The clinical and genetic spectrum of paediatric speech and language disorders.

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.

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. 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.

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.

A Comparison of Ketamine and Midazolam as First-Line Anesthetic Infusions for Pediatric Status Epilepticus.

BACKGROUND: Pediatric refractory status epilepticus (RSE) often requires management with anesthetic infusions, but few data compare first-line anesthetics. This study aimed to compare the efficacy and adverse effects of midazolam and ketamine infusions as first-line anesthetics for pediatric RSE. METHODS: Retrospective single-center study of consecutive study participants treated with ketamine or midazolam as the first-line anesthetic infusions for RSE at a quaternary care children's hospital from December 1, 2017, until September 15, 2021. RESULTS: We identified 117 study participants (28 neonates), including 79 (68%) who received midazolam and 38 (32%) who received ketamine as the first-line anesthetic infusions. Seizures terminated more often in study participants administered ketamine (61%, 23/38) than midazolam (28%, 22/79; odds ratio [OR] 3.97, 95% confidence interval [CI] 1.76-8.98; P < 0.01). Adverse effects occurred more often in study participants administered midazolam (24%, 20/79) than ketamine (3%, 1/38; OR 12.54, 95% CI 1.61-97.43; P = 0.016). Study participants administered ketamine were younger, ketamine was used more often for children with acute symptomatic seizures, and midazolam was used more often for children with epilepsy. Multivariable logistic regression of seizure termination by first-line anesthetic infusion (ketamine or midazolam) including age at SE onset, SE etiology category, and individual seizure duration at anesthetic infusion initiation indicated seizures were more likely to terminate following ketamine than midazolam (OR 4.00, 95% CI 1.69-9.49; P = 0.002) and adverse effects were more likely following midazolam than ketamine (OR 13.41, 95% CI 1.61-111.04; P = 0.016). Survival to discharge was higher among study participants who received midazolam (82%, 65/79) than ketamine (55%, 21/38; P = 0.002), although treating clinicians did not attribute any deaths to ketamine or midazolam. CONCLUSIONS: Among children and neonates with RSE, ketamine was more often followed by seizure termination and less often associated with adverse effects than midazolam when administered as the first-line anesthetic infusion. Further prospective data are needed to compare first-line anesthetics for RSE.

Visits of concern in child neurology telemedicine.

AIM: To characterize child neurology telemedicine visits flagged as requiring in-person evaluation during the COVID-19 pandemic. METHOD: We analyzed 7130 audio-video telemedicine visits between March and November 2020. Visits of concern (VOCs) were defined as telemedicine visits where the clinical scenario necessitated in-person follow-up evaluation sooner than if the visit had been conducted in-person. RESULTS: VOCs occurred in 5% (333/7130) of visits for 292 individuals (148 females, 144 males). Providers noted technical challenges more often in VOCs (40%; 133/333) than visits without concern (non-VOCs) (28%; 1922/6797) (p < 0.05). The median age was younger in VOCs (9 years 3 months, interquartile range [IQR] 2 years 0 months-14 years 3 months) than non-VOCs (11 years 3 months, IQR 5 years 10 months-15 years 10 months) (p < 0.05). Median household income was lower for patients with VOCs ($74 K, IQR $55 K-$97 K) compared to non-VOCs ($80 K, IQR $61 K-$100 K) (p < 0.05). Compared with all other race categories, families who self-identified as Black were more likely to have a VOC (odds ratio 1.53, 95% confidence interval 1.21-2.06). Epilepsy and headache represented the highest percentages of VOCs, while neuromuscular disorders and developmental delay had a higher proportion of VOCs than other neurological disorders. INTERPRETATION: These findings suggest that telemedicine is an effective platform for most child neurology visits. Younger children and those with neuromuscular disorders or developmental delays are more likely to require in-person evaluation. WHAT THIS PAPER ADDS: It is possible to successfully flag patients who need in-person assessment. Providers can manage issues arising during telemedicine in 95% of visits. Visits flagged as concerning were likely unrelated to modality of patient care. Provider concern was independent of technical difficulties for most telehealth visits. Younger age may be correlated with need for in-person assessment.

Novel use of a virtual driving assessment to classify driver skill at the time of licensure.

