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Autism & Developmental Disorders Inpatient Research Collaborative (ADDIRC), Spring Harbor Hospital, Westbrook, ME

Principal Investigator(s): Matthew Siegel, M.D., Maine Medical Center

The Autism Inpatient Collection (Phase II): Developing a Unique, Severely Affected Cohort To Accelerate Discovery and Targeted Treatment of Autism Subtypes (Co-funded with the Simons Foundation)

The NLMFF, in partnership with the Simons Foundation, is providing support for the Phase II application of the project entitled, “The Autism Inpatient Collection (AIC): Developing a Unique, Severely Affected Cohort To Accelerate Discovery and Targeted Treatment of Autism Subtypes.” Support from the Simons Foundation and the NLMFF in Phase I enabled Dr. Siegel to successfully work with his colleagues to launch the six-site collaborative and create the infrastructure for a uniquely valuable database of phenotypic and biological data to study patients at the severe end of the autism disorders spectrum. In Phase I, the collaborative collected critical data from over 300 patients and families across the six sites. This second phase of the project will expand the cohort studied to a total of 1,600 probands and their families, providing an opportunity to document and investigate the full phenotypic range of Autism Spectrum Disorders, including those with the most severe challenging behaviors, communication and autism, thereby providing critical information to assist patients and their families.

Autism & Developmental Disorders Inpatient Research Collaborative (ADDIRC), Spring Harbor Hospital, Westbrook, ME
2013 - 2015

Principal Investigator(s): Matthew Siegel, M.D., Maine Medical Center

Phenotyping of the Severely Affected Autism Population: Developing a Research Platform for the Study and Treatment of the Severely Affected Autism Population (Co-funded with the Simons Foundation)

Although rigorously collected phenotypic and biological data have contributed greatly to ASD research, adequate data from severely affected individuals are lacking. Barriers to the study of the severely affected ASD population include challenges in their recruitment and participation in outpatient research studies, the limited contact of most investigators with this population, and the relative lack of validated measures for characterizing these individuals.

The Autism and Developmental Disorders Inpatient Research Collaborative (ADDIRC) is dedicated to becoming a unique and singular research platform for advancing the clinical assessment and treatment of patients with severe ASD. Each year, over 1000 children and adolescents with ASD and serious behavioral disturbance are admitted to the nine specialized psychiatric hospital units that comprise the ADDIRC. These hospital units are staffed by multi-disciplinary teams of highly skilled clinicians and investigators with unique expertise in assessing and caring for these patients. The ADDIRC patient population is heavily weighted toward individuals with severe ASD.

The ADDIRC is engaged in a two-year project to establish multi-site data collection procedures and to test these procedures in a prospective study of its inpatient ASD population. The project team, including clinical and research scientists from each ADDIRC site and scientific and administrative advisory groups, has expertise in the assessment of severe ASD, the collection and analysis of phenotypic and genetic data and the administration of collaborative research. The immediate goal of the network is to develop a comprehensive registry for the collection of clinical and eventually biological data on severely affected children and adolescents with ASD in order to systematically develop effective interventions for this population. The network will collect detailed data on the severely affected population, including the dimensions of intelligence, communication ability, emotional regulation and self-injurious behavior, and examine the relationships among these critical factors.

Autism & Developmental Disorders Inpatient Research Collaborative (ADDIRC)

Center for Biomedical Informatics at Harvard Medical School, Boston, MA

Principal Investigator: Dennis P. Wall, Ph.D.

Mobilized Technology for Rapid Screening and Clinical Prioritization of ASD (Co-funded with the Simons Foundation)

Autism rates continue to rise with more and more children being referred for autism screening every day. The behavioral tests currently administered for diagnosis are several hours long and the diagnosis process as a whole is cumbersome for families.  In addition, clinical professionals capable of administering the exams tend to be too few and well above capacity. The average time between initial evaluation and diagnosis for a child living in a large metropolitan area is greater than one year and approaches five years for families living in more remote areas. The delay in diagnosis is not only frustrating for families, but prevents many children from receiving medical attention until they have past developmental time periods when behavioral therapy would have had appreciable impact. To combat this significant public health challenge, Dr. Wall has developed algorithms that rapidly analyze a short set of parent/caregiver-directed questions and a 2-5 minute video of the subject to yield an accurate classification of on or off the autism spectrum. The algorithms are derived from two of the most reliable and widely used behavioral instruments, the Autism Diagnostic Interview-Revised (ADI-R) and the Autism Diagnostic Observation Schedule (ADOS) and can be administered in minutes as compared to the hours typically required for delivery of the current methods. The present study is designed to test the potential of these highly abbreviated approaches directly in a high-volume clinical facility at Children’s Hospital Boston. Through the use of a mobilized web and iPad-friendly framework, Dr. Wall’s group will enroll 200 or more children, a majority of which will meet standard clinical criteria for an autism diagnosis, and a smaller but sizable percentage of which will be children with other developmental delays.  In so doing, it will be possible to measure both the sensitivity and specificity of these algorithms and to evaluate the efficacy of the mobilized approach for assisting the clinical diagnostic process overall.  The hope is that the work will bring us closer to a comprehensive technology that can provide rapid assessments and enable patient prioritization at the nearest and most appropriate clinical care facilities, and a technology that increases the reach to a larger percentage of the risk population to ensure timely delivery of therapies.

The Wall Lab, The Center for Biomedical Informatics at Harvard Medical School

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