Medicine (RMH) - Research Publications
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Item18F-FDG-PET hypometabolism as a predictor of favourable outcome in epilepsy surgery: protocol for a systematic review and meta-analysis(BMJ PUBLISHING GROUP, 2022-10)INTRODUCTION: A substantial proportion of patients who undergo surgery for drug resistant focal epilepsy do not become seizure free. While some factors, such as the detection of hippocampal sclerosis or a resectable lesion on MRI and electroencephalogram-MRI concordance, can predict favourable outcomes in epilepsy surgery, the prognostic value of the detection of focal hypometabolism with 18F-fluorodeoxyglucose positive emission tomography (18F-FDG-PET) hypometabolism is uncertain. We propose a protocol for a systematic review and meta-analysis to examine whether localisation with 18F-FDG-PET hypometabolism predicts favourable outcomes in epilepsy surgery. METHODS AND ANALYSIS: A systematic literature search of Medline, Embase and Web of Science will be undertaken. Publications which include evaluation with 18F-FDG-PET prior to surgery for drug resistant focal epilepsy, and which report ≥12 months of postoperative surgical outcome data will be included. Non-human, non-English language publications, publications with fewer than 10 participants and unpublished data will be excluded. Screening and full-text review of publications for inclusion will be undertaken by two independent investigators, with discrepancies resolved by consensus or a third investigator. Data will be extracted and pooled using random effects meta-analysis, with heterogeneity quantified using the I2 analysis. ETHICS AND DISSEMINATION: Ethics approval is not required. Once complete, the systematic review will be published in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022324823.
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ItemNo Preview AvailableAutomated Interictal Epileptiform Discharge Detection from Scalp EEG Using Scalable Time-series Classification Approaches(WORLD SCIENTIFIC PUBL CO PTE LTD, 2023-01)Deep learning for automated interictal epileptiform discharge (IED) detection has been topical with many published papers in recent years. All existing works viewed EEG signals as time-series and developed specific models for IED classification; however, general time-series classification (TSC) methods were not considered. Moreover, none of these methods were evaluated on any public datasets, making direct comparisons challenging. This paper explored two state-of-the-art convolutional-based TSC algorithms, InceptionTime and Minirocket, on IED detection. We fine-tuned and cross-evaluated them on a public (Temple University Events - TUEV) and two private datasets and provided ready metrics for benchmarking future work. We observed that the optimal parameters correlated with the clinical duration of an IED and achieved the best area under precision-recall curve (AUPRC) of 0.98 and F1 of 0.80 on the private datasets, respectively. The AUPRC and F1 on the TUEV dataset were 0.99 and 0.97, respectively. While algorithms trained on the private sets maintained their performance when tested on the TUEV data, those trained on TUEV could not generalize well to the private data. These results emerge from differences in the class distributions across datasets and indicate a need for public datasets with a better diversity of IED waveforms, background activities and artifacts to facilitate standardization and benchmarking of algorithms.
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ItemThe effect of epilepsy surgery on productivity: A systematic review and meta-analysis(WILEY, 2022-04)OBJECTIVES: An important but understudied benefit of resective epilepsy surgery is improvement in productivity; that is, people's ability to contribute to society through participation in the workforce and in unpaid roles such as carer duties. Here, we aimed to evaluate productivity in adults with drug-resistant epilepsy (DRE) pre- and post-resective epilepsy surgery, and to explore the factors that positively influence productivity outcomes. METHODS: We conducted a systematic review and meta-analysis using four electronic databases: Medline (Ovid), EMBASE (Ovid), EBM Reviews - Cochrane Central Register of Controlled Trials (CENTRAL), and Cochrane Library. All studies over the past 30 years reporting on pre- and post-resective epilepsy surgical outcomes in adults with DRE were eligible for inclusion. Meta-analysis was performed to assess the post-surgery change in employment outcomes. RESULTS: A total of 1005 titles and abstracts were reviewed. Seventeen studies, comprising 2056 unique patients, were suitable for the final quantitative synthesis and meta-analysis. Resective epilepsy surgery resulted in a 22% improvement in overall productivity (95% confidence interval [CI]: 1.07-1.40). The factors associated with increased post-surgery employment risk ratios were lower pre-surgical employment in the workforce (relative risk ratio [RRR] =0.34; 95% CI: 0.15-0.74), shorter follow-up duration (RRR = 0.95; 95% CI: 0.90-0.99), and lower mean age at time of surgery (RRR= 0.97; 95% CI: 0.94-0.99). The risk of bias of the included studies was assessed using Risk Of Bias In Non-randomised Studies - of Interventions and was low for most variables except "measurement of exposure." SIGNIFICANCE: There is clear evidence that resective surgery in eligible surgical DRE patients results in improved productivity. Future work may include implementing a standardized method for collecting and reporting productivity in epilepsy cohorts and focusing on ways to reprioritize health care resource allocation to allow suitable candidates to access surgery earlier. This will ultimately benefit individuals with DRE, their families, our communities, and the wider health care system.