Motor vehicle crash rates are highest immediately after licensure, and driver error is one of the leading causes. Yet, few studies have quantified driving skills at the time of licensure, making it difficult to identify at-risk drivers before independent driving. Using data from a virtual driving assessment implemented into the licensing workflow in Ohio, this study presents the first population-level study classifying degree of skill at the time of licensure and validating these against a measure of on-road performance: license exam outcomes. Principal component and cluster analysis of 33,249 virtual driving assessments identified 20 Skill Clusters that were then grouped into 4 major summary "Driving Classes"; i) No Issues (i.e. careful and skilled drivers); ii) Minor Issues (i.e. an average new driver with minor vehicle control skill deficits); iii) Major Issues (i.e. drivers with more control issues and who take more risks); and iv) Major Issues with Aggression (i.e. drivers with even more control issues and more reckless and risk-taking behavior). Category labels were determined based on patterns of VDA skill deficits alone (i.e. agnostic of the license examination outcome). These Skill Clusters and Driving Classes had different distributions by sex and age, reflecting age-related licensing policies (i.e. those under 18 and subject to GDL and driver education and training), and were differentially associated with subsequent performance on the on-road licensing examination (showing criterion validity). The No Issues and Minor Issues classes had lower than average odds of failing, and the other two more problematic Driving Classes had higher odds of failing. Thus, this study showed that license applicants can be classified based on their driving skills at the time of licensure. Future studies will validate these Skill Cluster classes in relation to their prediction of post-licensure crash outcomes.

A longitudinal footprint of genetic epilepsies using automated electronic medical record interpretation.

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.

Correction: A longitudinal footprint of genetic epilepsies using automated electronic medical record interpretation.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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.

Quantitative EEG Spectral Features Differentiate Genetic Epilepsies and Predict Neurologic Outcomes.

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.

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.

Driving Skills at Licensure and Time to First Crash.

BACKGROUND AND OBJECTIVES: Young drivers are overrepresented in crashes, and newly licensed drivers are at high risk, particularly in the months immediately post-licensure. Using a virtual driving assessment (VDA) implemented in the licensing workflow in Ohio, this study examined how driving skills measured at the time of licensure contribute to crash risk post-licensure in newly licensed young drivers. METHODS: This study examined 16 914 young drivers (<25 years of age) in Ohio who completed the VDA at the time of licensure and their subsequent police-reported crash records. By using the outcome of time to first crash, a Cox proportional hazard model was used to estimate the risk of a crash during the follow-up period as a function of VDA Driving Class (and Skill Cluster) membership. RESULTS: The best performing No Issues Driving Class had a crash risk 10% lower than average (95% confidence interval [CI] 13% to 6%), whereas the Major Issues with Dangerous Behavior Class had a crash risk 11% higher than average (95% CI 1% to 22%). These results withstood adjusting for covariates (age, sex, and tract-level socioeconomic status indicators). At the same time, drivers licensed at age 18 had a crash risk 16% higher than average (95% CI 6% to 27%). CONCLUSIONS: This population-level study reveals that driving skills measured at the time of licensure are a predictor of crashes early in licensure, paving the way for better prediction models and targeted, personalized interventions. The authors of future studies should explore time- and exposure-varying risks.

Leveraging electronic medical record-embedded standardised electroencephalogram reporting to develop neonatal seizure prediction models: a retrospective cohort study.

BACKGROUND: Accurate prediction of seizures can help to direct resource-intense continuous electroencephalogram (CEEG) monitoring to neonates at high risk of seizures. We aimed to use data from standardised EEG reports to generate seizure prediction models for vulnerable neonates. METHODS: In this retrospective cohort study, we included neonates who underwent CEEG during the first 30 days of life at the Children's Hospital of Philadelphia (Philadelphia, PA, USA). The hypoxic ischaemic encephalopathy subgroup included only patients with CEEG data during the first 5 days of life, International Classification of Diseases, revision 10, codes for hypoxic ischaemic encephalopathy, and documented therapeutic hypothermia. In January, 2018, we implemented a novel CEEG reporting system within the electronic medical record (EMR) using common data elements that incorporated standardised terminology. All neonatal CEEG data from Jan 10, 2018, to Feb 15, 2022, were extracted from the EMR using age at the time of CEEG. We developed logistic regression, decision tree, and random forest models of neonatal seizure prediction using EEG features on day 1 to predict seizures on future days. FINDINGS: We evaluated 1117 neonates, including 150 neonates with hypoxic ischaemic encephalopathy, with CEEG data reported using standardised templates between Jan 10, 2018, and Feb 15, 2022. Implementation of a consistent EEG reporting system that documents discrete and standardised EEG variables resulted in more than 95% reporting of key EEG features. Several EEG features were highly correlated, and patients could be clustered on the basis of specific features. However, no simple combination of features adequately predicted seizure risk. We therefore applied computational models to complement clinical identification of neonates at high risk of seizures. Random forest models incorporating background features performed with classification accuracies of up to 90% (95% CI 83-94) for all neonates and 97% (88-99) for neonates with hypoxic ischaemic encephalopathy; recall (sensitivity) of up to 97% (91-100) for all neonates and 100% (100-100) for neonates with hypoxic ischaemic encephalopathy; and precision (positive predictive value) of up to 92% (84-96) in the overall cohort and 97% (80-99) in neonates with hypoxic ischaemic encephalopathy. INTERPRETATION: Using data extracted from the standardised EEG report on the first day of CEEG, we predict the presence or absence of neonatal seizures on subsequent days with classification performances of more than 90%. This information, incorporated into routine care, could guide decisions about the necessity of continuing EEG monitoring beyond the first day, thereby improving the allocation of limited CEEG resources. Additionally, this analysis shows the benefits of standardised clinical data collection, which can drive learning health system approaches to personalised CEEG use. FUNDING: Children's Hospital of Philadelphia, the Hartwell Foundation, the National Institute of Neurological Disorders and Stroke, and the Wolfson Foundation.

