Medicine (RMH) - Research Publications

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    The effect of epilepsy surgery on productivity: A systematic review and meta-analysis
    Siriratnam, P ; Foster, E ; Shakhatreh, L ; Neal, A ; Carney, PW ; Jackson, GD ; O'Brien, TJ ; Kwan, P ; Chen, Z ; Ademi, Z (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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    Identification of factors associated with new-onset vascular disease in patients admitted for video-EEG monitoring: A longitudinal cohort study
    Nicolo, J-P ; Chen, Z ; Nightscales, R ; O'Brien, TJ ; Kwan, P (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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    Automated seizure detection with noninvasive wearable devices: A systematic review and meta-analysis
    Naganur, V ; Sivathamboo, S ; Chen, Z ; Kusmakar, S ; Antonic-Baker, A ; O'Brien, TJ ; Kwan, P (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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    Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy
    Sinclair, B ; Cahill, V ; Seah, J ; Kitchen, A ; Vivash, LE ; Chen, Z ; Malpas, CB ; O'Shea, MF ; Desmond, PM ; Hicks, RJ ; Morokoff, AP ; King, JA ; Fabinyi, GC ; Kaye, AH ; Kwan, P ; Berkovic, SF ; Law, M ; O'Brien, TJ (WILEY, 2022-05)
    OBJECTIVES: Around 30% of patients undergoing surgical resection for drug-resistant mesial temporal lobe epilepsy (MTLE) do not obtain seizure freedom. Success of anterior temporal lobe resection (ATLR) critically depends on the careful selection of surgical candidates, aiming at optimizing seizure freedom while minimizing postoperative morbidity. Structural MRI and FDG-PET neuroimaging are routinely used in presurgical assessment and guide the decision to proceed to surgery. In this study, we evaluate the potential of machine learning techniques applied to standard presurgical MRI and PET imaging features to provide enhanced prognostic value relative to current practice. METHODS: Eighty two patients with drug resistant MTLE were scanned with FDG-PET pre-surgery and T1-weighted MRI pre- and postsurgery. From these images the following features of interest were derived: volume of temporal lobe (TL) hypometabolism, % of extratemporal hypometabolism, presence of contralateral TL hypometabolism, presence of hippocampal sclerosis, laterality of seizure onset volume of tissue resected and % of temporal lobe hypometabolism resected. These measures were used as predictor variables in logistic regression, support vector machines, random forests and artificial neural networks. RESULTS: In the study cohort, 24 of 82 (28.3%) who underwent an ATLR for drug-resistant MTLE did not achieve Engel Class I (i.e., free of disabling seizures) outcome at a minimum of 2 years of postoperative follow-up. We found that machine learning approaches were able to predict up to 73% of the 24 ATLR surgical patients who did not achieve a Class I outcome, at the expense of incorrect prediction for up to 31% of patients who did achieve a Class I outcome. Overall accuracies ranged from 70% to 80%, with an area under the receiver operating characteristic curve (AUC) of .75-.81. We additionally found that information regarding overall extent of both total and significantly hypometabolic tissue resected was crucial to predictive performance, with AUC dropping to .59-.62 using presurgical information alone. Incorporating the laterality of seizure onset and the choice of machine learning algorithm did not significantly change predictive performance. SIGNIFICANCE: Collectively, these results indicate that "acceptable" to "good" patient-specific prognostication for drug-resistant MTLE surgery is feasible with machine learning approaches utilizing commonly collected imaging modalities, but that information on the surgical resection region is critical for optimal prognostication.