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ItemNo Preview AvailableDeep learning for automated epileptiform discharge detection from scalp EEG: A systematic review(IOP Publishing Ltd, 2022-10-01)Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine learning methods at the forefront in recent years. As computational resources become more accessible, researchers have applied deep learning (DL) to IED detection with promising results. This systematic review aims to provide an overview of the current DL approaches to automated IED detection from scalp electroencephalography (EEG) and establish recommendations for the clinical research community. We conduct a systematic review according to the PRISMA guidelines. We searched for studies published between 2012 and 2022 implementing DL for automating IED detection from scalp EEG in major medical and engineering databases. We highlight trends and formulate recommendations for the research community by analyzing various aspects: data properties, preprocessing methods, DL architectures, evaluation metrics and results, and reproducibility. The search yielded 66 studies, and 23 met our inclusion criteria. There were two main DL networks, convolutional neural networks in 14 studies and long short-term memory networks in three studies. A hybrid approach combining a hidden Markov model with an autoencoder was employed in one study. Graph convolutional network was seen in one study, which considered a montage as a graph. All DL models involved supervised learning. The median number of layers was 9 (IQR: 5-21). The median number of IEDs was 11 631 (IQR: 2663-16 402). Only six studies acquired data from multiple clinical centers. AUC was the most reported metric (median: 0.94; IQR: 0.94-0.96). The application of DL to IED detection is still limited and lacks standardization in data collection, multi-center testing, and reporting of clinically relevant metrics (i.e. F1, AUCPR, and false-positive/minute). However, the performance is promising, suggesting that DL might be a helpful approach. Further testing on multiple datasets from different clinical centers is required to confirm the generalizability of these methods.
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ItemNo Preview AvailableMultidimensional psychopathological profile differences between patients with psychogenic nonepileptic seizures and epileptic seizure disorders(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2022-10)OBJECTIVE: Early differential diagnosis of psychogenic nonepileptic seizures (PNES) and epileptic seizures (ES) remains difficult. Self-reported psychopathology is often elevated in patients with PNES, although relatively few studies have examined multiple measures of psychopathology simultaneously. This study aimed to identify differences in multidimensional psychopathology profiles between PNES and ES patient groups. METHOD: This was a retrospective case-control study involving patients admitted for video-EEG monitoring (VEM) over a two-year period. Clinicodemographic variables and psychometric measures of depression, anxiety, dissociation, childhood trauma, maladaptive personality traits, and cognition were recorded. Diagnosis of PNES or ES was determined by multidisciplinary assessment and consensus opinion. General linear mixed models (GLMMs) were used to investigate profile differences between diagnostic groups across psychometric measures. A general psychopathology factor was then computed using principal components analysis (PCA) and differences between groups in this 'p' factor were investigated. RESULTS: 261 patients (77 % with ES and 23 % with PNES) were included in the study. The PNES group endorsed greater symptomatology with GLMM demonstrating a significant main effect of group (η2p = 0.05) and group by measure interaction (η2p = 0.03). Simple effects analysis indicated that the PNES group had particularly elevated scores for childhood trauma (β = 0.78), dissociation (β = 0.70), and depression (β = 0.60). There was a high correlation between psychopathology measures, with a single p factor generated to explain 60 % variance in the psychometric scores. The p factor was elevated in the PNES group (β = 0.61). ROC curve analysis indicated that these psychometric measures had limited usefulness when considered individually (AUC range = 0.63-0.69). CONCLUSION: Multidimensional psychopathological profile differences exist between patients with PNES and ES. Patients with PNES report more psychopathology overall, with particular elevations in childhood trauma, dissociation, and depression. Although not suitable to be used as a standalone screening tool to differentiate PNES and ES, understanding of these profiles at a construct level might help triage patients and guide further psychiatric examination and enquiry.
