Improving precision use of antipsychotic medication in people with autism

提高自闭症患者抗精神病药物的精确使用

• 批准号: 10415084
• 负责人: Lea K Davis
• 金额: $ 24.02万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-06 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10415084
• 关键词: Address; Adult; Affect; Aggressive behavior; Ambulatory Care Facilities; Antipsychotic Agents; Anxiety; Area; Behavior; Behavior Therapy; Behavioral; Biological Markers; Cardiovascular system; Caregiver Burden; Caregivers; Caring; Child; Clinical; Clinical Decision Support Systems; Communities; Computerized Medical Record; Data; Data Science Core; Databases; Development; Diabetes Mellitus; Drug usage; Electronic Health Record; Elements; Emergency department visit; Evaluation; FDA approved; Family; Genetic; Genotype; Health; High Prevalence; Individual; Intellectual and Developmental Disabilities Research Centers; Intervention; Knowledge; Link; Measures; Medical Genetics; Mental Health; Metabolic syndrome; Methods; Modeling; Neurodevelopmental Disorder; Obesity; Obsessive compulsive behavior; Outcome; Outcome Measure; Parents; Participant; Performance; Persons; Pharmaceutical Preparations; Pharmacogenetics; Pharmacological Treatment; Pharmacology; Phenotype; Pragmatic clinical trial; Problem behavior; Public Health; Quality of life; Randomized; Reporting; Research Project Grants; Risk; Risk Factors; Risperidone; Sampling; Self-Injurious Behavior; Services; Stroke; Weight; Weight Gain; Work; aripiprazole; atypical antipsychotic; autism spectrum disorder; autistic children; base; biobank; clinical care; clinical predictors; clinically relevant; cohort; comorbidity; design; efficacy evaluation; electronic consent; genetic association; genetic information; genetic predictors; genetic risk factor; genome wide association study; high risk; improved; individuals with autism spectrum disorder; innovation; innovative technologies; interest; machine learning method; machine learning model; neural network; obesity development; personalized care; pragmatic trial; predictive modeling; primary outcome; public health relevance; random forest; repetitive behavior; social communication; standard care

Autism spectrum disorder (ASD) is the most common neurodevelopmental condition, occurring in 1 in 59 children and commonly associated with behavioral problems that include aggression, irritability, and self-injury that are highly disabling to children with ASD and their families. While behavioral approaches are sometimes effective for these problems, they are may not be readily accessible to all families, are usually not covered in older individuals, and may not provide complete benefit to some people with ASD. These issues leads to the use of pharmacological intervention, often with atypical antipsychotics (ATAP) such as risperidone or aripiprazole. These two ATAPs are FDA approved to treat severe behavior disturbances such as aggression and irritability in ASD, and while ATAPs can be effective, these drugs are associated with increased weight gain, with a high risk of developing obesity. Understanding the clinical and genetic predictors of weight gain, and the differential effects of the most commonly used ATAPs on weight gain, is critical to improving the health of individuals with ASD. The objective of the Research Project is to address the need for precision use of ATAPs in ASD. Our Specific Aims will: (1) develop an electronic health record (EHR) based predictive model of atypical antipsychotic (ATAP)-induced weight gain in ASD, using a large and unique de-identified institutional database; (2) identify pharmacogenetic risk factors associated with ATAP-induced weight gain in ASD harnessing existing genetic information linked to the EHR; and (3) compare rates of ATAP-induced weight gain in children with ASD randomized to one of two FDA-approved ATAPs via a pragmatic trial that will take place in an outpatient clinic setting. Other innovative aspects of the pragmatic trial include the use of a modified electronic consent to decrease participant/caregiver burden, the incorporation of EHR embedded health measures to increase trial efficiency, and inclusion of a caregiver-reported outcome, the Aberrant Behavior Checklist – Irritability scale, embedded in the EHR. To accomplish these Aims, we will (1) Use machine learning methods to develop predictive modeling of ATAP-induced weight gain; (2) Estimate the contribution of genetic data to ATAP-induced weight gain, and (3) carry out a pragmatic clinical trial in children with ASD requiring ATAP treatment. The Research Project is a key element of our IDDRC renewal through its interaction with the IDDRC Cores, particularly the Clinical Translational Core which will manage the pragmatic trial, the Data Science Core, which will analyze resulting data, and the Administrative Core, which will promote dissemination efforts as well as stakeholder involvement in the design and conduct of the pragmatic trial. It addresses three focus areas within the parent RFA: (1) Interventions and Management of Co-morbid Mental Health Conditions; (2) Innovative Technologies to Improve Assessments, Interventions, and Outcomes for Those with IDD; and (3) Outcome Measures or Biomarkers for Interventions or Treatments.

自闭症谱系障碍（ASD）是最常见的神经发育疾病，发病率为59分之一