Enhancing early autism prediction based on electronic records using clinical narratives.

Recent work has shown that predictive models can be applied to structured electronic health record (EHR) data to stratify autism likelihood from an early age (<1 year). Integrating clinical narratives (or notes) with structured data has been shown to improve prediction performance in other clinical applications, but the added predictive value of this information in early autism prediction has not yet been explored. In this study, we aimed to enhance the performance of early autism prediction by using both structured EHR data and clinical narratives. We built models based on structured data and clinical narratives separately, and then an ensemble model that integrated both sources of data. We assessed the predictive value of these models from Duke University Health System over a 14-year span to evaluate ensemble models predicting later autism diagnosis (by age 4 years) from data collected from ages 30 to 360 days. Our sample included 11,750 children above by age 3 years (385 meeting autism diagnostic criteria). The ensemble model for autism prediction showed superior performance and at age 30 days achieved 46.8% sensitivity (95% confidence interval, CI: 22.0%, 52.9%), 28.0% positive predictive value (PPV) at high (90%) specificity (CI: 2.0%, 33.1%), and AUC4 (with at least 4-year follow-up for controls) reaching 0.769 (CI: 0.715, 0.811). Prediction by 360 days achieved 44.5% sensitivity (CI: 23.6%, 62.9%), and 13.7% PPV at high (90%) specificity (CI: 9.6%, 18.9%), and AUC4 reaching 0.797 (CI: 0.746, 0.840). Results show that incorporating clinical narratives in early autism prediction achieved promising accuracy by age 30 days, outperforming models based on structured data only. Furthermore, findings suggest that additional features learned from clinician narratives might be hypothesis generating for understanding early development in autism.

A scalable computational approach to assessing response to name in toddlers with autism.

BACKGROUND: This study is part of a larger research program focused on developing objective, scalable tools for digital behavioral phenotyping. We evaluated whether a digital app delivered on a smartphone or tablet using computer vision analysis (CVA) can elicit and accurately measure one of the most common early autism symptoms, namely failure to respond to a name call. METHODS: During a pediatric primary care well-child visit, 910 toddlers, 17-37 months old, were administered an app on an iPhone or iPad consisting of brief movies during which the child's name was called three times by an examiner standing behind them. Thirty-seven toddlers were subsequently diagnosed with autism spectrum disorder (ASD). Name calls and children's behavior were recorded by the camera embedded in the device, and children's head turns were coded by both CVA and a human. RESULTS: CVA coding of response to name was found to be comparable to human coding. Based on CVA, children with ASD responded to their name significantly less frequently than children without ASD. CVA also revealed that children with ASD who did orient to their name exhibited a longer latency before turning their head. Combining information about both the frequency and the delay in response to name improved the ability to distinguish toddlers with and without ASD. CONCLUSIONS: A digital app delivered on an iPhone or iPad in real-world settings using computer vision analysis to quantify behavior can reliably detect a key early autism symptom-failure to respond to name. Moreover, the higher resolution offered by CVA identified a delay in head turn in toddlers with ASD who did respond to their name. Digital phenotyping is a promising methodology for early assessment of ASD symptoms.

Brief Report: Relationships Between Caregiver-Reported Externalizing and Internalizing Behaviors and the Modified Checklist for Autism in Toddlers.

PURPOSE: Early detection and intervention are associated with improved outcomes for autistic children. Thus, it is important to understand factors influencing early screening tools designed to detect autism. This study examined the relationship between caregiver-reported emotional and behavioral symptoms and children's scores on a commonly used autism screening questionnaire, the Modified Checklist for Autism in Toddlers-Revised with Follow-Up (M-CHAT-R/F). METHODS: Toddlers were recruited from four primary care clinics between 2018 and 2021. Their caregivers completed the M-CHAT-R/F as well as the Child Behavior Checklist (CBCL), a well-validated, normed measure of emotional and behavioral functioning. Correlational and group analyses were evaluated to examine relationships between CBCL scales and M-CHAT-R/F scores. RESULTS: 1765 toddlers were recruited for the study. CBCL scores for the internalizing, externalizing, autism, ADHD, and anxiety scales were all modestly positively correlated with M-CHAT-R/F scores. Compared to toddlers with elevated autism scale scores only, toddlers with elevations in both autism and ADHD/externalizing scales had higher M-CHAT-R/F scores. In contrast, no significant difference in scores were found between toddlers with elevated autism scale scores only compared to those with elevated scores on both autism and internalizing scales. CONCLUSION: Findings suggest that, for children with elevated autism behaviors, the presence of externalizing symptoms, including ADHD-related concerns, is associated with elevated scores on the M-CHAT-R/F. In contrast, internalizing symptoms did not show an association with elevated M-CHAT-R/F scores among toddlers with elevated autism-related behaviors. Interpretation of the M-CHAT-R/F should include consideration of co-occurring psychiatric conditions, especially externalizing conditions such as ADHD.

