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Abstract
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.
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Affiliation(s)
| | - Lara Jehi
- Epilepsy Center, Neurological Institute
- Center for Computational Life Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA
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2
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Eriksson MH, Ripart M, Piper RJ, Moeller F, Das KB, Eltze C, Cooray G, Booth J, Whitaker KJ, Chari A, Martin Sanfilippo P, Perez Caballero A, Menzies L, McTague A, Tisdall MM, Cross JH, Baldeweg T, Adler S, Wagstyl K. Predicting seizure outcome after epilepsy surgery: Do we need more complex models, larger samples, or better data? Epilepsia 2023; 64:2014-2026. [PMID: 37129087 PMCID: PMC10952307 DOI: 10.1111/epi.17637] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/30/2023] [Accepted: 05/01/2023] [Indexed: 05/03/2023]
Abstract
OBJECTIVE The accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if (1) training more complex models, (2) recruiting larger sample sizes, or (3) using data-driven selection of clinical predictors would improve our ability to predict postoperative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict postoperative seizure outcome. METHODS We performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models-a logistic regression, a multilayer perceptron, and an XGBoost model-to predict 1-year postoperative seizure outcome on our data set. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up to N = 2000. Finally, we examined the impact of predictor selection on model performance. RESULTS Our logistic regression achieved an accuracy of 72% (95% confidence interval [CI] = 68%-75%, area under the curve [AUC] = .72), whereas our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP = 67%-74%, AUCMLP = .70; 95% CIXGBoost own = 68%-75%, AUCXGBoost own = .70). There was no significant difference in performance between our three models (all p > .4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI = 59%-67%, AUC = .62; pLR = .005, pMLP = .01, pXGBoost own = .01) on our data. All models showed improved performance with increasing sample size, but limited improvements beyond our current sample. The best model performance was achieved with data-driven feature selection. SIGNIFICANCE We show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict postoperative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.
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Affiliation(s)
- Maria H. Eriksson
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeuropsychologyGreat Ormond Street HospitalLondonUK
- Department of NeurologyGreat Ormond Street HospitalLondonUK
- The Alan Turing InstituteLondonUK
| | - Mathilde Ripart
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
| | - Rory J. Piper
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
| | | | - Krishna B. Das
- Department of NeurologyGreat Ormond Street HospitalLondonUK
- Department of NeurophysiologyGreat Ormond Street HospitalLondonUK
| | - Christin Eltze
- Department of NeurophysiologyGreat Ormond Street HospitalLondonUK
| | - Gerald Cooray
- Department of NeurophysiologyGreat Ormond Street HospitalLondonUK
- Clinical NeuroscienceKarolinska InstituteSolnaSweden
| | - John Booth
- Digital Research EnvironmentGreat Ormond Street HospitalLondonUK
| | | | - Aswin Chari
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
| | - Patricia Martin Sanfilippo
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeuropsychologyGreat Ormond Street HospitalLondonUK
| | | | - Lara Menzies
- Department of Clinical GeneticsGreat Ormond Street HospitalLondonUK
| | - Amy McTague
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurologyGreat Ormond Street HospitalLondonUK
| | - Martin M. Tisdall
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
| | - J. Helen Cross
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurologyGreat Ormond Street HospitalLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
- Young EpilepsyLingfieldUK
| | - Torsten Baldeweg
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeuropsychologyGreat Ormond Street HospitalLondonUK
| | - Sophie Adler
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
| | - Konrad Wagstyl
- Imaging NeuroscienceUCL Queen Square Institute of NeurologyLondonUK
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Tangsrivimol JA, Schonfeld E, Zhang M, Veeravagu A, Smith TR, Härtl R, Lawton MT, El-Sherbini AH, Prevedello DM, Glicksberg BS, Krittanawong C. Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future. Diagnostics (Basel) 2023; 13:2429. [PMID: 37510174 PMCID: PMC10378231 DOI: 10.3390/diagnostics13142429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.
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Affiliation(s)
- Jonathan A Tangsrivimol
- Division of Neurosurgery, Department of Surgery, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok 10210, Thailand
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Ethan Schonfeld
- Department Biomedical Informatics, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Michael Zhang
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA 94305, USA
| | - Anand Veeravagu
- Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory, Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Timothy R Smith
- Department of Neurosurgery, Computational Neuroscience Outcomes Center (CNOC), Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
| | - Roger Härtl
- Weill Cornell Medicine Brain and Spine Center, New York, NY 10022, USA
| | - Michael T Lawton
- Department of Neurosurgery, Barrow Neurological Institute (BNI), Phoenix, AZ 85013, USA
| | - Adham H El-Sherbini
- Faculty of Health Sciences, Queen's University, Kingston, ON K7L 3N6, Canada
| | - Daniel M Prevedello
- Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Chayakrit Krittanawong
- Cardiology Division, New York University Langone Health, New York University School of Medicine, New York, NY 10016, USA
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4
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Baciu M, O'Sullivan L, Torlay L, Banjac S. New insights for predicting surgery outcome in patients with temporal lobe epilepsy. A systematic review. Rev Neurol (Paris) 2023:S0035-3787(23)00884-6. [PMID: 37003897 DOI: 10.1016/j.neurol.2023.02.067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 01/16/2023] [Accepted: 02/22/2023] [Indexed: 04/03/2023]
Abstract
Resective surgery is the treatment of choice for one-third of adult patients with focal, drug-resistant epilepsy. This procedure is associated with substantial clinical and cognitive risks. In clinical practice, there is no validated model for epilepsy surgery outcome prediction (ESOP). Meta-analyses on ESOP studies assessing prognostic factors report discrepancies in terms of study design. Our review aims to systematically investigate methodological and analytical aspects of studies predicting clinical and cognitive outcomes after temporal lobe epilepsy surgery. A systematic review of ESOP studies published between 2000 and 2022 from three databases (MEDLINE, Web of Science, and PsycINFO) was completed by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. It yielded 4867 articles. Among them, 21 corresponded to our inclusion criteria and were therefore retained in the final review. The risk of bias was assessed using A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST). Data extracted from the 21 studies were analyzed using narrative synthesis and descriptive statistics. Our findings show an increase in the use of multimodal datasets and machine learning analyses in recent ESOP studies, although regression remained the most frequently used approach. We also identified a more frequent use of network notions in recent ESOP studies. Nevertheless, several methodological issues were noted, such as small sample sizes, lack of information on the follow-up period, variability in seizure outcome, and the definition of neuropsychological postoperative change. Of 21 studies, only one provided a clinical tool to anticipate the cognitive outcome after epilepsy surgery. We conclude that methodological issues should be overcome before we move towards more complete models to better predict clinical and cognitive outcomes after epilepsy surgery. Recommendations for future studies to harness the possibilities of multimodal datasets and data fusion, are provided. A stronger bridge between fundamental and clinical research may result in developing accessible clinical tools.
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Affiliation(s)
- M Baciu
- Université Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - L O'Sullivan
- Université Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - L Torlay
- Université Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France
| | - S Banjac
- Université Grenoble Alpes, CNRS LPNC UMR 5105, 38000 Grenoble, France.
