Dataset Information

Early Identification of ASD Through Telemedicine: Potential Value for Underserved Populations.

ABSTRACT: Increasing access to diagnostic services is crucial for identifying ASD in young children. We therefore evaluated a telemedicine assessment procedure. First, we compared telediagnostic accuracy to blinded gold-standard evaluations (n = 20). ASD cases identified via telemedicine were confirmed by in-person evaluation. However, 20% of children diagnosed with ASD in-person were not diagnosed via telemedicine. Second, we evaluated telediagnostic feasibility and acceptability in a rural catchment. Children (n = 45) and caregivers completed the telemedicine procedure and provided feedback. Families indicated high levels of satisfaction. Remote diagnostic clinicians diagnosed 62% of children with ASD, but did not feel capable of ruling-in or out ASD in 13% of cases. Findings support preliminary feasibility, accuracy, and clinical utility of telemedicine-based assessment of ASD for young children.

SUBMITTER: Juarez AP

PROVIDER: S-EPMC6041148 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Early Identification of ASD Through Telemedicine: Potential Value for Underserved Populations.

Juárez A Pablo AP Weitlauf Amy S AS Nicholson Amy A Pasternak Anna A Broderick Neill N Hine Jeffrey J Stainbrook J Alacia JA Warren Zachary Z

Journal of autism and developmental disorders 20180801 8

Increasing access to diagnostic services is crucial for identifying ASD in young children. We therefore evaluated a telemedicine assessment procedure. First, we compared telediagnostic accuracy to blinded gold-standard evaluations (n = 20). ASD cases identified via telemedicine were confirmed by in-person evaluation. However, 20% of children diagnosed with ASD in-person were not diagnosed via telemedicine. Second, we evaluated telediagnostic feasibility and acceptability in a rural catchment. Ch ...[more]

PMID: 29527626

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Project description:BackgroundThe wide range of ability and disability in ASD creates a need for tools that parse the phenotypic heterogeneity into meaningful subtypes. Using eye tracking, our past studies revealed that when presented with social and geometric images, a subset of ASD toddlers preferred viewing geometric images, and these toddlers also had greater symptom severity than ASD toddlers with greater social attention. This study tests whether this "GeoPref test" effect would generalize across different social stimuli.MethodsTwo hundred and twenty-seven toddlers (76 ASD) watched a 90-s video, the Complex Social GeoPref test, of dynamic geometric images paired with social images of children interacting and moving. Proportion of visual fixation time and number of saccades per second to both images were calculated. To allow for cross-paradigm comparisons, a subset of 126 toddlers also participated in the original GeoPref test. Measures of cognitive and social functioning (MSEL, ADOS, VABS) were collected and related to eye tracking data. To examine utility as a diagnostic indicator to detect ASD toddlers, validation statistics (e.g., sensitivity, specificity, ROC, AUC) were calculated for the Complex Social GeoPref test alone and when combined with the original GeoPref test.ResultsASD toddlers spent a significantly greater amount of time viewing geometric images than any other diagnostic group. Fixation patterns from ASD toddlers who participated in both tests revealed a significant correlation, supporting the idea that these tests identify a phenotypically meaningful ASD subgroup. Combined use of both original and Complex Social GeoPref tests identified a subgroup of about 1 in 3 ASD toddlers from the "GeoPref" subtype (sensitivity 35%, specificity 94%, AUC 0.75.) Replicating our previous studies, more time looking at geometric images was associated with significantly greater ADOS symptom severity.ConclusionsRegardless of the complexity of the social images used (low in the original GeoPref test vs high in the new Complex Social GeoPref test), eye tracking of toddlers can accurately identify a specific ASD "GeoPref" subtype with elevated symptom severity. The GeoPref tests are predictive of ASD at the individual subject level and thus potentially useful for various clinical applications (e.g., early identification, prognosis, or development of subtype-specific treatments).

Project description:BackgroundWith demographic shifts toward older populations, the number of people with dementia is steadily increasing. Alzheimer's disease (AD) is the most common cause of dementia, and no curative treatment is available. The current best strategy is to delay disease progression and to practice early intervention to reduce the number of patients that ultimately develop AD. Therefore, promising novel biomarkers for early diagnosis are urgently required.MethodsTo identify blood-based biomarkers for early diagnosis of AD, we performed RNA sequencing (RNA-seq) analysis of 610 blood samples, representing 271 patients with AD, 91 cognitively normal (CN) adults, and 248 subjects with mild cognitive impairment (MCI). We first estimated cell-type proportions among AD, MCI, and CN samples from the bulk RNA-seq data using CIBERSORT and then examined the differentially expressed genes (DEGs) between AD and CN samples. To gain further insight into the biological functions of the DEGs, we performed gene set enrichment analysis (GSEA) and network-based meta-analysis.ResultsIn the cell-type distribution analysis, we found a significant association between the proportion of neutrophils and AD prognosis at a false discovery rate (FDR) < 0.05. Furthermore, a similar trend emerged in the results of routine blood tests from a large number of samples (n = 3,099: AD, 1,605; MCI, 994; CN, 500). In addition, GSEA and network-based meta-analysis based on DEGs between AD and CN samples revealed functional modules and important hub genes associated with the pathogenesis of AD. The risk prediction model constructed by using the proportion of neutrophils and the most important hub genes (EEF2 and RPL7) achieved a high AUC of 0.878 in a validation cohort; when further applied to a prospective cohort, the model achieved a high accuracy of 0.727.ConclusionsOur model was demonstrated to be effective in prospective AD risk prediction. These findings indicate the discovery of potential biomarkers for early diagnosis of AD, and their further improvement may lead to future practical clinical use.

Dataset Information

Early Identification of ASD Through Telemedicine: Potential Value for Underserved Populations.

Publications

Early Identification of ASD Through Telemedicine: Potential Value for Underserved Populations.

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