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ItemNo Preview AvailablePsychiatric symptoms are the strongest predictors of quality of life in patients with drug-resistant epilepsy or psychogenic nonepileptic seizures: Authors' response(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2022-01)
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ItemNo Preview AvailableIdentification of factors associated with new-onset vascular disease in patients admitted for video-EEG monitoring: A longitudinal cohort study(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2022-10)OBJECTIVE: People with epilepsy are at a higher risk of developing vascular disease. Understanding the risk factors in observational studies is hampered by the challenge in separating epilepsy-related risk and treatment-related risk, and uncertainty in the epilepsy diagnosis. This study aimed to identify factors associated with risk of subsequent vascular disease in patients with video-EEG monitoring (VEM) confirmed epilepsy. METHODS: We included patients with a diagnosis of epilepsy and nonepileptic disorders between January 1, 1995, and December 31, 2015. Incident cardiovascular, cerebrovascular, and peripheral vascular disease was determined by linkage to a state-wide hospital admissions database between 1st July 1994 and 28th February 2018. Incidence was compared with the general population. RESULTS: 1728 patients (59.7% female, median age 35 years) underwent VEM, and were followed up for a median of 9.2 years (range 2.2-22.9 years). Eight-hundred and thirty -two were diagnosed with epilepsy and 896 nonepileptic disorders. The incidence of cerebrovascular disease was higher in both patients with epilepsy (incidence rate ratio [IRR] 1.78, p = 0.001) and with nonepileptic disorders (IRR 1.61, p < 0.001) than in the general population. Patients who took valproic acid (VPA) were at a lower risk of vascular disease than those taking enzyme-inducing antiseizure medications (EIASM, subdistribution hazard ratio [SHR] 0.42, p = 0.013), and those taking neither VPA nor EIASM (SHR 0.47, p = 0.03). There was no difference in the incidence of vascular disease between patients with epilepsy and those without epilepsy (SHR 0.94, p = 0.766). Factors associated with increased risk included age (SHR 1.04, p < 0.001), male sex (SHR 1.50, p = 0.017), and smoking (SHR 1.68, p = 0.017). SIGNIFICANCE: In this study, both patients with epilepsy and without epilepsy had increased vascular risk. This suggests that the increased risk may be in part due to factors not directly related to epilepsy, such as EIASM use and vascular risk factors.
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ItemNo Preview AvailableDiagnostic delay in focal epilepsy: Association with brain pathology and age(W B SAUNDERS CO LTD, 2022-03)PURPOSE: Between 16-77% of patients with newly diagnosed epilepsy report seizures before diagnosis but little is known about the risk factors for diagnostic delay. Here, we examined the association between prior seizures and neuroimaging findings in newly diagnosed focal epilepsy. METHODS: Adults diagnosed with focal epilepsy at First Seizure Clinics (FSC) at the Royal Melbourne Hospital or Austin Health, Melbourne, Australia, between 2000 and 2010 were included. Medical records were audited for seizure history accrued from the detailed FSC interview. Potentially epileptogenic brain abnormality type, location and extent was determined from neuroimaging. Statistical analysis comprised multivariate logistic regression. RESULTS: Of 735 patients, 44% reported seizure/s before the index seizure. Among the 260 individuals with a potentially epileptogenic brain imaging abnormality, 34% reported prior seizures. Of 475 individuals with no abnormality, 50% reported prior seizures (p < 0.001). Patients with post-stroke changes had lower odds of prior seizures (n = 24/95, OR 0.5, p = 0.005) compared to patients without abnormalities, as did patients with high-grade tumors (n = 1/10, OR 0.1, p = 0.04). Abnormality location or extent was not associated with seizures. Prior seizures were inversely associated with age, patients aged >50 years had lower odds compared to those 18-30 years (OR 0.5, p = 0.01). CONCLUSIONS: A history of prior seizures is less common in patients with newly diagnosed focal epilepsy associated with antecedent stroke or high-grade tumor than in those without a lesion, and is also less common in older individuals. These findings may be related to age, biological mechanisms or aspects of diagnosis and assessment of these events.
