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Lolk K, Werenberg Dreier J, Christensen J. Individual and neighborhood-level socioeconomic deprivation and risk of epilepsy after traumatic brain Injury: A register-based cohort study. Epilepsy Behav 2024; 156:109807. [PMID: 38678986 DOI: 10.1016/j.yebeh.2024.109807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/21/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Affiliation(s)
- Kasper Lolk
- National Centre for Register-based Research, Aarhus BSS, Aarhus University, Denmark; Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark.
| | - Julie Werenberg Dreier
- National Centre for Register-based Research, Aarhus BSS, Aarhus University, Denmark; Centre for Integrated Register-based Research, CIRRAU, Aarhus University, Aarhus, Denmark
| | - Jakob Christensen
- National Centre for Register-based Research, Aarhus BSS, Aarhus University, Denmark; Department of Neurology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Denmark
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Boßelmann CM, Ivaniuk A, St John M, Taylor SC, Krishnaswamy G, Milinovich A, Leu C, Gupta A, Pestana-Knight EM, Najm I, Lal D. Healthcare utilization and clinical characteristics of genetic epilepsy in electronic health records. Brain Commun 2024; 6:fcae090. [PMID: 38524155 PMCID: PMC10959483 DOI: 10.1093/braincomms/fcae090] [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: 10/29/2023] [Revised: 02/05/2024] [Accepted: 03/12/2024] [Indexed: 03/26/2024] Open
Abstract
Understanding the clinical characteristics and medical treatment of individuals affected by genetic epilepsies is instrumental in guiding selection for genetic testing, defining the phenotype range of these rare disorders, optimizing patient care pathways and pinpointing unaddressed medical need by quantifying healthcare resource utilization. To date, a matched longitudinal cohort study encompassing the entire spectrum of clinical characteristics and medical treatment from childhood through adolescence has not been performed. We identified individuals with genetic and non-genetic epilepsies and onset at ages 0-5 years by linkage across the Cleveland Clinic Health System. We used natural language processing to extract medical terms and procedures from longitudinal electronic health records and tested for cross-sectional and temporal associations with genetic epilepsy. We implemented a two-stage design: in the discovery cohort, individuals were stratified as being 'likely genetic' or 'non-genetic' by a natural language processing algorithm, and controls did not receive genetic testing. The validation cohort consisted of cases with genetic epilepsy confirmed by manual chart review and an independent set of controls who received negative genetic testing. The discovery and validation cohorts consisted of 503 and 344 individuals with genetic epilepsy and matched controls, respectively. The median age at the first encounter was 0.1 years and 7.9 years at the last encounter, and the mean duration of follow-up was 8.2 years. We extracted 188,295 Unified Medical Language System annotations for statistical analysis across 9659 encounters. Individuals with genetic epilepsy received an earlier epilepsy diagnosis and had more frequent and complex encounters with the healthcare system. Notably, the highest enrichment of encounters compared with the non-genetic groups was found during the transition from paediatric to adult care. Our computational approach could validate established comorbidities of genetic epilepsies, such as behavioural abnormality and intellectual disability. We also revealed novel associations for genitourinary abnormalities (odds ratio 1.91, 95% confidence interval: 1.66-2.20, P = 6.16 × 10-19) linked to a spectrum of underrecognized epilepsy-associated genetic disorders. This case-control study leveraged real-world data to identify novel features associated with the likelihood of a genetic aetiology and quantified the healthcare utilization of genetic epilepsies compared with matched controls. Our results strongly recommend early genetic testing to stratify individuals into specialized care paths, thus improving the clinical management of people with genetic epilepsies.
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Affiliation(s)
- Christian M Boßelmann
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Alina Ivaniuk
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Mark St John
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Sara C Taylor
- Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | | | - Alex Milinovich
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Costin Leu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, WC1N 3BG, UK
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Center for Neurogenetics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ajay Gupta
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | | | - Imad Najm
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Dennis Lal
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Center for Neurogenetics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and M.I.T., Cambridge, MA 02142, USA
- Cologne Center for Genomics (CCG), University of Cologne, 50931 Cologne, Germany
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Kerr WT, McFarlane KN. Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist. Curr Neurol Neurosci Rep 2023; 23:869-879. [PMID: 38060133 DOI: 10.1007/s11910-023-01318-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 12/08/2023]
Abstract
PURPOSE OF REVIEW Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice. RECENT FINDINGS ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.
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Affiliation(s)
- Wesley T Kerr
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Biomedical Informatics, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA.
- Department of Neurology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
| | - Katherine N McFarlane
- Department of Neurology, University of Pittsburgh, 3471 Fifth Ave, Kaufmann 811.22, Pittsburgh, PA, 15213, USA
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Vulpius SA, Werge S, Jørgensen IF, Siggaard T, Hernansanz Biel J, Knudsen GM, Brunak S, Pinborg LH. Text mining of electronic health records can validate a register-based diagnosis of epilepsy and subgroup into focal and generalized epilepsy. Epilepsia 2023; 64:2750-2760. [PMID: 37548470 DOI: 10.1111/epi.17734] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/01/2023] [Accepted: 08/01/2023] [Indexed: 08/08/2023]
Abstract
OBJECTIVE Combining population-based health registries and electronic health records offers the opportunity to create large, phenotypically detailed patient cohorts of high quality. In this study, we used text mining of clinical notes to confirm International Classification of Diseases, 10th Revision (ICD-10)-registered epilepsy diagnoses and classify patients according to focal and generalized epilepsy types. METHODS Using the Danish National Patient Registry, we identified patients who between 2006 and 2016 received an ICD-10 diagnosis of epilepsy. To validate the epilepsy diagnosis and stratify patients into focal and generalized epilepsy types, we constructed dictionaries for text mining-based extraction of clinical notes. Two physicians manually reviewed the clinical notes for a total of 527 patients and assigned epilepsy diagnoses, which were compared with the text-mined diagnoses. RESULTS We identified 23 632 patients with an ICD-10 diagnosis of epilepsy, of whom 50% were registered with an unspecified epilepsy diagnosis. In total, 11 211 patients were considered likely to have epilepsy by text mining, with an F1 measure ranging from 82% to 90%. Manual review of the electronic health records for 310 patients revealed a false discovery rate of 29%. This rate was decreased to 4% by the text mining algorithm. The weighted average F1 measure for text mining-assigned epilepsy types was 79% (82% for focal and 76% for generalized epilepsy). Text mining successfully assigned a focal or generalized epilepsy type to 92% of the text mining-eligible patients registered with unspecified epilepsy. SIGNIFICANCE Text mining of electronic health records can be used to establish a patient cohort with much higher likelihood of having a diagnosis of epilepsy and a focal or generalized epilepsy type compared to the cohort created from ICD-10 epilepsy codes alone. We believe the concept will be essential for future genome-wide and phenome-wide association studies and subsequently the development of precision medicine for epilepsy patients.
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Affiliation(s)
- Siri A Vulpius
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian Werge
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Isabella Friis Jørgensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Troels Siggaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Jorge Hernansanz Biel
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Gitte M Knudsen
- Epilepsy Clinic and Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Institute for Clinical Medicine, Faculty of Health and Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Lars H Pinborg
- Epilepsy Clinic and Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
- Institute for Clinical Medicine, Faculty of Health and Medicine, University of Copenhagen, Copenhagen, Denmark
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