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The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

More than 100 genetic etiologies have been identified in developmental and epileptic encephalopathies (DEEs), but correlating genetic findings with clinical features at scale has remained a hurdle because of a lack of frameworks for analyzing heterogenous clinical data. Here, we analyzed 31,742 Human Phenotype Ontology (HPO) terms in 846 individuals with existing whole-exome trio data and assessed associated clinical features and phenotypic relatedness by using HPO-based semantic similarity analysis for individuals with de novo variants in the same gene. Gene-specific phenotypic signatures included associations of SCN1A with "complex febrile seizures" (HP: 0011172; p = 2.1 × 10-5) and "focal clonic seizures" (HP: 0002266; p = 8.9 × 10-6), STXBP1 with "absent speech" (HP: 0001344; p = 1.3 × 10-11), and SLC6A1 with "EEG with generalized slow activity" (HP: 0010845; p = 0.018). Of 41 genes with de novo variants in two or more individuals, 11 genes showed significant phenotypic similarity, including SCN1A (n = 16, p < 0.0001), STXBP1 (n = 14, p = 0.0021), and KCNB1 (n = 6, p = 0.011). Including genetic and phenotypic data of control subjects increased phenotypic similarity for all genetic etiologies, whereas the probability of observing de novo variants decreased, emphasizing the conceptual differences between semantic similarity analysis and approaches based on the expected number of de novo events. We demonstrate that HPO-based phenotype analysis captures unique profiles for distinct genetic etiologies, reflecting the breadth of the phenotypic spectrum in genetic epilepsies. Semantic similarity can be used to generate statistical evidence for disease causation analogous to the traditional approach of primarily defining disease entities through similar clinical features.

Aim: To determine the long‐term impact of telemedicine in child neurology care during the COVID‐19 pandemic and with the reopening of outpatient clinics. Method: We performed an observational cohort study of 34 837 in‐person visits and 14 820 telemedicine outpatient visits across 26 399 individuals. We assessed differences in care across visit types, time‐period observed, time between follow‐ups, patient portal activation rates, and demographic factors. Results: We observed a higher proportion of telemedicine for epilepsy (International Classification of Diseases, 10th Revision G40: odds ratio [OR] 1.4, 95% confidence interval [CI] 1.3–1.5) and a lower proportion for movement disorders (G25: OR 0.7, 95% CI 0.6–0.8; R25: OR 0.7, 95% CI 0.6–0.9) relative to in‐person visits. Infants were more likely to be seen in‐person after reopening clinics than by telemedicine (OR 1.6, 95% CI 1.5–1.8) as were individuals with neuromuscular disorders (OR 1.6, 95% CI 1.5–1.7). Self‐reported racial and ethnic minority populations and those with highest social vulnerability had lower telemedicine participation rates (OR 0.8, 95% CI 0.8–0.8; OR 0.7, 95% CI 0.7–0.8). Interpretation: Telemedicine continued to be utilized even once in‐person clinics were available. Pediatric epilepsy care can often be performed using telemedicine while young patients with neuromuscular disorders often require in‐person assessment. Prominent barriers for socially vulnerable families and racial and ethnic minorities persist. [ABSTRACT FROM AUTHOR]

Representative vignettes for VOCs Based on review of telemedicine VOCs, we selected representative audio-video telemedicine encounters that demonstrate how providers determined labeling a patient encounter concerning. This study tracks pediatric neurology telemedicine visits and finds that telemedicine can be used in most encounters, and it is possible to successfully track and adjust for visits flagged as in need of an in-person evaluation. The percentage of VOCs were stable over time between 16th March 2020 and 6th November 2020 (Figure 1), and the percentage was the same as reported previously (5%; 65/1285) for a shorter initial time period (16th March 2020-24th April 2020).9 VOCs involved both follow-up patient visits (57%; 190/333) and new patient visits (43%; 143/333). This larger data set assessed telemedicine visits beyond the initial period of telemedicine initiation and confirmed our earlier observation,9 suggesting that issues arising in telemedicine are dealt with adequately and promptly. [Extracted from the article]