Predictive Value of Early Autism Detection Models Based on Electronic Health Record Data Collected Before Age 1 Year.

Importance: Autism detection early in childhood is critical to ensure that autistic children and their families have access to early behavioral support. Early correlates of autism documented in electronic health records (EHRs) during routine care could allow passive, predictive model-based monitoring to improve the accuracy of early detection. Objective: To quantify the predictive value of early autism detection models based on EHR data collected before age 1 year. Design, Setting, and Participants: This retrospective diagnostic study used EHR data from children seen within the Duke University Health System before age 30 days between January 2006 and December 2020. These data were used to train and evaluate L2-regularized Cox proportional hazards models predicting later autism diagnosis based on data collected from birth up to the time of prediction (ages 30-360 days). Statistical analyses were performed between August 1, 2020, and April 1, 2022. Main Outcomes and Measures: Prediction performance was quantified in terms of sensitivity, specificity, and positive predictive value (PPV) at clinically relevant model operating thresholds. Results: Data from 45 080 children, including 924 (1.5%) meeting autism criteria, were included in this study. Model-based autism detection at age 30 days achieved 45.5% sensitivity and 23.0% PPV at 90.0% specificity. Detection by age 360 days achieved 59.8% sensitivity and 17.6% PPV at 81.5% specificity and 38.8% sensitivity and 31.0% PPV at 94.3% specificity. Conclusions and Relevance: In this diagnostic study of an autism screening test, EHR-based autism detection achieved clinically meaningful accuracy by age 30 days, improving by age 1 year. This automated approach could be integrated with caregiver surveys to improve the accuracy of early autism screening.

Relationship between quantitative digital behavioral features and clinical profiles in young autistic children.

Early behavioral markers for autism include differences in social attention and orienting in response to one's name when called, and differences in body movements and motor abilities. More efficient, scalable, objective, and reliable measures of these behaviors could improve early screening for autism. This study evaluated whether objective and quantitative measures of autism-related behaviors elicited from an app (SenseToKnow) administered on a smartphone or tablet and measured via computer vision analysis (CVA) are correlated with standardized caregiver-report and clinician administered measures of autism-related behaviors and cognitive, language, and motor abilities. This is an essential step in establishing the concurrent validity of a digital phenotyping approach. In a sample of 485 toddlers, 43 of whom were diagnosed with autism, we found that CVA-based gaze variables related to social attention were associated with the level of autism-related behaviors. Two language-related behaviors measured via the app, attention to people during a conversation and responding to one's name being called, were associated with children's language skills. Finally, performance during a bubble popping game was associated with fine motor skills. These findings provide initial support for the concurrent validity of the SenseToKnow app and its potential utility in identifying clinical profiles associated with autism. Future research is needed to determine whether the app can be used as an autism screening tool, can reliably stratify autism-related behaviors, and measure changes in autism-related behaviors over time.

Blink rate and facial orientation reveal distinctive patterns of attentional engagement in autistic toddlers: a digital phenotyping approach.

Differences in social attention are well-documented in autistic individuals, representing one of the earliest signs of autism. Spontaneous blink rate has been used to index attentional engagement, with lower blink rates reflecting increased engagement. We evaluated novel methods using computer vision analysis (CVA) for automatically quantifying patterns of attentional engagement in young autistic children, based on facial orientation and blink rate, which were captured via mobile devices. Participants were 474 children (17-36 months old), 43 of whom were diagnosed with autism. Movies containing social or nonsocial content were presented via an iPad app, and simultaneously, the device's camera recorded the children's behavior while they watched the movies. CVA was used to extract the duration of time the child oriented towards the screen and their blink rate as indices of attentional engagement. Overall, autistic children spent less time facing the screen and had a higher mean blink rate compared to neurotypical children. Neurotypical children faced the screen more often and blinked at a lower rate during the social movies compared to the nonsocial movies. In contrast, autistic children faced the screen less often during social movies than during nonsocial movies and showed no differential blink rate to social versus nonsocial movies.

Early detection of autism using digital behavioral phenotyping.