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5
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Hinds W, Modi S, Ankeeta A, Sperling MR, Pustina D, Tracy JI. Pre-surgical features of intrinsic brain networks predict single and joint epilepsy surgery outcomes. Neuroimage Clin 2023; 38:103387. [PMID: 37023491 PMCID: PMC10122017 DOI: 10.1016/j.nicl.2023.103387] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/02/2023] [Accepted: 03/24/2023] [Indexed: 03/31/2023]
Abstract
Despite the effectiveness of surgical interventions for the treatment of intractable focal temporal lobe epilepsy (TLE), the substrates that support good outcomes are poorly understood. While algorithms have been developed for the prediction of either seizure or cognitive/psychiatric outcomes alone, no study has reported on the functional and structural architecture that supports joint outcomes. We measured key aspects of pre-surgical whole brain functional/structural network architecture and evaluated their ability to predict post-operative seizure control in combination with cognitive/psychiatric outcomes. Pre-surgically, we identified the intrinsic connectivity networks (ICNs) unique to each person through independent component analysis (ICA), and computed: (1) the spatial-temporal match between each person's ICA components and established, canonical ICNs, (2) the connectivity strength within each identified person-specific ICN, (3) the gray matter (GM) volume underlying the person-specific ICNs, and (4) the amount of variance not explained by the canonical ICNs for each person. Post-surgical seizure control and reliable change indices of change (for language [naming, phonemic fluency], verbal episodic memory, and depression) served as binary outcome responses in random forest (RF) models. The above functional and structural measures served as input predictors. Our empirically derived ICN-based measures customized to the individual showed that good joint seizure and cognitive/psychiatric outcomes depended upon higher levels of brain reserve (GM volume) in specific networks. In contrast, singular outcomes relied on systematic, idiosyncratic variance in the case of seizure control, and the weakened pre-surgical presence of functional ICNs that encompassed the ictal temporal lobe in the case of cognitive/psychiatric outcomes. Our data made clear that the ICNs differed in their propensity to provide reserve for adaptive outcomes, with some providing structural (brain), and others functional (cognitive) reserve. Our customized methodology demonstrated that when substantial unique, patient-specific ICNs are present prior to surgery there is a reliable association with poor post-surgical seizure control. These ICNs are idiosyncratic in that they did not match the canonical, normative ICNs and, therefore, could not be defined functionally, with their location likely varying by patient. This important finding suggested the level of highly individualized ICN's in the epileptic brain may signal the emergence of epileptogenic activity after surgery.
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Affiliation(s)
- Walter Hinds
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Shilpi Modi
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Ankeeta Ankeeta
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | - Michael R Sperling
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA
| | | | - Joseph I Tracy
- Thomas Jefferson University, Department of Neurology, and Vicky and Jack Farber Institute for Neuroscience, USA.
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6
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Abstract
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.
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Affiliation(s)
- Lara Jehi
- Cleveland Clinic Ringgold Standard Institution, Cleveland, OH, USA
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7
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Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach. Brain Sci 2022; 13:brainsci13010071. [PMID: 36672052 PMCID: PMC9856795 DOI: 10.3390/brainsci13010071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 12/21/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome. METHODS We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy. RESULTS Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified. CONCLUSIONS The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT. SIGNIFICANCE The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.
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8
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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. Machine Learning Approaches for Imaging-Based Prognostication of the Outcome of Surgery for Mesial Temporal Lobe Epilepsy. Epilepsia 2022; 63:1081-1092. [PMID: 35266138 PMCID: PMC9545680 DOI: 10.1111/epi.17217] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 02/09/2022] [Accepted: 03/07/2022] [Indexed: 11/29/2022]
Abstract
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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Affiliation(s)
- Benjamin Sinclair
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Varduhi Cahill
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,Academic Neurology Unit, University of Sheffield, Royal Hallamshire Hospital, Sheffield, United Kingdom.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, University of Manchester, Manchester, United Kingdom.,Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Jarrel Seah
- Department of Radiology, Alfred Health, Melbourne, Victoria, Australia
| | - Andy Kitchen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Lucy E Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Charles B Malpas
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia.,Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Melbourne, Victoria, Australia.,Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia
| | - Marie F O'Shea
- Melbourne School of Psychological Sciences, University of Melbourne, Melbourne, Victoria, Australia.,Comprehensive Epilepsy Program, Austin Health, Melbourne, Victoria, Australia
| | - Patricia M Desmond
- Department of Radiology, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Rodney J Hicks
- Peter MacCallum Cancer Centre and the Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew P Morokoff
- Department of Surgery, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - James A King
- Department of Surgery, University of Melbourne, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Gavin C Fabinyi
- Department of Surgery, University of Melbourne, Austin Hospital, Melbourne, Victoria, Australia
| | - Andrew H Kaye
- Department of Neurosurgery, Hadassah Hebrew University Hospital, Jerusalem, Israel
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia.,Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Samuel F Berkovic
- Epilepsy Research Centre, University of Melbourne, Austin Hospital, Melbourne, Victoria, Australia.,Comprehensive Epilepsy Program, Austin Health, Melbourne, Victoria, Australia
| | - Meng Law
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department of Radiology, Alfred Health, Melbourne, Victoria, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.,Department Neurology, Alfred Health, Melbourne, Victoria, Australia.,Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia.,Department of Neurology, Melbourne Brain Centre, Royal Melbourne Hospital, Melbourne, Victoria, Australia
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9
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Machine learning models for decision support in epilepsy management: A critical review. Epilepsy Behav 2021; 123:108273. [PMID: 34507093 DOI: 10.1016/j.yebeh.2021.108273] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/13/2021] [Accepted: 08/14/2021] [Indexed: 12/12/2022]
Abstract
PURPOSE There remain major challenges for the clinician in managing patients with epilepsy effectively. Choosing anti-seizure medications (ASMs) is subject to trial and error. About one-third of patients have drug-resistant epilepsy (DRE). Surgery may be considered for selected patients, but time from diagnosis to surgery averages 20 years. We reviewed the potential use of machine learning (ML) predictive models as clinical decision support tools to help address some of these issues. METHODS We conducted a comprehensive search of Medline and Embase of studies that investigated the application of ML in epilepsy management in terms of predicting ASM responsiveness, predicting DRE, identifying surgical candidates, and predicting epilepsy surgery outcomes. Original articles addressing these 4 areas published in English between 2000 and 2020 were included. RESULTS We identified 24 relevant articles: 6 on ASM responsiveness, 3 on DRE prediction, 2 on identifying surgical candidates, and 13 on predicting surgical outcomes. A variety of potential predictors were used including clinical, neuropsychological, imaging, electroencephalography, and health system claims data. A number of different ML algorithms and approaches were used for prediction, but only one study utilized deep learning methods. Some models show promising performance with areas under the curve above 0.9. However, most were single setting studies (18 of 24) with small sample sizes (median number of patients 55), with the exception of 3 studies that utilized large databases and 3 studies that performed external validation. There was a lack of standardization in reporting model performance. None of the models reviewed have been prospectively evaluated for their clinical benefits. CONCLUSION The utility of ML models for clinical decision support in epilepsy management remains to be determined. Future research should be directed toward conducting larger studies with external validation, standardization of reporting, and prospective evaluation of the ML model on patient outcomes.
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10
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Samanta D, Beal JC, Grinspan ZM. Automated Identification of Surgical Candidates and Estimation of Postoperative Seizure Freedom in Children - A Focused Review. Semin Pediatr Neurol 2021; 39:100914. [PMID: 34620464 PMCID: PMC9082396 DOI: 10.1016/j.spen.2021.100914] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/09/2021] [Accepted: 08/11/2021] [Indexed: 11/15/2022]
Abstract
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.
