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.

Translating Research to Practice for Children With Autism Spectrum Disorder: Part 2: Behavior Management in Home and Health Care Settings.

INTRODUCTION: Managing home and health care for children with autism spectrum disorder can be challenging because of the range of symptoms and behaviors exhibited. METHOD: This article presents an overview of the emerging science related to the methods to foster family self-management of common concerns regarding activities of daily living and behaviors, as well as for the health care provider in primary and acute health care settings. RESULTS: Recommendations are provided to enhance the overall delivery of services, including understanding and managing a child's challenging behaviors, and supporting family management of common activities of daily living and behaviors. DISCUSSION: Health care providers' knowledge of evidence-based recommendations for providing care, supporting family self-management of common concerns, and referral heighten the likelihood of better outcomes for children with autism spectrum disorder.

Translating Research to Practice for Children With Autism Spectrum Disorder: Part I: Definition, Associated Behaviors, Prevalence, Diagnostic Process, and Interventions.

INTRODUCTION: The number of children with autism spectrum disorder (ASD) is rising, along with the potential for challenging behaviors during health care encounters. METHOD: We present an overview of the emerging science related to ASD diagnosis and interventions for children with ASD. RESULTS: Emerging science on ASD reveals common associated challenging behaviors, increasing prevalence, emphasis on early diagnosis at 18 to 24 months of age, changes in the diagnostic process with criteria from the Diagnostic and Statistical Manual of Mental Disorders, 5th edition, and interventions with medication, education, and behavior management. DISCUSSION: Family and health care provider preparation strategies facilitate care of children with ASD and their families. Early diagnosis at 18 to 24 months of age and evidence-based interventions contribute to best outcomes for children and families. Health care providers must be aware of the state of the science for diagnosis and best practices to provide family-centered care for this growing population.

Autism spectrum disorders.

Autism spectrum disorders are being diagnosed with increasing frequency. The likelihood that a primary care provider will see a patient with autism spectrum disorder in their clinic is high. In this article, current diagnostic criteria and expected changes in DSM criteria, as well as prevalence rates and epidemiologic studies are reviewed. Recommendations for screening, including early warning signs, and best practices for diagnosis are discussed. Comprehensive evidence based intervention for ASD as well as the findings of the National Standards Project are reviewed. Medication management is also described, as are the roles of other treating professionals.

Electroencephalogram abnormalities in children with autism spectrum disorders.

Children with an autism spectrum disorder (ASD) often are evaluated with electroencephalogram (EEG) studies to assess their risk for seizures or other underlying abnormalities. Their risk is estimated at 7% - 42%. EEG studies were conducted on a subgroup of children while following established practice parameters for evaluating children for ASD. Abnormal EEG results were obtained in 85 (27%) of the 316 children evaluatedfor ASD. Within the subset of abnormal results, 64 children had autism, 10 had an ASD or milder presentation, 6 had another developmental disorder, 3 had Rett syndrome, had Down syndrome, and 1 had Wolf-Hirshhorn syndrome. The abnormal EEG findings included epileptiform abnormalities in 55 patients (65%), and slowing in only 13 patients (15%). The focality of the epileptiformfindings included 26 (30%) in the temporal areas, 24 (28%) in the central area, 20 (23%) in the frontal area, and 7 (8%) in the occipital area. These findings confirm the importance of ongoing medicalfollow-up for children with ASDs, especially for those with abnormal EEG results.