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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., Competing Interests: Conflict of Interest The authors declare no conflicts of interest., (Copyright © 2024 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights reserved.)

Purpose: Childhood epilepsies have a strong genetic contribution, but the disease trajectory for many genetic etiologies remains unknown. Electronic medical record (EMR) data potentially allow for the analysis of longitudinal clinical information but this has not yet been explored., Methods: We analyzed provider-entered neurological diagnoses made at 62,104 patient encounters from 658 individuals with known or presumed genetic epilepsies. To harmonize clinical terminology, we mapped clinical descriptors to Human Phenotype Ontology (HPO) terms and inferred higher-level phenotypic concepts. We then binned the resulting 286,085 HPO terms to 100 3-month time intervals and assessed gene-phenotype associations at each interval., Results: We analyzed a median follow-up of 6.9 years per patient and a cumulative 3251 patient years. Correcting for multiple testing, we identified significant associations between "Status epilepticus" with SCN1A at 1.0 years, "Severe intellectual disability" with PURA at 9.75 years, and "Infantile spasms" and "Epileptic spasms" with STXBP1 at 0.5 years. The identified associations reflect known clinical features of these conditions, and manual chart review excluded provider bias., Conclusion: Some aspects of the longitudinal disease histories can be reconstructed through EMR data and reveal significant gene-phenotype associations, even within closely related conditions. Gene-specific EMR footprints may enable outcome studies and clinical decision support.