1
|
Galer PD, Parthasarathy S, Xian J, McKee JL, Ruggiero SM, Ganesan S, Kaufman MC, Cohen SR, Haag S, Chen C, Ojemann WKS, Kim D, Wilmarth O, Vaidiswaran P, Sederman C, Ellis CA, Gonzalez AK, Boßelmann CM, Lal D, Sederman R, Lewis-Smith D, Litt B, Helbig I. Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals. Genet Med 2024; 26:101211. [PMID: 39011766 DOI: 10.1016/j.gim.2024.101211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 07/10/2024] [Accepted: 07/10/2024] [Indexed: 07/17/2024] Open
Abstract
PURPOSE An early genetic diagnosis can guide the time-sensitive treatment of individuals with genetic epilepsies. However, most genetic diagnoses occur long after disease onset. We aimed to identify early clinical features suggestive of genetic diagnoses in individuals with epilepsy through large-scale analysis of full-text electronic medical records. METHODS We extracted 89 million time-stamped standardized clinical annotations using Natural Language Processing from 4,572,783 clinical notes from 32,112 individuals with childhood epilepsy, including 1925 individuals with known or presumed genetic epilepsies. We applied these features to train random forest models to predict SCN1A-related disorders and any genetic diagnosis. RESULTS We identified 47,774 age-dependent associations of clinical features with genetic etiologies a median of 3.6 years before molecular diagnosis. Across all 710 genetic etiologies identified in our cohort, neurodevelopmental differences between 6 to 9 months increased the likelihood of a later molecular diagnosis 5-fold (P < .0001, 95% CI = 3.55-7.42). A later diagnosis of SCN1A-related disorders (area under the curve [AUC] = 0.91) or an overall positive genetic diagnosis (AUC = 0.82) could be reliably predicted using random forest models. CONCLUSION Clinical features predictive of genetic epilepsies precede molecular diagnoses by up to several years in conditions with known precision treatments. An earlier diagnosis facilitated by automated electronic medical records analysis has the potential for earlier targeted therapeutic strategies in the genetic epilepsies.
Collapse
Affiliation(s)
- Peter D Galer
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA; University of Pennsylvania, Center for Neuroengineering and Therapeutics, Philadelphia, PA
| | - Shridhar Parthasarathy
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Julie Xian
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Jillian L McKee
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Sarah M Ruggiero
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Shiva Ganesan
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Michael C Kaufman
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Stacey R Cohen
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Scott Haag
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA
| | | | - William K S Ojemann
- University of Pennsylvania, Center for Neuroengineering and Therapeutics, Philadelphia, PA
| | | | - Olivia Wilmarth
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Priya Vaidiswaran
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Casey Sederman
- Department of Human Genetics, University of Utah, Salt Lake City, UT; Utah Center for Genetic Discovery, School of Medicine, University of Utah, Salt Lake City, UT
| | - Colin A Ellis
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Alexander K Gonzalez
- Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA
| | - Christian M Boßelmann
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH
| | - Dennis Lal
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH; Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany
| | | | - David Lewis-Smith
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK; Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle-upon-Tyne, UK; FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin 2, Ireland
| | - Brian Litt
- University of Pennsylvania, Center for Neuroengineering and Therapeutics, Philadelphia, PA; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Ingo Helbig
- Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA; Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA; The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA; Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
| |
Collapse
|
2
|
Sheidley BR, Malinowski J, Bergner AL, Bier L, Gloss DS, Mu W, Mulhern MM, Partack EJ, Poduri A. Genetic testing for the epilepsies: A systematic review. Epilepsia 2021; 63:375-387. [PMID: 34893972 DOI: 10.1111/epi.17141] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 12/29/2022]
Abstract
OBJECTIVE Numerous genetic testing options for individuals with epilepsy have emerged over the past decade without clear guidelines regarding optimal testing strategies. We performed a systematic evidence review (SER) and conducted meta-analyses of the diagnostic yield of genetic tests commonly utilized for patients with epilepsy. We also assessed nonyield outcomes (NYOs) such as changes in treatment and/or management, prognostic information, recurrence risk determination, and genetic counseling. METHODS We performed an SER, in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), using PubMed, Embase, CINAHL, and Cochrane Central through December of 2020. We included studies that utilized genome sequencing (GS), exome sequencing (ES), multigene panel (MGP), and/or genome-wide comparative genomic hybridization/chromosomal microarray (CGH/CMA) in cohorts (n ≥ 10) ascertained for epilepsy. Quality assessment was undertaken using ROBINS-I (Risk of Bias in Non-Randomized Studies of Interventions). We estimated diagnostic yields and 95% confidence intervals with random effects meta-analyses and narratively synthesized NYOs. RESULTS From 5985 nonduplicated articles published through 2020, 154 met inclusion criteria and were included in meta-analyses of diagnostic yield; 43 of those were included in the NYO synthesis. The overall diagnostic yield across all test modalities was 17%, with the highest yield for GS (48%), followed by ES (24%), MGP (19%), and CGH/CMA (9%). The only phenotypic factors that were significantly associated with increased yield were (1) the presence of developmental and epileptic encephalopathy and/or (2) the presence of neurodevelopmental comorbidities. Studies reporting NYOs addressed clinical and personal utility of testing. SIGNIFICANCE This comprehensive SER, focused specifically on the literature regarding patients with epilepsy, provides a comparative assessment of the yield of clinically available tests, which will help shape clinician decision-making and policy regarding insurance coverage for genetic testing. We highlight the need for prospective assessment of the clinical and personal utility of genetic testing for patients with epilepsy and for standardization in reporting patient characteristics.
Collapse
Affiliation(s)
- Beth R Sheidley
- Epilepsy Genetics Program, Division of Epilepsy and Neurophysiology, Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | | | - Amanda L Bergner
- Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Louise Bier
- Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, New York, USA
| | - David S Gloss
- Department of Neurology, Charleston Area Medical Center, Charleston, West Virginia, USA
| | - Weiyi Mu
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Maureen M Mulhern
- Department of Pathology, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA.,Department of Neurology, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Emily J Partack
- Genomics Services, Quest Diagnostics, Marlborough, Massachusetts, USA
| | - Annapurna Poduri
- Epilepsy Genetics Program, Division of Epilepsy and Neurophysiology, Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|