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: 10229594
• 负责人: Lea K Davis
• 金额: $ 24.01万
• 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/10229594
• 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; Machine Learning; Measures; Medical Genetics; Mental Health; Metabolic syndrome; Methods; Modeling; Neurodevelopmental Disorder; Obesity; Obsessive compulsive behavior; Outcome; Outcome Measure; Parents; Participant; Performance; 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; 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状况，在59中发生在1中。 儿童以及通常与行为问题有关的问题，包括侵略性，易怒和自我伤害 这对ASD及其家人的儿童高度致残。而行为方法有时是 对于这些问题有效，所有家庭可能不容易访问它们，通常不涵盖 老年人，可能不会为某些ASD的人提供完全的好处。这些问题导致 使用药物干预，通常具有非典型抗精神病药（ATAP），例如利培酮或 阿立哌唑。这两个ATAP被FDA批准用于治疗严重的行为障碍，例如侵略性 和ASD的烦躁不良，尽管ATAP可以有效，但这些药物与体重的增加有关 获得，具有肥胖的高风险。了解体重增加的临床和遗传预测因子， 最常用的ATAP对体重增加的差异影响对于改善健康至关重要 患有ASD的人。研究项目的目的是解决精确使用的需求 ASD中的ataps。我们的具体目的将：（1）开发基于电子健康记录（EHR）的预测模型 非典型抗精神病药（ATAP）诱导的ASD体重增加，使用大型且独特的识别机构 数据库; （2）确定与ASD中ASAP诱导的体重增加相关的药物遗传学危险因素 利用与EHR相关的现有遗传信息； （3）比较ATAP诱导的体重增加的速率 在ASD的儿童中，将通过务实的试验随机分为两个FDA批准的ATAP 门诊诊所。务实试验的其他创新方面包括使用修改 电子同意减少参与者/照顾者伯恩，雇用EHR嵌入式健康 提高试验效率的措施，包括护理人员报告的结果，异常行为 清单 - 易怒量表，嵌入EHR中。为了实现这些目标，我们将（1）使用机器 学习ATAP诱导的体重增加的预测建模的学习方法； （2）估计 遗传数据以诱导ATAP诱导的体重增加，（3）在ASD儿童中进行一项务实的临床试验 需要ATAP处理。研究项目是我们IDDRC通过其互动的关键要素 借助IDDRC核心，特别是将管理务实试验的临床翻译核心 数据科学核心将分析结果数据和行政核心，这将促进 传播工作以及利益相关者参与了务实试验的设计和行为。 解决家长RFA中的三个重点领域：（1）合并心理的干预和管理 健康状况； （2）改善评估，干预措施和结果的创新技术 患有IDD的人； （3）干预或治疗的结果指标或生物标志物。