Licensing Examination and Crash Outcomes Postlicensure in Young Drivers.

Importance: Despite US graduated driver licensing laws, young novice driver crash rates remain high. Study findings suggest comprehensive license policy that mandates driver education including behind-the-wheel (BTW) training may reduce crashes postlicensure. However, only 15 states mandate BTW training. Objective: To identify differences in licensing and crash outcomes for drivers younger than 18 years who are subject to comprehensive licensing requirements (graduated driver licensing, driver education, and BTW training) vs those aged 18 to 24 years who are exempt from these requirements. Design, Setting, and Participants: This prospective, population-based cohort study used Ohio licensing data to define a cohort of 2018 license applicants (age 16-24 years, n = 136 643) and tracked licensed driver (n = 129 897) crash outcomes up to 12 months postlicensure. The study was conducted from January 1, 2018, to December 31, 2019, and data analysis was performed from October 7, 2019, to February 11, 2022. Main Outcomes and Measures: Licensing examination performance and population-based, police-reported crash rates in the first 2 months and 12 months postlicensure across age groups, sex, and census tract-level sociodemographic variables were measured. Poisson regression models compared newly licensed driver crash rates, with reference to individuals licensed at 18 years, while controlling for census tract-level sociodemographic factors, time spent in the learner permit period, and licensing examination performance measures. Results: Of 136 643 novice drivers, 69 488 (50.9%) were male and 67 152 (49.1%) were female. Mean (SD) age at enrollment (age at first on-road examination) was 17.7 (2.1) years. License applicants aged 16 and 17 years performed best on license examinations (15 466 [21.6%] and 5112 [30.9%] failing vs 7981 [37.5%] of applicants aged 18 years). Drivers licensed at 18 years had the highest crash rates of all those younger than 25 years. Compared with drivers licensed at 18 years, crash rates were 27% lower in individuals aged 16 years and 14% lower in those aged 17 years during the first 2 months postlicensure when controlling for socioeconomic status, time spent in learner permit status, and license examination performance measures (adjusted relative risk [aRR] at age 16 years: 0.73; 95% CI, 0.67-0.80; age 17 years: aRR, 0.86; 95% CI, 0.77-0.96). At 12 months postlicensure, crash rates were 19% lower for individuals licensed at age 16 years (aRR, 0.81; 95%, CI, 0.77-0.85) and 6% lower at age 17 years (aRR, 0.94; 95% CI, 0.89-0.99) compared with individuals aged 18 years. Conclusions and Relevance: In Ohio, drivers younger than 18 years who are subject to graduated driver licensing and driver education, including BTW training requirements, had lower crash rates in the first year postlicensure compared with those aged 18 years, with controls applied. These findings suggest that it may be fruitful for future work to reconsider the value of mandated driver license policies, including BTW training, and to examine reasons for delayed licensure and barriers to accessing training.

Ketamine for Management of Neonatal and Pediatric Refractory Status Epilepticus.

BACKGROUND AND OBJECTIVES: Few data are available regarding the use of anesthetic infusions for refractory status epilepticus (RSE) in children and neonates, and ketamine use is increasing despite limited data. We aimed to describe the impact of ketamine for RSE in children and neonates. METHODS: Retrospective single-center cohort study of consecutive patients admitted to the intensive care units of a quaternary care children's hospital treated with ketamine infusion for RSE. RESULTS: Sixty-nine patients were treated with a ketamine infusion for RSE. The median age at onset of RSE was 0.7 years (interquartile range 0.15-7.2), and the cohort included 13 (19%) neonates. Three patients (4%) had adverse events requiring intervention during or within 12 hours of ketamine administration, including hypertension in 2 patients and delirium in 1 patient. Ketamine infusion was followed by seizure termination in 32 patients (46%), seizure reduction in 19 patients (28%), and no change in 18 patients (26%). DISCUSSION: Ketamine administration was associated with few adverse events, and seizures often terminated or improved after ketamine administration. Further data are needed comparing first-line and subsequent anesthetic medications for treatment of pediatric and neonatal RSE. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence on the therapeutic utility of ketamine for treatment of RSE in children and neonates.