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Affiliation(s)
- Debopam Samanta
- Neurology Division, Department of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, AR
| | - Jules C. Beal
- Department of Pediatrics, Weill Cornell Medicine, New York, NY
| | - Zachary M. Grinspan
- Department of Pediatrics, Weill Cornell Medicine, New York, NY.,Department of Population Health Sciences, Weill Cornell Medicine, New York, NY
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11
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Rigney G, Lennon M, Holderrieth P. The use of computational models in the management and prognosis of refractory epilepsy: A critical evaluation. Seizure 2021; 91:132-140. [PMID: 34153898 DOI: 10.1016/j.seizure.2021.06.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 05/05/2021] [Accepted: 06/06/2021] [Indexed: 11/17/2022] Open
Abstract
PURPOSE Drug resistant epilepsy (DRE) affects approximately 30 percent of individuals with epilepsy worldwide. Surgery remains the most effective treatment for individuals with DRE, but referral to surgery is low and only about 60 percent of individuals who undergo surgery experience seizure control postoperatively. The present paper evaluates the evidence for using computational models in the prediction of surgical resection sites and surgical outcomes for patients with DRE. METHODS We conducted a search in the Medline data base using the terms "refractory epilepsy", "drug-resistant epilepsy", "surgery", "computational model", and "artificial intelligence". Inclusion: original articles in English and case reports from 2000 to 2020. Reviews were excluded. RESULTS Clinical applications of computational models may lead to increased utilisation of surgical services through improving our ability to predict outcomes and by improving surgical outcomes outright. The identification and optimisation of nodes that are crucial for the genesis and propagation of epileptiform activity offers the most promising clinical applications of computational models discussed herein. CONCLUSION Advances in computational models may in the future significantly increase the application and efficacy of surgery for patients with DRE by optimising the site and amount of cortex to resect, but more research is needed before it achieves therapeutic utility.
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Affiliation(s)
- Grant Rigney
- The University of Oxford Department of Psychiatry, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom.
| | - Matthew Lennon
- Department of Physiology, Anatomy and Genetics, Sherrington Building, University of Oxford, United Kingdom; Faculty of Medicine, University of New South Wales, NSW, Australia.
| | - Peter Holderrieth
- Department of Physiology, Anatomy and Genetics, Sherrington Building, University of Oxford, United Kingdom.
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Whiting AC, Morita-Sherman M, Li M, Vegh D, Machado de Campos B, Cendes F, Wang X, Bingaman W, Jehi LE. Automated analysis of cortical volume loss predicts seizure outcomes after frontal lobectomy. Epilepsia 2021; 62:1074-1084. [PMID: 33756031 DOI: 10.1111/epi.16877] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 03/03/2021] [Accepted: 03/03/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Patients undergoing frontal lobectomy demonstrate lower seizure-freedom rates than patients undergoing temporal lobectomy and several other resective interventions. We attempted to utilize automated preoperative quantitative analysis of focal and global cortical volume loss to develop predictive volumetric indicators of seizure outcome after frontal lobectomy. METHODS Ninety patients who underwent frontal lobectomy were stratified based on seizure freedom at a mean follow-up time of 3.5 (standard deviation [SD] 2.5) years. Automated quantitative analysis of cortical volume loss organized by distinct brain region and laterality was performed on preoperative T1-weighted magnetic resonance imaging (MRI) studies. Univariate statistical analysis was used to select potential predictors of seizure freedom. Backward variable selection and multivariate logistical regression were used to develop models to predict seizure freedom. RESULTS Forty-eight of 90 (53.3%) patients were seizure-free at the last follow-up. Several frontal and extrafrontal brain regions demonstrated statistically significant differences in both volumetric cortical volume loss and volumetric asymmetry between the left and right sides in the seizure-free and non-seizure-free cohorts. A final multivariate logistic model utilizing only preoperative quantitative MRI data to predict seizure outcome was developed with a c-statistic of 0.846. Using both preoperative quantitative MRI data and previously validated clinical predictors of seizure outcomes, we developed a model with a c-statistic of 0.897. SIGNIFICANCE This study demonstrates that preoperative cortical volume loss in both frontal and extrafrontal regions can be predictive of seizure outcome after frontal lobectomy, and models can be developed with excellent predictive capabilities using preoperative MRI data. Automated quantitative MRI analysis can be quickly and reliably performed in patients with frontal lobe epilepsy, and further studies may be developed for integration into preoperative risk stratification.
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Affiliation(s)
- Alexander C Whiting
- Cleveland Clinic Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH, USA
| | | | - Manshi Li
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Deborah Vegh
- Cleveland Clinic Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH, USA
| | | | - Fernando Cendes
- Department of Neurology, University of Campinas UNICAMP, Campinas, Brazil
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - William Bingaman
- Cleveland Clinic Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Lara E Jehi
- Cleveland Clinic Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH, USA
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Gradually evaluating of suicidal risk in depression by semi-supervised cluster analysis on resting-state fMRI. Brain Imaging Behav 2020; 15:2149-2158. [PMID: 33151465 DOI: 10.1007/s11682-020-00410-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/14/2020] [Indexed: 12/23/2022]
Abstract
A timely and effective evaluation of the suicidal ideation bears practical meaning, particularly for the depressive who tend to disguise the real suicide intent and without obvious symptoms. Measuring individual ideation of the depression with uncertain or transient suicide crisis is the purpose. Resting-state fMRI data were collected from 78 depressed patients with variable clinical suicidal crisis. Thirty subjects were well labeled as extremely serious individuals with suicide attempters or as without suicidal ideation. A feature mask was constructed via the two sample t-test on their regional conncectivities. Then, a semi-supervised machine learning frame using the feature mask was designed to assist in clarifying gradation of suicidal susceptibility for the residual forty-eight vaguely defined subjects, by a way of Iterative Self-Organizing Data analysis techniques (ISODATA). Such semi-supervised model was designed purposely to block out the effect of disease itself on the suicide intendancy evaluation. The vague-labeled patients were divided into another two different stages relating to their suicidal susceptibility. The distance ratio of each subject to the two well-defined extreme groups in the feature space can be utilized as the suicide risk index. The re-evaluation of the Nurses' Global Assessment of Suicide Risk (NGASR) via experts blind to original HAM-D rates was significantly correlated with the model estimation. The constructed model suggested its potential to examine the risk of suicidal in an objective way. The functional connectivity, locating mostly within the frontal-temporal circuit and involving the default mode network (DMN), were well integrated to discriminative the gradual susceptibility of suicidal.
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Abbasi B, Goldenholz DM. Machine learning applications in epilepsy. Epilepsia 2019; 60:2037-2047. [PMID: 31478577 PMCID: PMC9897263 DOI: 10.1111/epi.16333] [Citation(s) in RCA: 179] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 07/25/2019] [Accepted: 08/12/2019] [Indexed: 02/05/2023]
Abstract
Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre-surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.
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Affiliation(s)
- Bardia Abbasi
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA 02215
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15
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Hwang G, Nair VA, Mathis J, Cook CJ, Mohanty R, Zhao G, Tellapragada N, Ustine C, Nwoke OO, Rivera-Bonet C, Rozman M, Allen L, Forseth C, Almane DN, Kraegel P, Nencka A, Felton E, Struck AF, Birn R, Maganti R, Conant LL, Humphries CJ, Hermann B, Raghavan M, DeYoe EA, Binder JR, Meyerand E, Prabhakaran V. Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning. Brain Connect 2019; 9:184-193. [PMID: 30803273 PMCID: PMC6484357 DOI: 10.1089/brain.2018.0601] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the FreeSurfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age- and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were ∼83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.