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    Asymmetric distribution of enlarged perivascular spaces in centrum semiovale may be associated with epilepsy after acute ischemic stroke
    Yu, N ; Sinclair, B ; Posada, LMG ; Chen, Z ; Di, Q ; Lin, X ; Kolbe, S ; Hlauschek, G ; Kwan, P ; Law, M (WILEY, 2022-03)
    OBJECTIVE: To investigate the factors influencing enlarged perivascular space (EPVS) characteristics at the onset of acute ischemic stroke (AIS), and whether the PVS characteristics can predict later post-stroke epilepsy (PSE). METHODS: A total of 312 patients with AIS were identified, of whom 58/312 (18.6%) developed PSE. Twenty healthy participants were included as the control group. The number of PVS in the basal ganglia (BG), centrum semiovale (CS), and midbrain (MB) was manually calculated on T2 -weighted MRI. The scores and asymmetry index (AI) of EPVS in each region were compared among the enrolled participants. Other potential risk factors for PSE were also analyzed, including NIHSS at admission and stroke etiologies. RESULTS: The EPVS scores were significantly higher in the bilateral BG and CS of AIS patients compared to those of the control group (both p < 0.01). No statistical differences in EPVS scores in BG, CS, and MB were obtained between the PSE group and the nonepilepsy AIS group (all p > 0.01). However, markedly different AI scores in CS were found between the PSE group and the nonepilepsy AIS group (p = 0.004). Multivariable analysis showed that high asymmetry index of EPVS (AI≥0.2) in CS was an independent predictor for PSE (OR = 3.7, 95% confidence interval 1.5-9.1, p = 0.004). CONCLUSIONS: Asymmetric distribution of EPVS in CS may be an independent risk factor and a novel imaging biomarker for the development of PSE. Further studies to understand the mechanisms of this association and confirmation with larger patient populations are warranted.
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    Prospective multisite cohort study of patient-reported outcomes in adults with new-onset seizures
    Foster, E ; Chen, Z ; Vaughan, DN ; Tailby, C ; Carney, PW ; D'Souza, W ; Yong, HMA ; Nicolo, J-P ; Pellinen, J ; de Albornoz, SC ; Liew, D ; O'Brien, TJ ; Kwan, P ; Ademi, Z (WILEY, 2022-03)
    OBJECTIVE: New-onset seizures affect up to 10% of people over their lifetime, however, their health economic impact has not been well-studied. This prospective multicenter study will collect patient-reported outcome measures (PROMs) from adults with new-onset seizures seen in six Seizure Clinics across Melbourne, Australia and The University of Colorado, USA. METHODS: Approximately 450 eligible patients will be enrolled in the study at or following their initial attendance to Seizure Clinics at the study hospitals. Inclusion criteria for the study group are those with new-onset acute symptomatic seizures, new-onset unprovoked seizures, and new-onset epilepsy. Inclusion criteria for the three comparator groups are those with noncardiac syncope, those with psychogenic nonepileptic seizures, as well as published PROMs data from the Australian general population. Exclusion criteria are those aged less than 18 years, those with a preexisting epilepsy diagnosis, and those with intellectual disabilities or other impairments which would preclude them from comprehending and completing the questionnaires. Patients will complete eight online questionnaires regarding the effect that their seizures (or seizure mimics) have had on various aspects of their life. These questionnaires will be readministered at 6 and 12 months. Patients with new-diagnosis epilepsy will also be asked to share the reasons why they have accepted or declined antiseizure medications. ANALYSIS: Primary outcome measures will be quality of life, work productivity, informal care needs, and mood, at baseline compared to 6 and 12 months later for those with new-onset seizures and comparing these outcomes to those in the three comparator groups. Secondary outcomes include mapping of QoLIE-31 to the EQ-5D-5L in epilepsy, modelling indirect costs of new-onset seizures, and exploring why patients may or may not wish to take antiseizure medications. SIGNIFICANCE: These data will form an evidence-base for future studies that examine the effectiveness of various healthcare interventions for new-onset seizure patients. ETHICS AND DISSEMINATION: This study is approved by the Alfred Health Human Research Ethics Committee (SERP: 52 538, Alfred HREC: 307/19), the Austin Health Human Research Ethics Committee (HREC/59148/Austin-2019), and the Colorado Multiple Institutional Review Board (COMIRB) (COMIRB #20-3028). ANZCTR TRIAL REGISTRATION NUMBER: ACTRN12621000908831.