Publications by authors named "Wesley Kerr"

Objective: Randomized controlled trials (RCTs) are necessary to evaluate the efficacy of novel treatments for epilepsy. However, there have been concerning increases in the placebo responder rate over time. To understand these trends, we evaluated features associated with increased placebo responder rate.

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Artificial intelligence, machine learning, and deep learning are increasingly being used in all medical fields including for epilepsy research and clinical care. Already there have been resultant cutting-edge applications in both the clinical and research arenas of epileptology. Because there is a need to disseminate knowledge about these approaches, how to use them, their advantages, and their potential limitations, the goal of the 2023 Merritt-Putnam Symposium and of this synopsis review of that symposium has been to present the background and state of the art and then to draw conclusions on current and future applications of these approaches through the following: (1) Initially provide an explanation of the fundamental principles of artificial intelligence, machine learning, and deep learning.

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Article Synopsis
  • Structural epilepsy can result from brain injuries during early or late life and is linked to epilepsy due to malformations of cortical development.
  • A study of 70 adults found that those with later life brain injuries experienced longer and less frequent seizures compared to those with malformations or early life injuries.
  • The research showed that later life injuries resulted in different seizure types, with a higher frequency of focal to bilateral tonic-clonic seizures and lower rates of seizures starting in both hemispheres.
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Background: This study aims to understand diagnosis communication experiences and preferences of youths with functional seizures and their parents.

Methods: Semistructured interviews with youths and their parents from a tertiary care children's hospital were conducted separately. We confirmed the diagnosis of functional seizures with the youth's treating providers.

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Purpose: Myoclonus after anoxic brain injury is a marker of significant cerebral injury. Absent cortical signal (N20) on somatosensory evoked potentials (SSEPs) after cardiac arrest is a reliable predictor of poor neurological recovery when combined with an overall clinical picture consistent with severe widespread neurological injury. We evaluated a clinical question of if SSEP result could be predicted from other clinical and neurodiagnostic testing results in patients with post-anoxic myoclonus.

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  • Recent advancements in HIV prevention highlight the effectiveness of pre- and post-exposure prophylaxis (PrEP and PEP), particularly in the context of new anti-seizure medications (ASMs) that have emerged since 2012.
  • The review discusses the potential drug interactions between PrEP/PEP and various ASMs, particularly focusing on enzyme-inducing ASMs that may lower the effectiveness of tenofovir-containing treatments, as well as the impact of ritonavir on lamotrigine levels.
  • Clinicians are encouraged to be aware of these interactions and side effects, such as osteopenia and gastrointestinal issues, to provide better care for patients who might be at risk for both seizures and HIV infection.
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Machine learning (ML) methods are becoming more prevalent in the neurology literature as alternatives to traditional statistical methods to address challenges in the analysis of modern data sets. Despite the increase in the popularity of ML methods in neurology studies, some authors do not fully address all items recommended in reporting guidelines. The authors of this Research Methods article are members of the ® editorial board and have reviewed many studies using ML methods.

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Seizures have a profound impact on quality of life and mortality, in part because they can be challenging both to detect and forecast. Seizure detection relies upon accurately differentiating transient neurological symptoms caused by abnormal epileptiform activity from similar symptoms with different causes. Seizure forecasting aims to identify when a person has a high or low likelihood of seizure, which is related to seizure prediction.

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Background And Objectives: Participants with treatment-resistant epilepsy who are randomized to add-on placebo and remain in a trial for the typical 3 to 5-month maintenance period may be at increased risk of adverse outcomes. A novel trial design has been suggested, time to prerandomization monthly seizure count (T-PSC), which would limit participants' time on ineffective therapy. We reanalyzed 11 completed trials to determine whether the primary efficacy conclusions at T-PSC matched each of the original, longer trials.

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Article Synopsis
  • The objective of the study was to evaluate the safety and tolerability of a new trial design called time to exceed prerandomization monthly seizure count (T-PSC), which aims to minimize participant exposure to placebos in epilepsy trials.
  • Using this design, researchers analyzed treatment-emergent adverse events (TEAEs) from a trial on the drug perampanel for seizures.
  • The results indicated that nearly all TEAEs occurred before the T-PSC cutoff, showing that the trial design can effectively replicate safety conclusions, with fewer adverse events reported for the perampanel treatment compared to placebo.
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  • This study reviews the effectiveness of psychological interventions for functional/dissociative seizures (FDS) through a systematic analysis of data from 44 studies involving 1,300 patients.
  • The meta-analysis found a 40% seizure freedom rate at treatment's end and a 66% rate of ≥50% improvement in seizure frequency during treatment, indicating moderate positive effects from psychological therapies.
  • Despite improvements in seizure-related outcomes, none of the explored factors significantly affected seizure freedom, highlighting the need for more research on optimal measures to evaluate FDS severity.
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Background And Objectives: To evaluate the standardized mortality ratio (SMR) of patients in the United States referred to a multidisciplinary clinic for treatment of functional seizures.