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Affiliation(s)
- Gyujoon Hwang
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Veena A. Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Jed Mathis
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Cole J. Cook
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rosaleena Mohanty
- Department of Electrical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Gengyan Zhao
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | | | - Candida Ustine
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | | | - Megan Rozman
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Linda Allen
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Courtney Forseth
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Dace N. Almane
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Peter Kraegel
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Andrew Nencka
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Felton
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Aaron F. Struck
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rasmus Birn
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin
| | - Rama Maganti
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Lisa L. Conant
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Colin J. Humphries
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Bruce Hermann
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
| | - Manoj Raghavan
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Edgar A. DeYoe
- Department of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Jeffrey R. Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Elizabeth Meyerand
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, Wisconsin
| | - Vivek Prabhakaran
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
- Department of Neurology, University of Wisconsin-Madison, Madison, Wisconsin
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Detection of Surgical Site Infection Utilizing Automated Feature Generation in Clinical Notes. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2018; 3:267-282. [PMID: 31728432 DOI: 10.1007/s41666-018-0042-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Postsurgical complications (PSCs) are known as a deviation from the normal postsurgical course and categorized by severity and treatment requirements. Surgical site infection (SSI) is one of major PSCs and the most common healthcare-associated infection, resulting in increased length of hospital stay and cost. In this work, we proposed an automated way to generate keyword features using sublanguage analysis with heuristics to detect SSI from cohort in clinical notes and evaluated these keywords with medical experts. To further valid our approach, we also applied different machine learning algorithms on cohort using automatically generated keywords. The results showed that our approach was able to identify SSI keywords from clinical narratives and can be used as a foundation to develop an information extraction system or support search-based natural language processing (NLP) approaches by augmenting search queries.
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Taylor PN, Sinha N, Wang Y, Vos SB, de Tisi J, Miserocchi A, McEvoy AW, Winston GP, Duncan JS. The impact of epilepsy surgery on the structural connectome and its relation to outcome. Neuroimage Clin 2018; 18:202-214. [PMID: 29876245 PMCID: PMC5987798 DOI: 10.1016/j.nicl.2018.01.028] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 12/05/2017] [Accepted: 01/21/2018] [Indexed: 01/26/2023]
Abstract
Background Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome. Methods We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks. Results Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole. Conclusion Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.
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Affiliation(s)
- Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK; Institute of Neuroscience, Faculty of Medical Science, Newcastle University, UK; NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK.
| | - Nishant Sinha
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK; Institute of Neuroscience, Faculty of Medical Science, Newcastle University, UK
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, UK; Institute of Neuroscience, Faculty of Medical Science, Newcastle University, UK; NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Sjoerd B Vos
- Translational Imaging Group, Centre for Medical Image Computing, University College London, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0LR, UK
| | - Jane de Tisi
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Anna Miserocchi
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Andrew W McEvoy
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Gavin P Winston
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0LR, UK
| | - John S Duncan
- NIHR University College London Hospitals Biomedical Research Centre, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Chalfont Centre for Epilepsy, Chalfont St Peter SL9 0LR, UK
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18
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Machine Learning and Neurosurgical Outcome Prediction: A Systematic Review. World Neurosurg 2018; 109:476-486.e1. [DOI: 10.1016/j.wneu.2017.09.149] [Citation(s) in RCA: 217] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 09/20/2017] [Accepted: 09/21/2017] [Indexed: 11/18/2022]
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19
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Youngerman BE, McKhann GM. From Nodes to Networks: Can Virtual Resections Predict Neurosurgical Outcomes in Focal Epilepsy? Neurosurgery 2017; 81:N25-N26. [PMID: 28859458 DOI: 10.1093/neuros/nyx391] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Brett E Youngerman
- Department of Neurological Surgery Columbia University Medical Center New York, New York
| | - Guy M McKhann
- Department of Neurological Surgery Columbia University Medical Center New York, New York
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20
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Chassoux F, Artiges E, Semah F, Laurent A, Landré E, Turak B, Gervais P, Helal BO, Devaux B. 18F-FDG-PET patterns of surgical success and failure in mesial temporal lobe epilepsy. Neurology 2017; 88:1045-1053. [PMID: 28188304 DOI: 10.1212/wnl.0000000000003714] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2016] [Accepted: 10/06/2016] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To search for [18F]-fluorodeoxyglucose (FDG)-PET patterns predictive of long-term prognosis in surgery for drug-resistant mesial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis (HS). METHODS We analyzed metabolic data with [18F]-FDG-PET in 97 patients with MTLE (53 female participants; age range 15-56 years) with unilateral HS (50 left) and compared the metabolic patterns, electroclinical features, and structural atrophy on MRI in patients with the best outcome after anteromesial temporal resection (Engel class IA, completely seizure-free) to those with a non-IA outcome, including suboptimal outcome and failure. Imaging processing was performed with statistical parametric mapping (SPM5). RESULTS With a mean follow-up of >6 years (range 2-14 years), 85% of patients achieved a class I outcome, including 45% in class IA. Class IA outcome was associated with a focal anteromesial temporal hypometabolism, whereas non-IA outcome correlated with extratemporal metabolic changes that differed according to the lateralization: ipsilateral mesial frontal and perisylvian hypometabolism in right HS and contralateral fronto-insular hypometabolism and posterior white matter hypermetabolism in left HS. Suboptimal outcome presented a metabolic pattern similar to the best outcome but with a larger involvement of extratemporal areas, including the contralateral side in left HS. Failure was characterized by a mild temporal involvement sparing the hippocampus and relatively high extratemporal hypometabolism on both sides. These findings were concordant with electroclinical features reflecting the organization of the epileptogenic zone but were independent of the structural abnormalities detected on MRI. CONCLUSIONS [18F]-FDG-PET patterns help refine the prognostic factors in MTLE and should be implemented in predictive models for epilepsy surgery.
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Affiliation(s)
- Francine Chassoux
- From the Department of Neurosurgery (F.C., A.L., E.L., B.T., B.D.), Sainte-Anne Hospital; Paris-Descartes University (F.C., A.L., E.L., B.T., B.D.); INSERM UMR 1129 (F.C., A.L.), Paris; Nuclear Medicine Department (F.C., P.G., B.-O.H.), SHFJ, CEA/SAC/DRF/IBM Neurospin Gif/Yvette; INSERM (E.A.), Research Unit 1000 "Neuroimaging and Psychiatry," Paris Sud University-Paris Saclay University; Psychiatry Department 91G16 (E.A.), Orsay Hospital, Orsay; Department of Nuclear Medicine and INSERM U 1171 (F.S.), CHU Lille; and INSERM U 1023 (P.G., B.-O.H.), IMIV, CEA, Paris-Sud University, Orsay, France.
