Evaluation of Quantitative Pupillometry in Acute Postinjury Pediatric Concussion.

BACKGROUND: Although millions of children sustain concussions each year, a rapid and objective test for concussion has remained elusive. The aim of this study was to investigate quantitative pupillometry in pediatric patients in the acute, postinjury setting. METHODS: This was a prospective case-control study of concussed patients presenting to the emergency department within 72 hours of injury. Pupillary measurements were gathered using NeurOptics' PLR 3000; evaluation included a symptom checklist and neurocognitive assessment. Data were analyzed using descriptive statistics and regression models. RESULTS: A total of 126 participants were enrolled. One significant difference in pupillometry between concussed and control participants was found: left minimum pupil diameter in 12- to 18 year-olds (P = 0.02). Models demonstrating odds of a concussion revealed significant associations for time to 75% recovery (T75) of the left pupil in five- to 11-year-olds and average dilation velocity of the left pupil in 12- to 18-year-olds (P = 0.03 and 0.02 respectively). Models predicting symptom improvement showed one significant association: percent change of the right pupil in five-to-11-year-olds (P = 0.02). Models predicting neurocognitive improvement in 12- to 18-year-olds demonstrated significant association in T75 in the left pupil for visual memory, visual motor processing speed, and reaction time (P = 0.002, P = 0.04, P = 0.04). CONCLUSIONS: The limited statistically significant associations found in this study suggest that pupillometry may not be useful in pediatrics in the acute postinjury setting for either the diagnosis of concussion or to stratify risk for prolonged recovery.

Leveraging expertise and optimizing clinical research: Initial success of a pediatric epilepsy surgery collaborative.

OBJECTIVE: Improve data-driven research to inform clinical decision-making with pediatric epilepsy surgery patients by expanding the Pediatric Epilepsy Research Consortium Epilepsy Surgery (PERC-Surgery) Workgroup to include neuropsychological data. This article reports on the process and initial success of this effort and characterizes the cognitive functioning of the largest multi-site pediatric epilepsy surgery cohort in the United States. METHODS: Pediatric neuropsychologists from 18 institutions completed surveys regarding neuropsychological practice and the impact of involvement in the collaborative. Neuropsychological data were entered through an online database. Descriptive analyses examined the survey responses and cognitive functioning of the cohort. Statistical analyses examined which patients were evaluated and if composite scores differed by domain, demographics, measures used, or epilepsy characteristics. RESULTS: Positive impact of participation was evident by attendance, survey responses, and the neuropsychological data entry of 534 presurgical epilepsy patients. This cohort, ages 6 months to 21 years, were majority White and non-Hispanic, and more likely to have private insurance. Mean intelligence quotient (IQ) scores were below to low average, with weaknesses in working memory and processing speed. Full-scale IQ (FSIQ) was lowest for patients with younger age at seizure onset, daily seizures, and magnetic resonance imaging (MRI) abnormalities. SIGNIFICANCE: We established a collaborative network and fundamental infrastructure to address questions outlined by the Epilepsy Research Benchmarks. There is a wide range in the age and IQ of patients considered for pediatric epilepsy surgery, yet it appears that social determinants of health impact access to care. Consistent with other national cohorts, this US cohort has a downward shift in IQ associated with seizure severity.

Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder.

Autism spectrum disorder (ASD) can be reliably diagnosed at 18 months, yet significant diagnostic delays persist in the United States. This double-blinded, multi-site, prospective, active comparator cohort study tested the accuracy of an artificial intelligence-based Software as a Medical Device designed to aid primary care healthcare providers (HCPs) in diagnosing ASD. The Device combines behavioral features from three distinct inputs (a caregiver questionnaire, analysis of two short home videos, and an HCP questionnaire) in a gradient boosted decision tree machine learning algorithm to produce either an ASD positive, ASD negative, or indeterminate output. This study compared Device outputs to diagnostic agreement by two or more independent specialists in a cohort of 18-72-month-olds with developmental delay concerns (425 study completers, 36% female, 29% ASD prevalence). Device output PPV for all study completers was 80.8% (95% confidence intervals (CI), 70.3%-88.8%) and NPV was 98.3% (90.6%-100%). For the 31.8% of participants who received a determinate output (ASD positive or negative) Device sensitivity was 98.4% (91.6%-100%) and specificity was 78.9% (67.6%-87.7%). The Device's indeterminate output acts as a risk control measure when inputs are insufficiently granular to make a determinate recommendation with confidence. If this risk control measure were removed, the sensitivity for all study completers would fall to 51.6% (63/122) (95% CI 42.4%, 60.8%), and specificity would fall to 18.5% (56/303) (95% CI 14.3%, 23.3%). Among participants for whom the Device abstained from providing a result, specialists identified that 91% had one or more complex neurodevelopmental disorders. No significant differences in Device performance were found across participants' sex, race/ethnicity, income, or education level. For nearly a third of this primary care sample, the Device enabled timely diagnostic evaluation with a high degree of accuracy. The Device shows promise to significantly increase the number of children able to be diagnosed with ASD in a primary care setting, potentially facilitating earlier intervention and more efficient use of specialist resources.

The impact of bilingualism on working memory in pediatric epilepsy.

Impairments in executive skills broadly span across multiple childhood epilepsy syndromes and can adversely affect quality of life. Bilingualism has been previously shown to correlate with enhanced executive functioning in healthy individuals. This study sought to determine whether the bilingual advantage in executive functioning exists in the context of pediatric epilepsy. We retrospectively analyzed neuropsychological data in 52 children with epilepsy and compared executive function scores in monolingual versus bilingual children with epilepsy while controlling for socioeconomic status and ethnicity. Bilingual children performed significantly better on the Working Memory Index than did monolingual children. There were no significant differences on the remaining executive function variables. The bilingual advantage appears to persist for working memory in children with epilepsy. These findings suggest that bilingualism is potentially a protective variable in the face of epilepsy-related working memory dysfunction.