Optimizing TBI Inpatient Rehabilitation: Longitudinal Analysis of Intervention

项目摘要 创伤性脑损伤的住院患者康复研究主要是作为一个未分化的黑人 盒子前瞻性、观察性TBI实践循证（PBE）研究详尽记录了 成千上万的治疗疗程，丰富的纵向结果，以及无与伦比的 描述损伤严重程度和疾病并发症的特征，以便阐明护理的“黑匣子”。的 迄今为止的分析表明，六项“高影响力”活动在近20年内取得了更好的成果， 所有严重程度亚组和许多不同结局。 拟议的项目旨在确定和描述治疗反应者的特定人群 TBI-PBE数据库中的无应答者，并确定与 更好的每周随访和9个月的结果，控制患者特征和治疗反应类别。 我们将使用增长混合模型（GMM）来识别响应者和非响应者类别。的GMM 识别应答者和非应答者的方法优于更传统的终点 治疗反应分析：与终点分析相比，它减少了偏倚并改善了信号检测，因为 它解释了来自同一个体的测量之间的相关性和结果的不等方差 随着时间我们将使用协方差分析（ANCOVA）线性混合效应回归模型进行比较 基线后测量，每一组干预措施的累积时间。作为二次分析， 我们将在其他时间点观察脑梗死相关性，以确定早期治疗干预的特征。 该项目的完成将促进对住院康复护理的了解， 经历过中度和重度TBI并对康复非常有价值的人 在这个令人难以置信的动态恢复期间进行治疗计划。该项目利用了可能 是有史以来收集的关于TBI住院康复的最丰富的数据集，由于其起源于临床实践， 促进临床医生的高度可接受性，并证明医疗保健系统的可行性 持份者更准确和详细的信息，不同的个人如何应对 干预措施将允许在急性康复期间和之后进行更多针对患者的治疗计划 放电更好地了解干预措施的性质和时机将有助于 这将有助于更有效地利用有限的资源。此外，拟议的 研究直接建立在研究小组迄今为止的工作基础上，有可能克服重大的 实现更知情、有效和公平的护理的障碍。

PROJECT SUMMARY Inpatient rehabilitation for traumatic brain injury has been studied largely as an undifferentiated black box. The prospective, observational TBI Practice-Based Evidence (PBE) study exhaustively documented hundreds of thousands of therapy sessions, a rich set of longitudinal outcomes, and an unrivaled characterization of injury severity and disease comorbidity in order to shed light on this “black box” of care. The analyses to date demonstrated that six “high impact” activities were associated with better outcomes in nearly all severity subgroups and across many different outcomes. The proposed project intends to identify and characterize specific populations of treatment responders and non-responders in the TBI-PBE database and to identify therapy interventions that are associated with better weekly FIM and 9-month outcomes, controlling for patient characteristics and treatment response class. We will use growth mixture modeling (GMM) to identify responder and non-responder classes. The GMM approach to identification of responders and non-responders has advantages over more traditional end-point analysis of treatment response: it reduces bias and improves signal detection over end-point analyses because it accounts for correlations between measures from the same individual and unequal variances of the outcome over time. We will use analysis of covariance (ANCOVA) linear mixed effect regression models to compare post-baseline FIM measures with cumulative time on each of the set of interventions. As a secondary analysis, we will look at FIM associations at other time points to characterize early treatment interventions. The completion of this proposed project will advance knowledge about inpatient rehabilitation care for persons who have experienced moderate and severe TBI and will be highly valuable for rehabilitation treatment planning during this incredibly dynamic period of recovery. The project makes use of what may likely be the richest data set on TBI inpatient rehabilitation ever assembled, which due to its origin in clinical practice, fosters high levels of acceptability from clinicians and demonstrates feasibility to health care system stakeholders. The more accurate and detailed information about how different individuals respond to interventions would allow more patient-specific treatment planning during acute rehabilitation and after discharge. Better understanding of the nature and timing of interventions would inform the staging of therapeutic modalities and would result in more effective use of limited resources. In addition, the proposed research builds directly on the work of the study team to date and has the potential to overcome significant barriers to realizing more informed, effective and equitable care.