| | - Eric Artiges
- From the Department of Neurosurgery (F.C., A.L., E.L., B.T., B.D.), Sainte-Anne Hospital; Paris-Descartes University (F.C., A.L., E.L., B.T., B.D.); INSERM UMR 1129 (F.C., A.L.), Paris; Nuclear Medicine Department (F.C., P.G., B.-O.H.), SHFJ, CEA/SAC/DRF/IBM Neurospin Gif/Yvette; INSERM (E.A.), Research Unit 1000 "Neuroimaging and Psychiatry," Paris Sud University-Paris Saclay University; Psychiatry Department 91G16 (E.A.), Orsay Hospital, Orsay; Department of Nuclear Medicine and INSERM U 1171 (F.S.), CHU Lille; and INSERM U 1023 (P.G., B.-O.H.), IMIV, CEA, Paris-Sud University, Orsay, France
| | - Franck Semah
- From the Department of Neurosurgery (F.C., A.L., E.L., B.T., B.D.), Sainte-Anne Hospital; Paris-Descartes University (F.C., A.L., E.L., B.T., B.D.); INSERM UMR 1129 (F.C., A.L.), Paris; Nuclear Medicine Department (F.C., P.G., B.-O.H.), SHFJ, CEA/SAC/DRF/IBM Neurospin Gif/Yvette; INSERM (E.A.), Research Unit 1000 "Neuroimaging and Psychiatry," Paris Sud University-Paris Saclay University; Psychiatry Department 91G16 (E.A.), Orsay Hospital, Orsay; Department of Nuclear Medicine and INSERM U 1171 (F.S.), CHU Lille; and INSERM U 1023 (P.G., B.-O.H.), IMIV, CEA, Paris-Sud University, Orsay, France
| | - Agathe Laurent
- From the Department of Neurosurgery (F.C., A.L., E.L., B.T., B.D.), Sainte-Anne Hospital; Paris-Descartes University (F.C., A.L., E.L., B.T., B.D.); INSERM UMR 1129 (F.C., A.L.), Paris; Nuclear Medicine Department (F.C., P.G., B.-O.H.), SHFJ, CEA/SAC/DRF/IBM Neurospin Gif/Yvette; INSERM (E.A.), Research Unit 1000 "Neuroimaging and Psychiatry," Paris Sud University-Paris Saclay University; Psychiatry Department 91G16 (E.A.), Orsay Hospital, Orsay; Department of Nuclear Medicine and INSERM U 1171 (F.S.), CHU Lille; and INSERM U 1023 (P.G., B.-O.H.), IMIV, CEA, Paris-Sud University, Orsay, France
| | - Elisabeth Landré
- From the Department of Neurosurgery (F.C., A.L., E.L., B.T., B.D.), Sainte-Anne Hospital; Paris-Descartes University (F.C., A.L., E.L., B.T., B.D.); INSERM UMR 1129 (F.C., A.L.), Paris; Nuclear Medicine Department (F.C., P.G., B.-O.H.), SHFJ, CEA/SAC/DRF/IBM Neurospin Gif/Yvette; INSERM (E.A.), Research Unit 1000 "Neuroimaging and Psychiatry," Paris Sud University-Paris Saclay University; Psychiatry Department 91G16 (E.A.), Orsay Hospital, Orsay; Department of Nuclear Medicine and INSERM U 1171 (F.S.), CHU Lille; and INSERM U 1023 (P.G., B.-O.H.), IMIV, CEA, Paris-Sud University, Orsay, France
| | - Baris Turak
- From the Department of Neurosurgery (F.C., A.L., E.L., B.T., B.D.), Sainte-Anne Hospital; Paris-Descartes University (F.C., A.L., E.L., B.T., B.D.); INSERM UMR 1129 (F.C., A.L.), Paris; Nuclear Medicine Department (F.C., P.G., B.-O.H.), SHFJ, CEA/SAC/DRF/IBM Neurospin Gif/Yvette; INSERM (E.A.), Research Unit 1000 "Neuroimaging and Psychiatry," Paris Sud University-Paris Saclay University; Psychiatry Department 91G16 (E.A.), Orsay Hospital, Orsay; Department of Nuclear Medicine and INSERM U 1171 (F.S.), CHU Lille; and INSERM U 1023 (P.G., B.-O.H.), IMIV, CEA, Paris-Sud University, Orsay, France
| | - Philippe Gervais
- From the Department of Neurosurgery (F.C., A.L., E.L., B.T., B.D.), Sainte-Anne Hospital; Paris-Descartes University (F.C., A.L., E.L., B.T., B.D.); INSERM UMR 1129 (F.C., A.L.), Paris; Nuclear Medicine Department (F.C., P.G., B.-O.H.), SHFJ, CEA/SAC/DRF/IBM Neurospin Gif/Yvette; INSERM (E.A.), Research Unit 1000 "Neuroimaging and Psychiatry," Paris Sud University-Paris Saclay University; Psychiatry Department 91G16 (E.A.), Orsay Hospital, Orsay; Department of Nuclear Medicine and INSERM U 1171 (F.S.), CHU Lille; and INSERM U 1023 (P.G., B.-O.H.), IMIV, CEA, Paris-Sud University, Orsay, France
| | - Badia-Ourkia Helal
- From the Department of Neurosurgery (F.C., A.L., E.L., B.T., B.D.), Sainte-Anne Hospital; Paris-Descartes University (F.C., A.L., E.L., B.T., B.D.); INSERM UMR 1129 (F.C., A.L.), Paris; Nuclear Medicine Department (F.C., P.G., B.-O.H.), SHFJ, CEA/SAC/DRF/IBM Neurospin Gif/Yvette; INSERM (E.A.), Research Unit 1000 "Neuroimaging and Psychiatry," Paris Sud University-Paris Saclay University; Psychiatry Department 91G16 (E.A.), Orsay Hospital, Orsay; Department of Nuclear Medicine and INSERM U 1171 (F.S.), CHU Lille; and INSERM U 1023 (P.G., B.-O.H.), IMIV, CEA, Paris-Sud University, Orsay, France
| | - Bertrand Devaux
- From the Department of Neurosurgery (F.C., A.L., E.L., B.T., B.D.), Sainte-Anne Hospital; Paris-Descartes University (F.C., A.L., E.L., B.T., B.D.); INSERM UMR 1129 (F.C., A.L.), Paris; Nuclear Medicine Department (F.C., P.G., B.-O.H.), SHFJ, CEA/SAC/DRF/IBM Neurospin Gif/Yvette; INSERM (E.A.), Research Unit 1000 "Neuroimaging and Psychiatry," Paris Sud University-Paris Saclay University; Psychiatry Department 91G16 (E.A.), Orsay Hospital, Orsay; Department of Nuclear Medicine and INSERM U 1171 (F.S.), CHU Lille; and INSERM U 1023 (P.G., B.-O.H.), IMIV, CEA, Paris-Sud University, Orsay, France
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Chen YC, Zhu GY, Wang X, Shi L, Jiang Y, Zhang X, Zhang JG. Deep brain stimulation of the anterior nucleus of the thalamus reverses the gene expression of cytokines and their receptors as well as neuronal degeneration in epileptic rats. Brain Res 2016; 1657:304-311. [PMID: 28027874 DOI: 10.1016/j.brainres.2016.12.020] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 12/16/2016] [Accepted: 12/20/2016] [Indexed: 02/07/2023]
Abstract
BACKGROUND Deep brain stimulation of the anterior nucleus of the thalamus (ANT-DBS) is effective in seizure control. However, the mechanisms remain unclear. METHODS Sixty-four rats were randomly assigned to the control group, the kainic acid (KA) group, the sham-DBS group and the DBS group. Video-electroencephalogram (EEG) was used to monitor seizures. Quantitative real time PCR (qPCR) was applied for detecting interleukin-1 beta (IL-1β), IL-1 receptor (IL-1R), IL-6, IL-6 receptor (IL-6R), gp130, tumor necrosis factor-alpha (TNF-α), TNF-receptor 1 (TNF-R1) and TNF-receptor 2 (TNF-R2) expression 12h after the establishment of an epileptic model. The neuronal structural degeneration in the hippocampus was evaluated with transmission electron microscopy (TEM) at this same time point. RESULTS The seizure frequency was 48.6% lower in the DBS group compared with the sham-DBS group (P<0.01). The expression of IL-1β, IL-1R, IL-6, IL-6R, gp130, TNF-α and TNF-R1 was elevated in both the KA and the sham group compared with the control group (all Ps<0.01). Additionally, ANT-DBS was able to reverse this gene expression pattern in the DBS group compared with the sham-DBS group (all Ps<0.01). There was no significant difference in TNF-R2 expression among the four groups. The neuronal structural degeneration in the KA group and the sham-DBS group was more severe than that in the control group (injury scores, all Ps<0.01). ANT-DBS was also capable of relieving the degeneration compared with the sham-DBS group (injury score, P<0.01). CONCLUSIONS This study demonstrated that ANT-DBS can reduce seizure frequency in the early stage in epileptic rats as well as relieve the pro-inflammatory state and neuronal injury, which may be one of the most effective mechanisms of ANT-DBS against epileptogenesis.
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Affiliation(s)
- Ying-Chuan Chen
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China.
| | - Guan-Yu Zhu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China.
