Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder.

OBJECTIVE: Cohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD. METHODS: We extracted ICD-9 codes and concepts derived from the clinical notes. A gold standard patient set was labeled by clinicians at Boston Children's Hospital (BCH) (N = 150) and Cincinnati Children's Hospital and Medical Center (CCHMC) (N = 152). Two algorithms were created: (1) rule-based implementing the ASD criteria from Diagnostic and Statistical Manual of Mental Diseases 4th edition, (2) predictive classifier. The positive predictive values (PPV) achieved by these algorithms were compared to an ICD-9 code baseline. We clustered the patients based on grouped ICD-9 codes and evaluated subgroups. RESULTS: The rule-based algorithm produced the best PPV: (a) BCH: 0.885 vs. 0.273 (baseline); (b) CCHMC: 0.840 vs. 0.645 (baseline); (c) combined: 0.864 vs. 0.460 (baseline). A validation at Children's Hospital of Philadelphia yielded 0.848 (PPV). Clustering analyses of comorbidities on the three-site large cohort (N = 20,658 ASD patients) identified psychiatric, developmental, and seizure disorder clusters. CONCLUSIONS: In a large cross-institutional cohort, co-occurrence patterns of comorbidities in ASDs provide further hypothetical evidence for distinct courses in ASD. The proposed automated algorithms for cohort selection open avenues for other large-scale EHR studies and individualized treatment of ASD.

Child and family characteristics associated with age of diagnosis of an autism spectrum disorder in a tertiary care setting.

OBJECTIVE: To identify child and family characteristics associated with age of diagnosis of autism spectrum disorder (ASD) in a tertiary care setting using objective, standardized assessments ensuring diagnostic validity and timing. METHODS: The authors conducted a chart review of children who received their initial ASD diagnosis from 2007 to 2011. Child variables included gender, birth order, cognitive functioning, and for children ≤36 months, language and adaptive assessments. Family variables included insurance, maternal age, maternal education, sibling or family member with ASD, and number of children in the house. Primary outcome was age of ASD diagnosis. The authors ran multiple regression models evaluating the impact of child and family variables on the total sample and on the subsample of children ≤36 months. RESULTS: Median age of diagnosis was 2.9 years (range, 15 mo-13.8 yr; n = 591). In the total sample, significant predictors of earlier age of diagnosis were later birth order, higher maternal education, fewer children in the house, and a sibling with ASD. In a separate analysis of children ≤36 months of age (n = 315) with additional data for language and adaptive assessments, significant predictors of younger age of diagnosis were higher cognitive and adaptive functioning, lower receptive and expressive language, and having a sibling with ASD. CONCLUSIONS: This study suggests that both family and child characteristics play an important role in the early identification of ASD and that predictive variables may vary based on a child's age. Future research should help to elucidate this finding so that screening measures and policies aimed at early identification can target the most predictive factors.

Prevalence, nature, and correlates of sleep problems among children with fragile X syndrome based on a large scale parent survey.

STUDY OBJECTIVES: This study reports on current child sleep difficulties reported by parents of children with Fragile X syndrome (FXS). We address prevalence and type of sleep problems (e.g., difficulty falling asleep, frequent awakenings); type and effectiveness of medical and behavioral treatments (e.g., medication, surgery, environmental changes); and explore specific child and family characteristics (e.g., child age, child gender, co-occurring conditions) as possible predictors of child sleep difficulties. DESIGN/PARTICIPANTS: This study is part of a larger survey addressing needs of families with children with FXS. This article focuses on the families who responded to the survey sleep questions, had one or more children with the full mutation FXS, and who reside in the United States. The mean age for male and female children in this group was 15 years and 16 years respectively (N=1295). RESULTS: Parents reported that 32% of the children with FXS currently experience sleep difficulties; 84% of those children are reported to have > or =2 current sleep problems. Problems falling asleep and frequent night awakenings were the most frequently reported difficulties; 47% of males and 40% of females received > or =1 medication to help with sleep. Children with more problematic health or behavioral characteristics had a higher likelihood of having current sleep problems. CONCLUSIONS: Our survey provides the most representative sample to date of sleep problems in children with FXS or any other neurodevelopmental disability. This large scale survey establishes a foundation for the prevalence of sleep disorders in children with FXS.

Functional characteristics of children diagnosed with Childhood Apraxia of Speech.

PURPOSE: The purpose of this study was to describe the complex array of functional problems in children diagnosed by their treating speech/language pathologist with Childhood Apraxia of Speech (CAS), a poorly understood, severe speech sound disorder. METHOD: We asked parents (n = 201) attending the first national parent conference on CAS to voluntarily complete a survey, which evaluated 43 functional domains from the International Classification of Functioning, Disability, and Health-Children and Youth version (ICF-CY). The survey also included questions about co-existing medical, developmental, and mental health conditions and about service utilization. Data were analyzed using descriptive statistics and exploratory factor analysis. RESULTS: The most prevalent functional problems in addition to communication were attention (focus), vestibular function, temperament, fine hand use, maintaining attention, and learning to write. Four orthogonal factors accounted for 23% of the variance in functional problems: Cognitive and Learning Problems, Social Communication Difficulties, Behavioral Dysregulation, and Other Oral Motor Problems. Over half the sample had health, mental health, and developmental conditions. Almost all of the children used early intervention and speech/language therapy services. CONCLUSIONS: The ICF-CY provided a systematic approach for describing and categorizing functional problems in children with CAS. The identified factors should guide the multidisciplinary team in conducting comprehensive evaluations, rehabilitation, and long-term follow-up of children with CAS.