Febrile infection‐related epilepsy syndrome (FIRES) is a devastating epilepsy characterized by new‐onset refractory status epilepticus with a prior febrile infection. We performed exome sequencing in 50 individuals with FIRES, including 27 patient–parent trios and 23 single probands, none of whom had pathogenic variants in established genes for epilepsies or neurodevelopmental disorders. We also performed HLA sequencing in 29 individuals with FIRES and 529 controls, which failed to identify prominent HLA alleles. The genetic architecture of FIRES is substantially different from other developmental and epileptic encephalopathies, and the underlying etiology remains elusive, requiring novel approaches to identify the underlying causative factors. [ABSTRACT FROM AUTHOR]

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52 143 individuals, reconstructing clinical histories using a large-scale data-mining approach of the electronic medical records from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of 26 broad speech and language diagnoses. We used natural language processing to assess the degree to which clinical diagnoses in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be retrieved easily through ICD-10 diagnosis codes, whereas stuttering as a speech phenotype was coded in only 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and, to a lesser degree, with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our analysis of electronic medical records were STXBP1 (n = 21), PTEN (n = 20) and CACNA1A (n = 18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P = 8.57 × 10-7, 95% confidence interval = 18.62-130.39) and MYO7A with speech and language development delay attributable to hearing loss (P = 1.24 × 10-5, 95% confidence interval = 17.46-infinity). Finally, in a sub-cohort of 726 individuals with whole-exome sequencing data, we identified an enrichment of rare variants in neuronal receptor pathways, in addition to associations of UQCRC1 and KIF17 with expressive aphasia, MROH8 and BCHE with poor speech, and USP37, SLC22A9 and UMODL1 with aphasia. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype-phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

EEG plays an integral part in the diagnosis and management of children with genetic epilepsies. Nevertheless, how quantitative EEG features differ between genetic epilepsies and neurological outcomes remains largely unknown. Here, we aimed to identify quantitative EEG biomarkers in children with epilepsy and a genetic diagnosis in STXBP1 , SCN1A , or SYNGAP1 , and to assess how quantitative EEG features associate with neurological outcomes in genetic epilepsies more broadly. We analyzed individuals with pathogenic variants in STXBP1 (95 EEGs, n =20), SCN1A (154 EEGs, n =68), and SYNGAP1 (46 EEGs, n =21) and a control cohort of individuals without epilepsy or known cerebral disease (847 EEGs, n =806). After removing artifacts and epochs with excess noise or altered state from EEGs, we extracted spectral features. We validated our preprocessing pipeline by comparing automatically-detected posterior dominant rhythm (PDR) to annotations from clinical EEG reports. Next, as a coarse measure of pathological slowing, we compared the alpha-delta bandpower ratio between controls and the different genetic epilepsies. We then trained random forest models to predict a diagnosis of STXBP1 , SCN1A , and SYNGAP1 . Finally, to understand how EEG features vary with neurological outcomes, we trained random forest models to predict seizure frequency and motor function. There was strong agreement between the automatically-calculated PDR and clinical EEG reports ( R 2 =0.75). Individuals with STXBP1 -related epilepsy have a significantly lower alpha-delta ratio than controls ( P <0.001) across all age groups. Additionally, individuals with a missense variant in STXBP1 have a significantly lower alpha-delta ratio than those with a protein-truncating variant in toddlers ( P <0.001), children ( P =0.02), and adults ( P <0.001). Models accurately predicted a diagnosis of STXBP1 (AUC=0.91), SYNGAP1 (AUC=0.82), and SCN1A (AUC=0.86) against controls and from each other in a three-class model (accuracy=0.74). From these models, we isolated highly correlated biomarkers for these respective genetic disorders, including alpha-theta ratio in frontal, occipital, and parietal electrodes with STXBP1 , SYNGAP1 , and SCN1A , respectively. Models were unable to predict seizure frequency (AUC=0.53). Random forest models predicted motor scores significantly better than age-based null models ( P <0.001), suggesting spectral features contain information pertinent to gross motor function. In summary, we demonstrate that STXBP1 -, SYNGAP1 -, and SCN1A -related epilepsies have distinct quantitative EEG signatures. Furthermore, EEG spectral features are predictive of some functional outcome measures in patients with genetic epilepsies. Large-scale retrospective quantitative analysis of clinical EEG has the potential to discover novel biomarkers and to quantify and track individuals' disease progression across development., Competing Interests: Competing interests The authors report no competing interests.