: Collaborative Care in Neurosurgery

The disciplines of neuropsychology and neurosurgery have a history of partnership that has improved prognoses for patients with neurologic diagnoses that once had poor outcomes. This article outlines the evolution of this relationship and describes the current role that clinical neuropsychology has within a department of neurological surgery across the preoperative, intraoperative, and postoperative stages of treatment. Understanding the foundations of collaboration between neuropsychology and neurosurgery contextualizes present challenges and future innovations for advancing excellence along the continuum of care for all neurosurgical patients.

Machine Learning for Precision Epilepsy Surgery

Brain surgery offers the best chance of seizure-freedom for patients with focal drug-resistant epilepsy, but only 50% achieve sustained seizure-freedom. With the explosion of data collected during routine presurgical evaluations and recent advances in computational science, we now have a tremendous potential to achieve precision epilepsy surgery: a data-driven tailoring of surgical planning. This review highlights the clinical need, the relevant computational science focusing on machine learning, and discusses some specific applications in epilepsy surgery.

Depression and suicide after temporal lobe epilepsy surgery: A systematic review

Psychiatric comorbidities, including depression and suicide, contribute substantially to the illness burden of patients with refractory temporal lobe epilepsy (TLE). The aim of this systematic review was to synthesize the existing literature assessing the effect of TLE surgery on (1) depression prevalence and (2) severity, and estimating the incidence of (3) de novo depression and (4) attempted and completed suicide following TLE surgery. A literature search was performed using Ovid Medline, Embase, Clarivate Web of Science, Cochrane Library, and ProQuest Dissertations and Theses. Studies of patients with TLE who underwent TLE surgery and reported estimates of at least one of the following outcomes were included: pre- and postoperative depression prevalence or severity, the incidence of postoperative de novo depression, or attempted or completed suicide. The search yielded 2,127 citations related to TLE surgery and postoperative depression or suicide. After a full-text review of 98 articles, 18 met the final eligibility criteria. Most studies reported a reduced or similar prevalence (n = 12) and severity of depression (n = 5) postoperatively, compared with the preoperative period. Eleven studies reported the incidence of postoperative de novo depression, which ranged from 0 % to 38 % over follow-up periods of three months to nine years. Four studies assessed the incidence of postoperative attempted or completed suicide, with completed suicide incidence ranging from 0 % to 3 % over follow-up periods of one to four years. Overall, the effect of TLE surgery on depression and suicide remains unclear, as many studies did not assess the statistical significance of depression prevalence or severity changes following TLE surgery. Therefore, timely psychosocial follow-up for patients after TLE surgery should be considered. Future longitudinal studies with consistent measures are needed to elucidate the effect of TLE surgery on the prevalence and severity of depression and estimate the incidence of de novo depression and suicide following surgery.

Long-term seizure and psychiatric outcomes following laser ablation of mesial temporal structures

OBJECTIVE

Postsurgical seizure outcome following laser interstitial thermal therapy (LiTT) for the management of drug-resistant mesial temporal lobe epilepsy (MTLE) has been limited to 2 years. Furthermore, its impact on presurgical mood and anxiety disorders has not been investigated. The objectives of this study were (1) to identify seizure outcome changes over a period ranging from 18 to 81 months; (2) to investigate the seizure-free rate in the last follow-up year; (3) to identify the variables associated with seizure freedom; and (4) to identify the impact of LiTT on presurgical mood and anxiety disorders.

METHODS

Medical records of all patients who underwent LiTT for MTLE from 2013 to 2019 at the University of Miami Comprehensive Epilepsy Center were retrospectively reviewed. Demographic, epilepsy-related, cognitive, psychiatric, and LiTT-related data were compared between seizure-free (Engel Class I) and non-seizure-free (Engel Class II + III + IV) patients. Statistical analyses included univariate and multivariate stepwise logistic regression analyses.

RESULTS

Forty-eight patients (mean age = 43 ± 14.2 years, range = 21-78) were followed for a mean period of 50 ± 20.7 months (range = 18-81); 29 (60.4%) achieved an Engel Class I outcome, whereas 11 (22.9%) had one to three seizures/year. Seizure-freedom rate decreased from 77.8% to 50% among patients with 24- and >61-month follow-up periods, respectively. In the last follow-up year, 83% of all patients were seizure-free. Seizure freedom was associated with having mesial temporal sclerosis (MTS), no presurgical focal to bilateral tonic-clonic seizures, and no psychopathology in the last follow-up year. Presurgical mood and/or anxiety disorder were identified in 30 patients (62.5%) and remitted after LiTT in 19 (62%).