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ItemAutomated seizure detection with noninvasive wearable devices: A systematic review and meta-analysis(WILEY, 2022-08)OBJECTIVE: This study was undertaken to review the reported performance of noninvasive wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures (PNES). METHODS: We conducted a systematic review and meta-analysis of studies reported up to November 15, 2021. We included studies that used video-electroencephalographic (EEG) monitoring as the gold standard to determine the sensitivity and false alarm rate (FAR) of noninvasive wearables for automated seizure detection. RESULTS: Twenty-eight studies met the criteria for the systematic review, of which 23 were eligible for meta-analysis. These studies (1269 patients in total, median recording time = 52.9 h per patient) investigated devices for tonic-clonic seizures using wrist-worn and/or ankle-worn devices to measure three-dimensional accelerometry (15 studies), and/or wearable surface devices to measure electromyography (eight studies). The mean sensitivity for detecting tonic-clonic seizures was .91 (95% confidence interval [CI] = .85-.96, I2 = 83.8%); sensitivity was similar between the wrist-worn (.93) and surface devices (.90). The overall FAR was 2.1/24 h (95% CI = 1.7-2.6, I2 = 99.7%); FAR was higher in wrist-worn (2.5/24 h) than in wearable surface devices (.96/24 h). Three of the 23 studies also detected PNES; the mean sensitivity and FAR from these studies were 62.9% and .79/24 h, respectively. Four studies detected both focal and tonic-clonic seizures, and one study detected focal seizures only; the sensitivities ranged from 31.1% to 93.1% in these studies. SIGNIFICANCE: Reported noninvasive wearable devices had high sensitivity but relatively high FARs in detecting tonic-clonic seizures during limited recording time in a video-EEG setting. Future studies should focus on reducing FAR, detection of other seizure types and PNES, and longer recording in the community.
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ItemGlutamate weighted imaging contrast in gliomas with 7 Tesla magnetic resonance imaging(ELSEVIER SCI LTD, 2019)INTRODUCTION: Diffuse gliomas are incurable malignancies, which undergo inevitable progression and are associated with seizure in 50-90% of cases. Glutamate has the potential to be an important glioma biomarker of survival and local epileptogenicity if it can be accurately quantified noninvasively. METHODS: We applied the glutamate-weighted imaging method GluCEST (glutamate chemical exchange saturation transfer) and single voxel MRS (magnetic resonance spectroscopy) at 7 Telsa (7 T) to patients with gliomas. GluCEST contrast and MRS metabolite concentrations were quantified within the tumour region and peritumoural rim. Clinical variables of tumour aggressiveness (prior adjuvant therapy and previous radiological progression) and epilepsy (any prior seizures, seizure in last month and drug refractory epilepsy) were correlated with respective glutamate concentrations. Images were separated into post-hoc determined patterns and clinical variables were compared across patterns. RESULTS: Ten adult patients with a histo-molecular (n = 9) or radiological (n = 1) diagnosis of grade II-III diffuse glioma were recruited, 40.3 +/- 12.3 years. Increased tumour GluCEST contrast was associated with prior adjuvant therapy (p = .001), and increased peritumoural GluCEST contrast was associated with both recent seizures (p = .038) and drug refractory epilepsy (p = .029). We distinguished two unique GluCEST contrast patterns with distinct clinical and radiological features. MRS glutamate correlated with GluCEST contrast within the peritumoural voxel (R = 0.89, p = .003) and a positive trend existed in the tumour voxel (R = 0.65, p = .113). CONCLUSION: This study supports the role of glutamate in diffuse glioma biology. It further implicates elevated peritumoural glutamate in epileptogenesis and altered tumour glutamate homeostasis in glioma aggressiveness. Given the ability to non-invasively visualise and quantify glutamate, our findings raise the prospect of 7 T GluCEST selecting patients for individualised therapies directed at the glutamate pathway. Larger studies with prospective follow-up are required.