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: 10085553
• 负责人: Lea K Davis
• 金额: $ 25.24万
• 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/10085553
• 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; Consent; 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; genetic association; genetic information; genetic predictors; genetic risk factor; genome wide association study; high risk; improved; 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疾病，发生率为五分之一 儿童，通常与行为问题有关，包括攻击性、易怒和自残 这对患有自闭症谱系障碍的儿童及其家人来说是严重的残疾，而行为方法有时却很困难。 对于这些问题有效，但它们可能并非所有家庭都能轻易获得，并且通常不包括在 老年人，可能无法为某些患有自闭症谱系障碍的人提供完全的好处。 使用药物干预，通常使用非典型抗精神病药（ATAP），例如利培酮或 这两种 ATAP 已获 FDA 批准用于治疗攻击行为等严重行为障碍。 和自闭症谱系障碍 (ASD) 患者的烦躁不安，虽然 ATAP 可能有效，但这些药物与体重增加有关 了解体重增加的临床和遗传预测因素， 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 该研究项目的目标是满足精确使用自闭症谱系障碍患者的需求。 ATAP 在 ASD 中的具体目标将：(1) 开发基于电子健康记录 (EHR) 的预测模型。 非典型抗精神病药物 (ATAP) 引起的 ASD 体重增加，使用大型且独特的去识别化机构 数据库；（2）确定与 ATAP 引起的 ASD 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 引起的体重增加率 通过一项实用性试验，患有 ASD 的儿童被随机分配到 FDA 批准的两种 ATAP 之一，该试验将在 实用试验的其他创新方面包括使用改良的方法。 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，并纳入护理人员报告的结果，即异常行为 检查表 – 烦躁量表，嵌入 EHR 中 为了实现这些目标，我们将 (1) 使用机器。 学习方法来开发 ATAP 引起的体重增加的预测模型；（2）估计 ATAP 引起的体重增加的遗传数据，以及 (3) 在自闭症谱系障碍 (ASD) 儿童中开展实用临床试验 需要 ATAP 治疗的研究项目是我们通过其互动进行 IDDRC 更新的关键要素。 与 IDDRC 核心一起，特别是管理实用试验的临床转化核心， 数据科学核心将分析结果数据，管理核心将促进 传播工作以及利益相关者参与务实试验的设计和实施。 解决母版 RFA 中的三个重点领域：(1) 共病心理的干预和管理 健康状况；(2) 改善评估、干预和结果的创新技术 患有 IDD 的人；以及 (3) 干预或治疗的结果衡量或生物标志物。