Methods: We identified patients who had or had not died based on automated retrospective review of electronic health records from a registry of patients referred to a single-center multidisciplinary functional seizures treatment clinic. We calculated an SMR by comparing the number of observed deaths with the expected number of deaths in an age-matched, sex-matched, and race-matched population within the same state, and year records were available.

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Substantial efforts are underway toward optimizing the diagnosis, monitoring, and treatment of seizures and epilepsy. We describe preclinical programs in place for screening investigational therapeutic candidates in animal models, with particular attention to identifying and eliminating drugs that might paradoxically aggravate seizure burden. After preclinical development, we discuss challenges and solutions in the design and regulatory logistics of clinical trial execution, and efforts to develop disease biomarkers and interventions that may be not only seizure-suppressing, but also disease-modifying.

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Purpose Of Review: Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

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Article Synopsis
  • Diagnosis of functional seizures, or psychogenic nonepileptic seizures, begins with patient interviews and descriptions of the seizures.* -
  • A focused evaluation can help distinguish functional seizures from other conditions, including epileptic seizures, using patient and witness accounts as well as seizure videos.* -
  • The review highlights the importance of understanding data limitations and how the expertise of healthcare providers affects the accuracy of diagnosis.*
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Background And Objectives: Projections from recent studies suggest that by 2025, there will not be enough neurologists to meet the demand in 41 states. In this study, we investigate the financial impact and improved access to care for persons with epilepsy that is possible by implementing a multidisciplinary treatment clinic for persons with functional seizures (FS), previously referred to as psychogenic nonepileptic seizures, thus separating those patients out of an epilepsy clinic.

Methods: This observational retrospective study used real-time data of 156 patients referred to an FS clinic integrated into a tertiary care epilepsy center to simulate its effect on epilepsy division access and finances.

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Objective: In this post hoc analysis of a subset of patients from a long-term, open-label phase 3 study, we assessed ≥50%, ≥75%, ≥90%, and 100% seizure reduction and sustainability of these responses with cenobamate using a time-to-event analytical approach.

Methods: Of 240 patients with uncontrolled focal seizures who had adequate seizure data available, 214 completed the 12-week titration phase and received ≥1 dose of cenobamate in the maintenance phase (max dose 400 mg/day) and were included in this post hoc analysis. Among patients who met an initial given seizure-reduction level (≥50%, ≥75%, ≥90%, or 100%), sustainability of that response was measured using a time-to-event methodology.

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Objective: This study was undertaken to evaluate how the challenges in the recruitment and retention of participants in clinical trials for focal onset epilepsy have changed over time.

Methods: In this systematic analysis of randomized clinical trials of adjunct antiseizure medications for medication-resistant focal onset epilepsy, we evaluated how the numbers of participants, sites, and countries have changed since the first such trial in 1990. We also evaluated the proportion of participants who completed each trial phase and their reasons for early trial exit.

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Background: Functional seizures (FS) are paroxysmal episodes, resembling epileptic seizures, but without underlying epileptic abnormality. The aetiology and neuroanatomic associations are incompletely understood. Recent brain imaging data indicate cerebral changes, however, without clarifying possible pathophysiology.

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Objective: We assessed mortality, sudden unexpected death in epilepsy (SUDEP), and standardized mortality ratio (SMR) among adults treated with cenobamate during the cenobamate clinical development program.

Methods: We retrospectively analyzed deaths among all adults with uncontrolled focal (focal to bilateral tonic-clonic [FBTC], focal impaired awareness, focal aware) or primary generalized tonic-clonic (PGTC) seizures who received ≥1 dose of adjunctive cenobamate in completed and ongoing phase 2 and 3 clinical studies. In patients with focal seizures from completed studies, median baseline seizure frequencies ranged from 2.

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Well-designed placebo-controlled clinical trials are critical to the development of novel treatments for epilepsy, but their design has not changed for decades. Patients, clinicians, regulators, and innovators all have concerns that recruiting for trials is challenging, in part, due to the static design of maintaining participants for long periods on add-on placebo when there are an increasing number of options for therapy. A traditional trial maintains participants on blinded treatment for a static period (e.

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Selection criteria for clinical trials for medication-resistant epilepsy are used to limit variability and to ensure safety. However, it has become more challenging to recruit subjects for trials. This study investigated the impact of each inclusion and exclusion criterion on medication-resistant epilepsy clinical trial recruitment at a large academic epilepsy center.

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Objective: Choosing candidates for antiseizure medication (ASM) withdrawal in well-controlled epilepsy is challenging. We evaluated (a) the correlation between neurologists' seizure risk estimation ("clinician predictions") vs calculated predictions, (b) how viewing calculated predictions influenced recommendations, and (c) barriers to using risk calculation.

Methods: We asked US and European neurologists to predict 2-year seizure risk after ASM withdrawal for hypothetical vignettes.

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