Dataset Information

Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling.

ABSTRACT: SEE EISSA AND SCHEVON DOI101093/AWW332 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these patients more effectively. Dynamical computational models, informed with patient data, can be used to make predictions and give mechanistic insight. In this study, we develop patient-specific dynamical network models of epileptogenic cortex. We infer the network connectivity matrix from non-seizure electrographic recordings of patients and use these connectivity matrices as the network structure in our model. The model simulates the dynamics of a bi-stable switch at every node in this network, meaning that every node starts in a background state, but has the ability to transit to a co-existing seizure state. Whether a transition happens in a node is partly determined by the stochastic nature of the input to the node, but also by the input the node receives from other connected nodes in the network. By conducting simulations with such a model, we can detect the average transition time for nodes in a given network, and therefore define nodes with a short transition time as highly epileptogenic. In a retrospective study, we found that in some patients the regions with high epileptogenicity in the model overlap with those identified clinically as the seizure onset zone. Moreover, it was found that the resection of these regions in the model reduces the overall likelihood of a seizure. Following removal of these regions in the model, we predicted surgical outcomes and compared these to actual patient outcomes. Our predictions were found to be 81.3% accurate on a dataset of 16 patients with intractable epilepsy. Intriguingly, in patients with unsuccessful outcomes, the proposed computational approach is able to suggest alternative resection sites. The model presented here gives mechanistic insight as to why surgery may be unsuccessful in some patients. This may aid clinicians in presurgical evaluation by providing a tool to explore various surgical options, offering complementary information to existing clinical techniques.

SUBMITTER: Sinha N

PROVIDER: S-EPMC5278304 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling.

Sinha Nishant N Dauwels Justin J Kaiser Marcus M Cash Sydney S SS Brandon Westover M M Wang Yujiang Y Taylor Peter N PN

Brain : a journal of neurology 20161223 2

SEE EISSA AND SCHEVON DOI101093/AWW332 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Surgery can be a last resort for patients with intractable, medically refractory epilepsy. For many of these patients, however, there is substantial risk that the surgery will be ineffective. The prediction of who is likely to benefit from a surgical approach is crucial for being able to inform patients better, conduct principled prospective clinical trials, and ultimately tailor therapeutic approaches to these p ...[more]

PMID: 28011454

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Project description:Foot orthoses are prescribed to reduce forefoot plantar pressures and pain in people with rheumatoid arthritis. Computational modelling can assess how the orthoses affect internal tissue stresses, but previous studies have focused on a single healthy individual. This study aimed to ascertain whether simplified forefoot models would produce differing biomechanical predictions at the orthotic interface between people with rheumatoid arthritis of varying severity, and in comparison to a healthy control. The forefoot models were developed from magnetic resonance data of 13 participants with rheumatoid arthritis and one healthy individual. Measurements of bony morphology and soft tissue thickness were taken to assess deformity. These were compared to model predictions (99th% shear strain and plantar pressure, max. pressure gradient, volume of soft tissue over 10% shear strain), alongside clinical data including body mass index and Leeds Foot Impact Scale-Impairment/Footwear score (LFIS-IF). The predicted pressure and shear strain for the healthy participant fell at the lower end of the rheumatoid models' range. Medial first metatarsal head curvature moderately correlated to all model predicted outcomes (0.529 < r < 0.574, 0.040 < p < 0.063). BMI strongly correlated to all model predictions except pressure gradients (0.600 < r < 0.652, p < 0.05). There were no apparent relationships between model predictions and instances of bursae, erosion and synovial hypertrophy or LFIS-IF score. The forefoot models produced differing biomechanical predictions between a healthy individual and participants with rheumatoid arthritis, and between individuals with rheumatoid arthritis. Models capable of predicting subject specific biomechanical orthotic interactions could be used in the future to inform more personalised devices to protect skin and soft tissue health. While the model results did not clearly correlate with all clinical measures, there was a wide range in model predictions and morphological measures across the participants. Thus, the need for assessment of foot orthoses across a population, rather than for one individual, is clear.

Project description:ObjectiveTo gather real-world evidence on antiseizure medications (ASMs) treatment patterns and related outcomes in patients with drug-resistant focal epilepsy.MethodsMedical insurance claims from the start of 2014 till the end of 2019 were used. Patient selection criteria included International Classification of Diseases (ICD) codes followed by documented ASM use. Baseline patient demographics along with ASM and rescue medication use patterns and related patient outcome were documented for first (index) ASM regimen. Patients who failed the first regimen and then failed the second regimen were considered drug resistant. Multivariate analyses were performed to identify risks and other characteristics for positive or negative treatment outcomes.ResultsStudy cohort consisted of 46 474 patients with a mean age of 47.23 (SD: 16.94). Levetiracetam was the most first-encountered ASM (37.94%). At baseline, 87.14% were treated with ASMs prior to having study-confirmed diagnoses. Mental comorbidities were present in 37.86% of patients. After first-year ASM treatment, 34.61% of patients persisted on their index regimen and 5.91% were seizure-free. Patients failing first ASM regimen numbered 12 868 (27.69%). Drug-resistant patients who failed first and then second ASM regimens numbered 6335 (49.23%). Percentages of patients who had successful second treatment and seizure-free were 21.32 and 3.65, respectively. Initiating patients on lamotrigine or carbamazepine (relative to levetiracetam), baseline use of index ASM, rescue medications, and older age or male gender all lowered the risk for treatment failure. Having higher comorbidity, comorbid mental illness, headache, or neoplasty increased such a risk. Baseline use of index ASM, depressive episode, or anxiety disorder all entailed higher risk of failing second ASM treatment.SignificanceOverall, reported findings indicated that patient history at baseline and the early selection of an ASM all influenced treatment outcomes. Findings pointed to the complex nature of ASM treatment in drug-resistant focal epilepsy patients calling for additional research to identify the optimal treatment to achieve beneficial patient outcomes.

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Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling.

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Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling.

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