Trajectories of Medication Adherence in Pediatric Organ Transplantation and Their Relationship to Posttransplant Health Outcomes

儿科器官移植中药物依从性的变化轨迹及其与预后的关系 移植后的健康状况 项目摘要/摘要 不坚持使用免疫抑制药物仍然是晚期急性胰腺炎最重要的预测因素之一 排斥反应(LAR)，住院次数和频率增加，需要再次移植，甚至 儿科移植患者的死亡率。用药水平变异指数(MLVI)已被验证为一种 用药依从性的量度，并根据常规临床期间获得的用药血液水平计算 照顾这些病人。然而，很少有研究考察依从性的纵向轨迹 免疫抑制药物和移植后结果的可能差异。进一步的研究是 了解儿科器官移植用药依从性变化轨迹的必要性 生长混合模型(GMM)是一种新的统计方法。我们建议解决这些问题 通过检查从最大的儿科医院收集的数据中MLVI值的轨迹来发现知识差距 全国的移植中心。来自器官共享联合网络的患者和管理数据 (UNOS，美国卫生与公众服务部)将与患者电子链接 健康记录(EHR)。我们将使用潜在增长曲线分析和一种新的GMM三步法来实现这一点 分析技术：1)检查患者和家族性危险因素与长期的关联性 移植后的健康结果，2)估计功能形式的药物增长轨迹，3)检查 MLVI值增长的级别数，并识别具有类似级别的患者，以及4)检查 生长类型与患者和家庭水平的预测因素以及移植后健康之间的关系 结果。了解患者和家庭层面对个人成长类别的预测将有助于 评估和干预努力，以确定哪些患者和家属可能是不遵守规定的最大风险 随着时间的推移和移植后糟糕的结果。结果：旨在推广应用于全国儿科器官移植 中心，儿童和青少年慢性病的其他领域，以及未来研究和 制定干预措施。

Trajectories of Medication Adherence in Pediatric Organ Transplantation and Their Relationship to Posttransplant Health Outcomes Project Summary / Abstract Non-adherence to immunosuppressive medication remains one of the most significant predictors of late acute rejection (LAR), increased number and frequency of hospitalizations, need for retransplantation, and even mortality in pediatric transplant patients. Medication Level Variability Index (MLVI) has been validated as a measure of medication adherence and calculated from medication blood levels obtained during routine clinical care of these patients. However, few studies have examined longitudinal trajectories of adherence to immunosuppressive medication and possible differences in posttransplant outcomes. Further research is necessary to understand variation in trajectories of medication adherence in pediatric organ transplantation with Growth Mixture Modeling (GMM) proposed as a novel statistical approach. We propose to address these gaps in knowledge by examining trajectories of MLVI values in data collected from one of the largest pediatric transplant centers in the country. Patient and administrative data from the United Network for Organ Sharing (UNOS, U.S. Department of Health and Human Services) will be electronically linked with patient electronic health records (EHR). We will use latent growth curve analysis and a novel GMM three-step approach to this analytic technique to 1) examine the association between patient and familial risk factors and long-term posttransplant health outcomes, 2) estimate the functional form medication growth trajectories, 3) examine the number of classes of growth in MLVI values and identify patients with similar classes, and 4) examine association between classes of growth and both patient- and family-level predictors and posttransplant health outcomes. Understanding the patient- and family-level predictors of classes of individual growth will inform assessment and intervention efforts as to which patients and families may be greatest risk for non-adherence over time and poor posttransplant outcomes. Results aim to be applicable to national pediatric organ transplant centers, other areas of chronic illness in childhood and adolescence, and the basis for future research and development of interventions.