Early detection of autism, a neurodevelopmental condition associated with challenges in social communication, ensures timely access to intervention. Autism screening questionnaires have been shown to have lower accuracy when used in real-world settings, such as primary care, as compared to research studies, particularly for children of color and girls. Here we report findings from a multiclinic, prospective study assessing the accuracy of an autism screening digital application (app) administered during a pediatric well-child visit to 475 (17-36 months old) children (269 boys and 206 girls), of which 49 were diagnosed with autism and 98 were diagnosed with developmental delay without autism. The app displayed stimuli that elicited behavioral signs of autism, quantified using computer vision and machine learning. An algorithm combining multiple digital phenotypes showed high diagnostic accuracy with the area under the receiver operating characteristic curve = 0.90, sensitivity = 87.8%, specificity = 80.8%, negative predictive value = 97.8% and positive predictive value = 40.6%. The algorithm had similar sensitivity performance across subgroups as defined by sex, race and ethnicity. These results demonstrate the potential for digital phenotyping to provide an objective, scalable approach to autism screening in real-world settings. Moreover, combining results from digital phenotyping and caregiver questionnaires may increase autism screening accuracy and help reduce disparities in access to diagnosis and intervention.

Infants' diminished response to DTaP vaccine is associated with exposure to organophosphate esters.

Organophosphate esters (OPEs) are commonly applied as flame retardants and plasticizers. Toxicological studies suggest exposure effects on immune endpoints, raising concerns as infants' OPE exposures are elevated compared to older children and adults due to hand-to-mouth behavior and breastfeeding. Here, we sought to evaluate the immune responsiveness of infants to a neoantigen (e.g., a newly encountered antigen) in the presence of OPE exposures. As a proxy for immune responsiveness, children were given three doses of the Diphtheria, Tetanus, and Pertussis (DTaP) vaccine as recommended, and diphtheria and tetanus antibodies were evaluated in serum samples collected when children were 12 months old (n = 84). Titers were compared, based on maximum sample overlap, to measurements of OPE metabolites in spot urine samples collected before vaccination (age 2 months, n = 73) and at the time of antibody assessment (12 months of age, n = 46). Metabolites of two chlorinated OPEs were significantly associated with diminished antibodies for diphtheria and tetanus. A metabolite of tris (1,3-dichloroisopropyl)phosphate (TDCIPP) measured at 2 months was associated with decreased diphtheria antibodies (-0.07 IU/mL per log10 increase in metabolite). One metabolite of tris(2-chloroisopropyl)phosphate (TCIPP) measured at 12 months was associated with decreased tetanus antibodies (-0.57 IU/mL per log10 increase in metabolite). These results provide some preliminary insights for OPE exposure impacts on vaccine responses in early life and may have important implications for immune health through childhood and adulthood.

Computational Methods to Measure Patterns of Gaze in Toddlers With Autism Spectrum Disorder.

Importance: Atypical eye gaze is an early-emerging symptom of autism spectrum disorder (ASD) and holds promise for autism screening. Current eye-tracking methods are expensive and require special equipment and calibration. There is a need for scalable, feasible methods for measuring eye gaze. Objective: Using computational methods based on computer vision analysis, we evaluated whether an app deployed on an iPhone or iPad that displayed strategically designed brief movies could elicit and quantify differences in eye-gaze patterns of toddlers with ASD vs typical development. Design, Setting, and Participants: A prospective study in pediatric primary care clinics was conducted from December 2018 to March 2020, comparing toddlers with and without ASD. Caregivers of 1564 toddlers were invited to participate during a well-child visit. A total of 993 toddlers (63%) completed study measures. Enrollment criteria were aged 16 to 38 months, healthy, English- or Spanish-speaking caregiver, and toddler able to sit and view the app. Participants were screened with the Modified Checklist for Autism in Toddlers-Revised With Follow-up during routine care. Children were referred by their pediatrician for diagnostic evaluation based on results of the checklist or if the caregiver or pediatrician was concerned. Forty toddlers subsequently were diagnosed with ASD. Exposures: A mobile app displayed on a smartphone or tablet. Main Outcomes and Measures: Computer vision analysis quantified eye-gaze patterns elicited by the app, which were compared between toddlers with ASD vs typical development. Results: Mean age of the sample was 21.1 months (range, 17.1-36.9 months), and 50.6% were boys, 59.8% White individuals, 16.5% Black individuals, 23.7% other race, and 16.9% Hispanic/Latino individuals. Distinctive eye-gaze patterns were detected in toddlers with ASD, characterized by reduced gaze to social stimuli and to salient social moments during the movies, and previously unknown deficits in coordination of gaze with speech sounds. The area under the receiver operating characteristic curve discriminating ASD vs non-ASD using multiple gaze features was 0.90 (95% CI, 0.82-0.97). Conclusions and Relevance: The app reliably measured both known and new gaze biomarkers that distinguished toddlers with ASD vs typical development. These novel results may have potential for developing scalable autism screening tools, exportable to natural settings, and enabling data sets amenable to machine learning.

Young infants' exposure to organophosphate esters: Breast milk as a potential source of exposure.