Multicenter Study of the Impact of COVID-19 Shelter-In-Place on Tertiary Hospital-based Care for Pediatric Neurologic Disease.

Objective: To describe changes in hospital-based care for children with neurologic diagnoses during the initial 6 weeks following regional Coronavirus 2019 Shelter-in-Place orders. Methods: This retrospective cross-sectional study of 7 US and Canadian pediatric tertiary care institutions included emergency and inpatient encounters with a neurologic primary discharge diagnosis code in the initial 6 weeks of Shelter-in-Place (COVID-SiP), compared to the same period during the prior 3 years (Pre-COVID). Patient demographics, encounter length, and neuroimaging and electroencephalography use were extracted from the medical record. Results: 27,900 encounters over 4 years were included. Compared to Pre-COVID, there was a 54% reduction in encounters during Shelter-in-Place. COVID-SiP patients were younger (median 5 years vs 7 years). The incidence of encounters for migraine fell by 72%, and encounters for acute diagnoses of status epilepticus, infantile spasms, and traumatic brain injury dropped by 53%, 55%, and 56%, respectively. There was an increase in hospital length of stay, relative utilization of intensive care, and diagnostic testing (long-term electroencephalography, brain MRI, and head CT (all P<.01)). Conclusion: During the initial 6 weeks of SiP, there was a significant decrease in neurologic hospital-based encounters. Those admitted required a high level of care. Hospital-based neurologic services are needed to care for acutely ill patients. Precise factors causing these shifts are unknown and raise concern for changes in care seeking of patients with serious neurologic conditions. Impacts of potentially delayed diagnosis or treatment require further investigation.

Phenotypic homogeneity in childhood epilepsies evolves in gene-specific patterns across 3251 patient-years of clinical data.

While genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies. After mapping clinical data to the Human Phenotype Ontology, we determined the phenotypic similarity of individuals sharing each genetic etiology within each 3-month age interval from birth up to a maximum age of 25 years. 140 of 600 (23%) of all 27 genes and 3-month age intervals with sufficient data for calculation of phenotypic similarity were significantly higher than expect by chance. 11 of 27 genetic etiologies had significant overall phenotypic similarity trajectories. These do not simply reflect strong statistical associations with single phenotypic features but appear to emerge from complex clinical constellations of features that may not be strongly associated individually. As an attempt to reconstruct the cognitive framework of syndrome recognition in clinical practice, longitudinal phenotypic similarity analysis extends the traditional phenotyping approach by utilizing data from electronic medical records at a scale that is far beyond the capabilities of manual phenotyping. Delineation of how the phenotypic homogeneity of genetic epilepsies varies with age could improve the phenotypic classification of these disorders, the accuracy of prognostic counseling, and by providing historical control data, the design and interpretation of precision clinical trials in rare diseases.

Analyzing 2,589 child neurology telehealth encounters necessitated by the COVID-19 pandemic.

OBJECTIVE: To assess the rapid implementation of child neurology telehealth outpatient care with the onset of the coronavirus disease 2019 (COVID-19) pandemic in March 2020. METHODS: This was a cohort study with retrospective comparison of 14,780 in-person encounters and 2,589 telehealth encounters, including 2,093 audio-video telemedicine and 496 scheduled telephone encounters, between October 1, 2019 and April 24, 2020. We compared in-person and telehealth encounters for patient demographics and diagnoses. For audio-video telemedicine encounters, we analyzed questionnaire responses addressing provider experience, follow-up plans, technical quality, need for in-person assessment, and parent/caregiver satisfaction. We performed manual reviews of encounters flagged as concerning by providers. RESULTS: There were no differences in patient age and major ICD-10 codes before and after transition. Clinicians considered telemedicine satisfactory in 93% (1,200 of 1,286) of encounters and suggested telemedicine as a component for follow-up care in 89% (1,144 of 1,286) of encounters. Technical challenges were reported in 40% (519 of 1,314) of encounters. In-person assessment was considered warranted after 5% (65 of 1,285) of encounters. Patients/caregivers indicated interest in telemedicine for future care in 86% (187 of 217) of encounters. Participation in telemedicine encounters compared to telephone encounters was less frequent among patients in racial or ethnic minority groups. CONCLUSIONS: We effectively converted most of our outpatient care to telehealth encounters, including mostly audio-video telemedicine encounters. Providers rated the vast majority of telemedicine encounters to be satisfactory, and only a small proportion of encounters required short-term in-person follow-up. These findings suggest that telemedicine is feasible and effective for a large proportion of child neurology care. Additional strategies are needed to ensure equitable telemedicine use.