SIGNIFICANCE

LiTT appears to be a safe and effective surgical option for treatment-resistant MTLE, particularly among patients with MTS. Remission of presurgical mood and anxiety disorders can also result from LiTT.

Automated Identification of Surgical Candidates and Estimation of Postoperative Seizure Freedom in Children - A Focused Review

Surgery is an effective but underused treatment for drug-resistant epilepsy in children. Algorithms to identify surgical candidates and estimate the likelihood of postoperative clinical improvement may be valuable to improve access to epilepsy surgery. We provide a focused review of these approaches. For adults with epilepsy, tools to identify surgical candidates and predict seizure and cognitive outcomes (Ie, Cases for Epilepsy (toolsforepilepsy.com) and Epilepsy Surgery Grading Scale) have been validated and are in use. Analogous tools for children need development. A promising approach is to apply statistical learning tools to clinical datasets, such as electroencephalogram tracings, imaging studies, and the text of clinician notes. Demonstration projects suggest these techniques have the potential to be highly accurate, and await further validation and clinical application.

Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings

OBJECTIVE

This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom.

METHODS

We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction.

RESULTS

Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes.

SIGNIFICANCE

This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.

Healthcare professionals' knowledge, attitude, and perception of epilepsy surgery: A systematic review

OBJECTIVE

The epilepsy surgery treatment gap is well defined and secondary to a broad range of issues, including healthcare professionals' (HCPs') knowledge, attitude, and perception (KAP) toward epilepsy surgery. However, no previous systematic reviews investigated this important topic.

METHODS

The systematic review was conducted according to Preferred Reporting Items for the Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We identified a total of 652 articles from multiple databases using database-specific queries and included 65 articles for full-text review after screening the titles and abstracts of the articles. Finally, we selected 11 papers for qualitative analysis. We critically appraised the quality of the studies using the Joanna Briggs critical appraisal tool.

RESULTS

The qualitative analysis of the content identified several key reasons causing healthcare professional-related barriers to epilepsy surgery: inadequate knowledge and awareness about the role of epilepsy surgery in drug-resistant epilepsy (DRE), poor identification and referral of patients with DRE, insufficient selection of candidates for presurgical workup, negative or ambivalent attitudes and perceptions regarding epilepsy surgery, deficient communication practices with patients regarding risk-benefit analysis of epilepsy surgery, and challenging coordination issues with the surgical referral. Neurologists with formal instruction in epilepsy, surgical exposure during training, participation in high volume epilepsy practice, or prior experience in surgical referral may refer more patients for surgical evaluation.

CONCLUSIONS

While significant work has been conducted in a limited number of studies to explore HCPs' knowledge gap and educational need regarding epilepsy surgery, further research is needed in defining the learning goals, assessing and validating specific learning gaps among providers, defining the learning outcomes, optimizing the educational format, content, and outcome measures, and appraising the achieved results following the educational intervention.

Underutilization of epilepsy surgery: Part II: Strategies to overcome barriers

Interventions focused on utilization of epilepsy surgery can be divided into groups: those that improve patients' access to surgical evaluation and those that facilitate completion of the surgical evaluation and treatment. Educational intervention, technological innovation, and effective coordination and communication can significantly improve patients' access to surgery. Patient and public facing, individualized (analog and/or digital) communication can raise awareness and acceptance of epilepsy surgery. Educational interventions aimed at providers may mitigate knowledge gaps using practical and concise consensus statements and guidelines, while specific training can improve awareness around implicit bias. Innovative technology, such as clinical decision-making toolkits within the electronic medical record (EMR), machine learning techniques, online decision-support tools, nomograms, and scoring algorithms can facilitate timely identification of appropriate candidates for epilepsy surgery with individualized guidance regarding referral appropriateness, postoperative seizure freedom rate, and risks of complication after surgery. There are specific strategies applicable for epilepsy centers' success: building a multidisciplinary setup, maintaining/tracking volume and complexity of cases, collaborating with other centers, improving surgical outcome with reduced complications, utilizing advanced diagnostics tools, and considering minimally invasive surgical techniques. Established centers may use other strategies, such as multi-stage procedures for multifocal epilepsy, advanced functional mapping with tailored surgery for epilepsy involving the eloquent cortex, and generation of fresh hypotheses in cases of surgical failure. Finally, improved access to epilepsy surgery can be accomplished with policy changes (e.g., anti-discrimination policy, exemption in transportation cost, telehealth reimbursement policy, patient-centered epilepsy care models, pay-per-performance models, affordability and access to insurance, and increased funding for research). Every intervention should receive regular evaluation and feedback-driven modification to ensure appropriate utilization of epilepsy surgery.