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China.
| | - Lin Shi
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China.
| | - Yin Jiang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100050, China.
| | - Xin Zhang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100050, China.
| | - Jian-Guo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China; Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing 100050, China; Beijing Key Laboratory of Neurostimulation, Beijing 100050, China.
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Sinha N, Dauwels J, Kaiser M, Cash SS, Brandon Westover M, Wang Y, Taylor PN. Predicting neurosurgical outcomes in focal epilepsy patients using computational modelling. Brain 2016; 140:319-332. [PMID: 28011454 PMCID: PMC5278304 DOI: 10.1093/brain/aww299] [Citation(s) in RCA: 173] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 10/08/2016] [Accepted: 10/10/2016] [Indexed: 01/03/2023] Open
Abstract
See Eissa and Schevon (doi:10.1093/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.
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Affiliation(s)
- Nishant Sinha
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
| | - Justin Dauwels
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK.,Institute of Neuroscience, Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, UK
| | - Sydney S Cash
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - M Brandon Westover
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Yujiang Wang
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK
| | - Peter N Taylor
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing Science, Newcastle University, Newcastle upon Tyne, UK .,Institute of Neuroscience, Faculty of Medical Science, Newcastle University, Newcastle upon Tyne, UK.,Institute of Neurology, University College London, UK
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Passos IC, Mwangi B, Cao B, Hamilton JE, Wu MJ, Zhang XY, Zunta-Soares GB, Quevedo J, Kauer-Sant'Anna M, Kapczinski F, Soares JC. Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach. J Affect Disord 2016; 193:109-16. [PMID: 26773901 PMCID: PMC4744514 DOI: 10.1016/j.jad.2015.12.066] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2015] [Revised: 12/09/2015] [Accepted: 12/26/2015] [Indexed: 12/31/2022]
Abstract
OBJECTIVE A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to estimate the probability of an individual patient attempting suicide. METHOD A total of 144 patients with mood disorders were included. Clinical variables associated with suicide attempts among patients with mood disorders and demographic variables were used to 'train' a machine learning algorithm. The resulting algorithm was utilized in identifying novel or 'unseen' individual subjects as either suicide attempters or non-attempters. Three machine learning algorithms were implemented and evaluated. RESULTS All algorithms distinguished individual suicide attempters from non-attempters with prediction accuracy ranging between 65% and 72% (p<0.05). In particular, the relevance vector machine (RVM) algorithm correctly predicted 103 out of 144 subjects translating into 72% accuracy (72.1% sensitivity and 71.3% specificity) and an area under the curve of 0.77 (p<0.0001). The most relevant predictor variables in distinguishing attempters from non-attempters included previous hospitalizations for depression, a history of psychosis, cocaine dependence and post-traumatic stress disorder (PTSD) comorbidity. CONCLUSION Risk for suicide attempt among patients with mood disorders can be estimated at an individual subject level by incorporating both demographic and clinical variables. Future studies should examine the performance of this model in other populations and its subsequent utility in facilitating selection of interventions to prevent suicide.
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Affiliation(s)
- Ives Cavalcante Passos
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA,Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Benson Mwangi
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA.
| | - Bo Cao
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Jane E Hamilton
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Mon-Ju Wu
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Xiang Yang Zhang
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA,Beijing HuiLongGuan Hospital, Peking University, Beijing, China
| | - Giovana B. Zunta-Soares
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Joao Quevedo
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
| | - Marcia Kauer-Sant'Anna
- Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Flávio Kapczinski
- Bipolar Disorder Program and Laboratory of Molecular Psychiatry, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Jair C. Soares
- Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, Texas, USA
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FTO gene variant modulates the neural correlates of visual food perception. Neuroimage 2016; 128:21-31. [DOI: 10.1016/j.neuroimage.2015.12.049] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2015] [Revised: 12/22/2015] [Accepted: 12/30/2015] [Indexed: 01/01/2023] Open
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Memarian N, Kim S, Dewar S, Engel J, Staba RJ. Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy. Comput Biol Med 2015; 64:67-78. [PMID: 26149291 PMCID: PMC4554822 DOI: 10.1016/j.compbiomed.2015.06.008] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 06/04/2015] [Accepted: 06/10/2015] [Indexed: 11/21/2022]
Abstract
BACKGROUND This study sought to predict postsurgical seizure freedom from pre-operative diagnostic test results and clinical information using a rapid automated approach, based on supervised learning methods in patients with drug-resistant focal seizures suspected to begin in temporal lobe. METHOD We applied machine learning, specifically a combination of mutual information-based feature selection and supervised learning classifiers on multimodal data, to predict surgery outcome retrospectively in 20 presurgical patients (13 female; mean age±SD, in years 33±9.7 for females, and 35.3±9.4 for males) who were diagnosed with mesial temporal lobe epilepsy (MTLE) and subsequently underwent standard anteromesial temporal lobectomy. The main advantage of the present work over previous studies is the inclusion of the extent of ipsilateral neocortical gray matter atrophy and spatiotemporal properties of depth electrode-recorded seizures as training features for individual patient surgery planning. RESULTS A maximum relevance minimum redundancy (mRMR) feature selector identified the following features as the most informative predictors of postsurgical seizure freedom in this study's sample of patients: family history of epilepsy, ictal EEG onset pattern (positive correlation with seizure freedom), MRI-based gray matter thickness reduction in the hemisphere ipsilateral to seizure onset, proportion of seizures that first appeared in ipsilateral amygdala to total seizures, age, epilepsy duration, delay in the spread of ipsilateral ictal discharges from site of onset, gender, and number of electrode contacts at seizure onset (negative correlation with seizure freedom). Using these features in combination with a least square support vector machine (LS-SVM) classifier compared to other commonly used classifiers resulted in very high surgical outcome prediction accuracy (95%). CONCLUSIONS Supervised machine learning using multimodal compared to unimodal data accurately predicted postsurgical outcome in patients with atypical MTLE.
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Affiliation(s)
- Negar Memarian
- Department of Psychology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States.
| | - Sally Kim
- Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States
| | - Sandra Dewar
- Department of Neurosurgery, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States
| | - Jerome Engel
- Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurosurgery, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Neurobiology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States; Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States
| | - Richard J Staba
- Department of Neurology, David Geffen School of Medicine and at UCLA, Los Angeles, CA 90095, United States
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Munsell BC, Wee CY, Keller SS, Weber B, Elger C, da Silva LAT, Nesland T, Styner M, Shen D, Bonilha L. Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. Neuroimage 2015; 118:219-30. [PMID: 26054876 DOI: 10.1016/j.neuroimage.2015.06.008] [Citation(s) in RCA: 98] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Revised: 05/25/2015] [Accepted: 06/02/2015] [Indexed: 10/23/2022] Open
Abstract
The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with "expert-based" clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy.
