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: 10686015
• 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/10686015
• 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; Institution; Intellectual and Developmental Disabilities Research Centers; Intervention; Knowledge; Link; Measures; Mental Health; Metabolic syndrome; Methods; Modeling; Neurodevelopmental Disorder; Obesity; Obsessive compulsive behavior; Outcome; Outcome Measure; Parents; Participant; Performance; Persons; Pharmaceutical Preparations; Pharmacogenetics; Pharmacological Treatment; 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; biobank; clinical care; clinical predictors; clinical translation; 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; pharmacologic; 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，发生在1/59 通常与行为问题有关，包括攻击性、易怒和自伤 对自闭症儿童及其家庭造成严重伤害虽然行为方法有时 有效解决这些问题，它们可能不是所有家庭都容易获得，通常不包括在 老年人，可能不会为一些ASD患者提供完全的益处。这些问题导致 使用药物干预，通常使用非典型抗精神病药（ATAP），如利培酮或 阿立哌唑。这两种ATAP被FDA批准用于治疗严重的行为障碍，如攻击性 而ATAP虽然有效，但这些药物与体重增加有关 增加，有很高的风险发展肥胖。了解体重增加的临床和遗传预测因素， 以及最常用的ATAP对体重增加的不同影响，对于改善健康至关重要。 ASD患者的情况。该研究项目的目标是解决精确使用 ASD中的ATAP。我们的具体目标是：（1）开发一个基于电子健康记录（EHR）的预测模型， 非典型抗精神病药（ATAP）诱导的ASD体重增加，使用一个大型和独特的去识别机构 数据库;（2）确定与ASD中ATAP诱导的体重增加相关的药物遗传学风险因素 利用与EHR相关的现有遗传信息;（3）比较ATAP诱导的体重增加率 在ASD儿童中，通过一项将于2015年进行的实用性试验， 门诊诊所环境。务实审判的其他创新方面包括使用修改后的 电子同意书，以减少参与者/护理人员的负担，纳入EHR嵌入式健康 提高试验效率的措施，并纳入一个由双方报告的结局，即异常行为， 检查表-易怒量表，嵌入EHR。为了实现这些目标，我们将（1）使用机器 学习方法，以开发ATAP诱导的体重增加的预测模型;（2）估计以下因素的贡献： 遗传数据对ATAP诱导的体重增加，（3）在ASD儿童中进行实用的临床试验 需要ATAP治疗。该研究项目是我们的IDDRC通过其相互作用更新的关键要素 有了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，行政核心将促进 传播努力以及利益相关者参与设计和进行务实的试验。它 针对父RFA中的三个重点领域：（1）共病精神疾病的干预和管理 健康状况;（2）创新技术，以改善评估，干预和成果， IDD患者;（3）干预或治疗的结局指标或生物标志物。