Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals.

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 (EMR). 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 1,925 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 prior to molecular diagnosis. Across all 710 genetic etiologies identified in our cohort, neurodevelopmental differences between 6-9 months increased the likelihood of a later molecular diagnosis fivefold (P<0.0001, 95% CI=3.55-7.42). A later diagnosis of SCN1A-related disorders (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 EMR analysis has the potential for earlier targeted therapeutic strategies in the genetic epilepsies.

Child neurology telemedicine: Analyzing 14 820 patient encounters during the first year of the COVID-19 pandemic.

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.

Diagnostic Yield of CSF Testing in Infants for Disorders of Biogenic Amine Neurotransmitter Metabolism.

BACKGROUND AND OBJECTIVES: Biochemical testing of CSF for neurotransmitter metabolites and their cofactors is often used in the diagnostic evaluation of infants with neurologic disorders but requires an invasive, labor-intensive procedure with many potential sources of error. Our aim was to determine the diagnostic yield of CSF testing for biogenic amines (serotonin, norepinephrine, epinephrine, and dopamine) and their cofactors in identifying inborn errors of neurotransmitter metabolism among infants. METHODS: We evaluated all infants aged 1 year or younger who underwent CSF biogenic amine neurotransmitter (CSFNT) testing at Children's Hospital of Philadelphia (CHOP) and Boston Children's Hospital (BCH) between 2008 and 2017 in this cross-sectional study. The primary outcome was the proportion of individuals who received a diagnostic result from CSFNT testing. Secondary assessments included the proportion of infants who obtained a diagnostic result from other types of diagnostic testing. RESULTS: The cohort included 323 individuals (191 from CHOP and 232 from BCH). The median age at presentation was 110 days (range 36-193). The most common presenting features were seizures (71%), hypotonia (47%), and developmental delay (43%). The diagnostic yield of CSFNT testing was zero. When CSF pyridoxal-5-phosphate level was assayed with CSFNT testing, 1 patient had a diagnostic result. An etiologic diagnosis was identified in 163 patients (50%) of the cohort, with genetic testing having the highest yield (120 individuals, 37%). DISCUSSION: Our findings support the case for deimplementation of CSFNT testing as a standard diagnostic test of etiology in infants aged 1 year or younger presenting with neurologic disorders.

The clinical and genetic spectrum of paediatric speech and language disorders in 52,143 individuals.

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 (EMR) 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 twenty-six broad speech and language diagnoses. We used Natural Language Processing to assess to which degree clinical diagnosis in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be easily retrieved through ICD-10 diagnosis codes, while stuttering as a speech phenotype was only coded in 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 EMR analysis 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, CI=18.62-130.39) and MYO7A with speech and language development delay due to hearing loss (P=1.24 × 10-5, CI=17.46-Inf). Finally, in a sub-cohort of 726 individuals with whole exome sequencing data, we identified an enrichment of rare variants in synaptic protein and neuronal receptor pathways and associations of UQCRC1 with expressive aphasia and WASHC4 with abnormality of speech or vocalization. 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.

Metabolomic and Immunologic Discriminators of MIS-C at Emergency Room Presentation.

Multisystem Inflammatory Syndrome in Childhood (MIS-C) follows SARS-CoV-2 infection and frequently leads to intensive care unit admission. The inability to rapidly discriminate MIS-C from similar febrile illnesses delays treatment and leads to misdiagnosis. To identify diagnostic discriminators at the time of emergency department presentation, we enrolled 104 children who met MIS-C screening criteria, 14 of whom were eventually diagnosed with MIS-C. Before treatment, we collected breath samples for volatiles and peripheral blood for measurement of plasma proteins and immune cell features. Clinical and laboratory features were used as inputs for a machine learning model to determine diagnostic importance. MIS-C was associated with significant changes in breath volatile organic compound (VOC) composition as well as increased plasma levels of secretory phospholipase A2 (PLA2G2A) and lipopolysaccharide binding protein (LBP). In an integrated model of all analytes, the proportion of TCRVß21.3+ non-naive CD4 T cells expressing Ki-67 had a high sensitivity and specificity for MIS-C, with diagnostic accuracy further enhanced by low sodium and high PLA2G2A. We anticipate that accurate diagnosis will become increasingly difficult as MIS-C becomes less common. Clinical validation and application of this diagnostic model may improve outcomes in children presenting with multisystem febrile illnesses.