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Affiliation(s)
- Brent C Munsell
- Department of Computer Science, College of Charleston, Charleston, SC, USA.
| | - Chong-Yaw Wee
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Simon S Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, UK
| | - Bernd Weber
- Department of Epileptogy, University of Bonn, Germany
| | | | | | - Travis Nesland
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
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Jayalakshmi S, Vooturi S, Vadapalli R, Somayajula S, Madigubba S, Panigrahi M. Outcome of surgery for temporal lobe epilepsy in adults - A cohort study. Int J Surg 2015; 36:443-447. [PMID: 25979111 DOI: 10.1016/j.ijsu.2015.05.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2015] [Accepted: 05/04/2015] [Indexed: 11/29/2022]
Abstract
INTRODUCTION The aim of the current study was to evaluate the factors associated with post-operative outcome in patients with temporal lobe epilepsy (TLE) undergoing Surgery. METHODS We analyzed data of 288 consecutive patients operated for drug-resistant TLE. All the patients had at least one year post surgery follow-up. Logistic regression model was used to evaluate the predictive value of different factors for outcome. RESULTS The mean age at onset of epilepsy of the study population was 15.51 ± 9.79 years; whereas the mean age at surgery was 32.16 ± 9.45 years, with 125 (43.4%) women. The age at surgery was significantly lower in the patients with favourable outcome (30.26 ± 9.05 vs. 34.06 ± 9.85 years; p = 0.007). The mean duration of epilepsy with age of onset below 12 years was higher than the rest (19.84 ± 7.30 vs. 13.00 ± 8.45 years; p < 0.001). The histopathology showed hippocampal sclerosis in 203 (70.4%) of the patients; isolated focal cortical dysplasia was associated with unfavourable outcome (9.3% vs.2.6%; p = 0.036). The duration of follow up ranged from 1 to 10.3 years. Three patients died late in the follow up. At the last follow 73% were seizure free and Engel's favourable outcome was noted in 82%. Duration of epilepsy greater than ten years (β = 6.997; 95%CI; 2.254-21.715; p = 0.01), younger age of onset of epilepsy (β = 1.07; 95%CI; 1.014-1.132; p = 0.015) and acute post operative seizures (APOS) (β = 4.761; 95%CI; 1.946-11.649; p = 0.001) were the predictors of unfavourable outcome. CONCLUSION Following surgery for TLE, 73% were seizure free and Engel's favourable outcome was noted in 82%. The predictors of unfavourable outcome were younger age of onset, pronged duration and of epilepsy and APOS.
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Affiliation(s)
- Sita Jayalakshmi
- Department of Neurology, Krishna Institute of Medical Sciences, Minister Road, Secunderabad - 03, Telangana, India.
| | - Sudhindra Vooturi
- Department of Neurology, Krishna Institute of Medical Sciences, Minister Road, Secunderabad - 03, Telangana, India
| | - Rammohan Vadapalli
- Department of Radiology, Vijaya Diagnostic Centre, Himayath Nagar, Hyderabad - 29, Telangana, India
| | - Shanmukhi Somayajula
- Department of Neurology, Krishna Institute of Medical Sciences, Minister Road, Secunderabad - 03, Telangana, India
| | - Sailaja Madigubba
- Department of Pathology, Krishna Institute of Medical Sciences, Minister Road, Secunderabad - 03, Telangana, India
| | - Manas Panigrahi
- Department of Neurosurgery, Krishna Institute of Medical Sciences, Minister Road, Secunderabad - 03, Telangana, India
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Bonilha L, Keller SS. Quantitative MRI in refractory temporal lobe epilepsy: relationship with surgical outcomes. Quant Imaging Med Surg 2015; 5:204-24. [PMID: 25853080 DOI: 10.3978/j.issn.2223-4292.2015.01.01] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2014] [Accepted: 01/07/2015] [Indexed: 11/14/2022]
Abstract
Medically intractable temporal lobe epilepsy (TLE) remains a serious health problem. Across treatment centers, up to 40% of patients with TLE will continue to experience persistent postoperative seizures at 2-year follow-up. It is unknown why such a large number of patients continue to experience seizures despite being suitable candidates for resective surgery. Preoperative quantitative MRI techniques may provide useful information on why some patients continue to experience disabling seizures, and may have the potential to develop prognostic markers of surgical outcome. In this article, we provide an overview of how quantitative MRI morphometric and diffusion tensor imaging (DTI) data have improved the understanding of brain structural alterations in patients with refractory TLE. We subsequently review the studies that have applied quantitative structural imaging techniques to identify the neuroanatomical factors that are most strongly related to a poor postoperative prognosis. In summary, quantitative imaging studies strongly suggest that TLE is a disorder affecting a network of neurobiological systems, characterized by multiple and inter-related limbic and extra-limbic network abnormalities. The relationship between brain alterations and postoperative outcome are less consistent, but there is emerging evidence suggesting that seizures are less likely to remit with surgery when presurgical abnormalities are observed in the connectivity supporting brain regions serving as network nodes located outside the resected temporal lobe. Future work, possibly harnessing the potential from multimodal imaging approaches, may further elucidate the etiology of persistent postoperative seizures in patients with refractory TLE. Furthermore, quantitative imaging techniques may be explored to provide individualized measures of postoperative seizure freedom outcome.
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Affiliation(s)
- Leonardo Bonilha
- 1 Department of Neurology and Neurosurgery, Medical University of South Carolina, Charleston, SC 29425, USA ; 2 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK ; 3 Department of Radiology, The Walton Centre NHS Foundation Trust, Liverpool, UK ; 4 Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Simon S Keller
- 1 Department of Neurology and Neurosurgery, Medical University of South Carolina, Charleston, SC 29425, USA ; 2 Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, Liverpool, UK ; 3 Department of Radiology, The Walton Centre NHS Foundation Trust, Liverpool, UK ; 4 Department of Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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Shi L, Yang AC, Li JJ, Meng DW, Jiang B, Zhang JG. Favorable modulation in neurotransmitters: Effects of chronic anterior thalamic nuclei stimulation observed in epileptic monkeys. Exp Neurol 2015; 265:94-101. [DOI: 10.1016/j.expneurol.2015.01.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/21/2014] [Accepted: 01/08/2015] [Indexed: 10/24/2022]
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Garcia Gracia C, Yardi R, Kattan MW, Nair D, Gupta A, Najm I, Bingaman W, Gonzalez-Martinez J, Jehi L. Seizure freedom score: A new simple method to predict success of epilepsy surgery. Epilepsia 2014; 56:359-65. [DOI: 10.1111/epi.12892] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2014] [Indexed: 11/30/2022]
Affiliation(s)
- Camilo Garcia Gracia
- Cleveland Clinic Epilepsy Center; Neurological Institute; Cleveland Clinic; Cleveland Ohio U.S.A
| | - Ruta Yardi
- Cleveland Clinic Epilepsy Center; Neurological Institute; Cleveland Clinic; Cleveland Ohio U.S.A
| | - Michael W. Kattan
- Quantitative Health Sciences; Cleveland Clinic; Cleveland Ohio U.S.A
| | - Dileep Nair
- Cleveland Clinic Epilepsy Center; Neurological Institute; Cleveland Clinic; Cleveland Ohio U.S.A
| | - Ajay Gupta
- Cleveland Clinic Epilepsy Center; Neurological Institute; Cleveland Clinic; Cleveland Ohio U.S.A
| | - Imad Najm
- Cleveland Clinic Epilepsy Center; Neurological Institute; Cleveland Clinic; Cleveland Ohio U.S.A
| | - William Bingaman
- Cleveland Clinic Epilepsy Center; Neurological Institute; Cleveland Clinic; Cleveland Ohio U.S.A
| | - Jorge Gonzalez-Martinez
- Cleveland Clinic Epilepsy Center; Neurological Institute; Cleveland Clinic; Cleveland Ohio U.S.A
| | - Lara Jehi
- Cleveland Clinic Epilepsy Center; Neurological Institute; Cleveland Clinic; Cleveland Ohio U.S.A
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Yankam Njiwa J, Gray K, Costes N, Mauguiere F, Ryvlin P, Hammers A. Advanced [(18)F]FDG and [(11)C]flumazenil PET analysis for individual outcome prediction after temporal lobe epilepsy surgery for hippocampal sclerosis. NEUROIMAGE-CLINICAL 2014; 7:122-31. [PMID: 25610774 PMCID: PMC4299974 DOI: 10.1016/j.nicl.2014.11.013] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2014] [Revised: 11/10/2014] [Accepted: 11/15/2014] [Indexed: 11/20/2022]
Abstract
Purpose We have previously shown that an imaging marker, increased periventricular [11C]flumazenil ([11C]FMZ) binding, is associated with failure to become seizure free (SF) after surgery for temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS). Here, we investigated whether increased preoperative periventricular white matter (WM) signal can be detected on clinical [18F]FDG-PET images. We then explored the potential of periventricular FDG WM increases, as well as whole-brain [11C]FMZ and [18F]FDG images analysed with random forest classifiers, for predicting surgery outcome. Methods Sixteen patients with MRI-defined HS had preoperative [18F]FDG and [11C]FMZ-PET. Fifty controls had [18F]FDG-PET (30), [11C]FMZ-PET (41), or both (21). Periventricular WM signal was analysed using Statistical Parametric Mapping (SPM8), and whole-brain image classification was performed using random forests implemented in R (http://www.r-project.org). Surgery outcome was predicted at the group and individual levels. Results At the group level, non-seizure free (NSF) versus SF patients had periventricular increases with both tracers. Against controls, NSF patients showed more prominent periventricular [11C]FMZ and [18F]FDG signal increases than SF patients. All differences were more marked for [11C]FMZ. For individuals, periventricular WM signal increases were seen at optimized thresholds in 5/8 NSF patients for both tracers. For SF patients, 1/8 showed periventricular signal increases for [11C]FMZ, and 4/8 for [18F]FDG. Hence, [18F]FDG had relatively poor sensitivity and specificity. Random forest classification accurately identified 7/8 SF and 7/8 NSF patients using [11C]FMZ images, but only 4/8 SF and 6/8 NSF patients with [18F]FDG. Conclusion This study extends the association between periventricular WM increases and NSF outcome to clinical [18F]FDG-PET, but only at the group level. Whole-brain random forest classification increases [11C]FMZ-PET's performance for predicting surgery outcome.