Organophosphate esters (OPEs) are applied as both flame retardants and plasticizers to a variety of consumer items such as home furnishings, construction materials, and children's products. While some assessments have characterized exposure among toddlers and young children, little is known about the OPE exposure among infants, who are a vulnerable population due to their rapid development. Here, we collected spot urine samples from 6-week-old (n = 100) and 12-month-old infants (n = 63), with about half of the infants evaluated at both ages (n = 52), to characterize OPE exposure and determine what factors contributed to higher exposures. Five of six OPE metabolites analyzed were detected frequently (>70%). Diphenyl phosphate was detected in every urine sample, while bis(2-chloro-isopropyl) phosphate (BCIPP) was the most abundant metabolite measured overall. Concentrations of bis(1-chloro-2-propyl) 1-hydroxy-2-propyl phosphate (BCIPHIPP) and BCIPP [i.e., metabolites of tris(2-chloro-isopropyl) phosphate (TCIPP)] were significantly greater among 6-week-old infants compared to 12-month-olds, while levels of other OPE metabolites were not statistically different in the first year of life. OPE metabolites were generally correlated with one another in samples collected at each age (rs = 0.25-0.75; p < 0.05), and except BCIPHIPP, concentrations of the same metabolite were correlated over time (rs = 0.41-0.53; p < 0.05). Breastfeeding at 6 weeks of age and owning a larger number of children's products were associated with increased concentrations of urinary BDCIPP. Infants who were currently receiving breast milk had higher levels of TCIPP metabolites; urinary BCIPP concentrations were 6.2 times higher in infants receiving breast milk at 6 weeks of age, and BCIPHIPP levels were 2.2 times higher for 12-month-old infants receiving breast milk (10ß = 7.2; 95% CI: 1.6-32.1 and 10ß = 3.2; 95% CI: 1.2-8.1, respectively). Differences in the predominant TCIPP metabolite associated with breastfeeding may suggest differences in metabolism with age. Cumulatively, our results suggest levels of OPE exposure may be higher for infants than other age groups, including toddlers and older children.

A Community-based Assessment of Skin Care, Allergies, and Eczema (CASCADE): an atopic dermatitis primary prevention study using emollients-protocol for a randomized controlled trial.

BACKGROUND: Atopic dermatitis (AD) is a common, chronic skin disorder often beginning in infancy. Skin barrier dysfunction early in life serves as a central event in the pathogenesis of AD. In infants at high risk of developing AD, preventative application of lipid-rich emollients may reduce the risk of developing AD. This study aims to measure the effectiveness of this intervention in a population not selected for risk via a pragmatic, randomized, physician-blinded trial in the primary care setting. METHODS: Infant-parent dyads are recruited from a primary care practice participating through one of four practice-based research networks in Oregon, Colorado, Wisconsin, and North Carolina. Eligible dyads are randomized to the intervention (daily use of lipid-rich emollient) or the control (no emollient) group (n = 625 infants in each) and are followed for 24 months. The primary outcome is the cumulative incidence of physician-diagnosed AD and secondary outcomes include caregiver-reported measures of AD and development of other atopic diseases. Data collection occurs via chart review and surveys, with no study visits required. Data will be analyzed utilizing intention-to-treat principles. DISCUSSION: AD is a common skin condition in infants that affects quality of life and is associated with the development of other atopic diseases. If a safe intervention, such as application of lipid-rich emollients, in the general population effectively decreases AD prevalence, this could alter the guidance given by providers regarding routine skin care of infants. Because of the pragmatic design, we anticipate that this trial will yield generalizable results. TRIAL REGISTRATION: ClinicalTrials.gov: NCT03409367. Registered on 11 February 2018.

Persistent antibody depletion after rituximab in three children with autoimmune cytopenias.

We report 3 children who developed persistent antibody depletion and abnormal response to bacteriophage after rituximab treatment for autoimmune cytopenias. Whether these patients have developed immunodeficiency secondary to an underlying disease process, to rituximab, or both, is not understood. Rituximab is an efficacious drug for a number of pediatric conditions. However, some patients who receive the drug have prolonged suppression or absence of B-cell function. Families should be counseled of this possibility prior to therapy. Patients should have baseline measurement of quantitative immunoglobulins and specific antibody levels and should be monitored for long term changes in immune function after rituximab.

Open-Label Pilot Study to Compare the Safety and Immunogenicity of Pentavalent Rotavirus Vaccine (RV5) Administered on an Early Alternative Dosing Schedule with Those of RV5 Administered on the Recommended Standard Schedule.

This study compares the safety and immunogenicity of pentavalent rotavirus vaccine (RV5) administered on an alternative schedule (initiated at 2-5 weeks of age) with those of RV5 administered on the recommended standard schedule. Our findings support the future conduct of larger clinical trials to confirm the safety and efficacy of rotavirus vaccination in the neonatal period.