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Affiliation(s)
- J. Yankam Njiwa
- Neurodis Foundation, Lyon, France
- Correspondence to: Cermep. — Imagerie du vivant, 59 Boulevard Pinel, Lyon/Bron 69677, France. Tel: +33 4 72 68 86 34.
| | - K.R. Gray
- Department Of Computing, Biomedical Image Analysis Group, Imperial College London, UK
| | - N. Costes
- Cermep-Imagerie du vivant, Lyon, France
| | - F. Mauguiere
- Université Lyon 1, Inserm, CNRS, Centre De Recherche en Neuroscience de Lyon, France
- Service de Neurologie Fonctionnelle et d'Epileptologie, Hôpital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, France
- Université De Lyon, Université Claude Bernard, Lyon, France
| | - P. Ryvlin
- Université Lyon 1, Inserm, CNRS, Centre De Recherche en Neuroscience de Lyon, France
- Service de Neurologie Fonctionnelle et d'Epileptologie, Hôpital Neurologique Pierre Wertheimer, Hospices Civils de Lyon, France
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Hamdy NA, Alamgir MJ, Mohammad EGE, Khedr MH, Fazili S. Profile of epilepsy in a regional hospital in Al qassim, saudi arabia. Int J Health Sci (Qassim) 2014; 8:247-55. [PMID: 25505860 PMCID: PMC4257360 DOI: 10.12816/0023977] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
INTRODUCTION Epilepsy is a diverse set of chronic neurological disorders characterized by seizures. It is one of the most common of the serious neurological disorders. About 3% of people will be diagnosed with epilepsy at some time in their lives. OBJECTIVES We aimed to address the commonest types of seizures, their aetiologies, EEG and neuroimaging results and prognosis of patients presented to neurology services of the King Fahad Specialist Hospital- AlQassim (KFSH). METHODOLOGY In this retrospective epidemiological study we investigated the medical records of patients with epilepsy, who attended the neurology services of KFSH, during the study period (26/10/2011-26/4/2012). RESULTS The study included 341 patients; 189 (55.4%) males and 152 (44.6%) females. Their ages ranged between 12 and 85 years (mean ± SD = 31±16.9). The majority of patients had Generalised Tonic Clonic Seizures (76.2%), followed by Complex Partial Seizures (7.6%). 73% of our patients had idiopathic epilepsy. The commonest causes for symptomatic epilepsy were Cerebro Vascular Accidents and Head trauma. Hemiplegia, mental retardation and psychiatric illness were the commonest comorbidity. 69.3% of patients had controlled seizures. Patients with idiopathic epilepsy were significantly controlled than patients with symptomatic epilepsy (P=0.01), and those using one Anti Epileptic Drug were significantly controlled compared to patients using polytherapy (P=0.0001) there was no significant relation between controlled seizure and duration of illness or hospitalization or EEG changes. CONCLUSION Seizure types, aetiology, drug therapy, Comorbidities and outcome in a tertiary care hospital in Saudi Arabia are similar to previous local and international studies. 35.3% of patients were hospitalized, higher rates than previous studies. Seizure control was better in generalized seizures and idiopathic epilepsy compared to complex partial seizures or partial seizures with secondary generalization and symptomatic epilepsy.
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Affiliation(s)
| | | | | | | | - Shafat Fazili
- King Fahad Specialist Hospital, Al Qassim, Saudi Arabia
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Zhang J, Liu W, Chen H, Xia H, Zhou Z, Mei S, Liu Q, Li Y. Identification of common predictors of surgical outcomes for epilepsy surgery. Neuropsychiatr Dis Treat 2013; 9:1673-82. [PMID: 24235833 PMCID: PMC3825696 DOI: 10.2147/ndt.s53802] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Although epilepsy surgery is an effective treatment for patients with drug-resistant epilepsy, surgical outcomes vary across patient groups and studies. Identification of reliable prognostic factors for surgical outcome is important for outcome research. In this study, recent systematic reviews and meta-analyses on prediction of seizure outcome have been analyzed, and common predictors of seizure outcome or unrelated factors for temporal lobe epilepsy (TLE), lesional extratemporal lobe epilepsy (ETLE), and tuberous sclerosis complex have been identified. Clinical factors such as lesional epilepsy, abnormal magnetic resonance imaging, partial seizures, and complete resection were found to be common positive predictors, and factors such as nonlesional epilepsy, poorly defined and localized epileptic focus, generalized seizures, and incomplete resection are common negative predictors, while factors such as age at surgery and side of surgery are unrelated to seizure outcome for TLE and lesional ETLE. In addition, diagnostic neuroimaging and resection are among the most important predictors of seizure outcome. However, common predictors of seizure outcome could not be identified in nonlesional ETLE because no predictors were found to be significant in adult patients (by meta-analysis), and outcome prediction is difficult in this case. Meta-analysis of other outcomes, such as neuropsychologic outcomes, is rare due to lack of evaluation standards. Further studies on identification of reliable predictors of surgical outcomes are needed.
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Affiliation(s)
- Jing Zhang
- School of Biomedical Engineering, Capital Medical University, Beijing, People’s Republic of China
| | - Weifang Liu
- School of Biomedical Engineering, Capital Medical University, Beijing, People’s Republic of China
| | - Hui Chen
- School of Biomedical Engineering, Capital Medical University, Beijing, People’s Republic of China
| | - Hong Xia
- School of Biomedical Engineering, Capital Medical University, Beijing, People’s Republic of China
| | - Zhen Zhou
- School of Biomedical Engineering, Capital Medical University, Beijing, People’s Republic of China
| | - Shanshan Mei
- Department of Functional Neurology and Neurosurgery, Beijing Haidian Hospital, Beijing, People’s Republic of China
| | - Qingzhu Liu
- Department of Functional Neurology and Neurosurgery, Beijing Haidian Hospital, Beijing, People’s Republic of China
| | - Yunlin Li
- Department of Functional Neurology and Neurosurgery, Beijing Haidian Hospital, Beijing, People’s Republic of China
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