Monitoring Lower Limb Movement to Predict Ambulatory Ability after Spinal Cord Injury

脊髓损伤(SCI)后，临床医生必须迅速决定在哪里集中治疗时间最大化。 个人出院时的功能性活动：步态训练或轮式活动干预。 临床预测规则(CPR)可以帮助临床医生做出这些困难的决定，但文献已经 研究表明，对于中度损伤的个体，目前使用年龄、力量和感觉的CPR是 不足以预测独立行走。此外，现有的CPR不能提供对临床的洞察 步态质量、效率和耐力的重要描述性指标 走动。我们最近的工作展示了接受步态训练的个体，但主要使用了 SCI后一年轮椅接受较少的转移和轮式活动训练，并有较低的 与从未接受过步态训练的非步行个体相比，参与的人数更多。在不断减少的背景下 住院康复治疗的时间长短，有效地利用治疗时间以最大限度地发挥功能至关重要 并避免这些长期后果。 使用活动监视器捕获的下肢运动(LLM)可能提供更敏感的测量 力量和感觉比传统的方法，如手动肌肉和光触摸感觉测试。这 技术是新颖的，因为脊髓损伤患者的LLM尚未在文献中报道。我们的预赛 分析表明，LLM、力量和行走能力之间的关联是有希望的(定义如下 步态质量、效率和耐力的衡量标准)。使用机器学习技术，我们能够 在LLM受试者中，确定哪些因素与行走能力有最强的关联 人口学、临床特征和其他协变量。 我们的长期目标是改进预测脊髓损伤后行走的CPR，从而适当地 有针对性的功能活动能力训练。作为迈向这一目标的第一步，我们将建立以下基础知识 LLM及其作为潜在生物标记物的关系在横断面研究中的应用 慢性脊髓损伤和已知的、多样的功能能力(目标1)。我们还将探索纵向LLM数据和 急性脊髓损伤患者的步行能力(目标2)，以评估LLM随时间的变化并创建 初步预测模型。 实现建议的目标将为预测个人流动性的因素提供新的见解 并提供关于这些因素在损伤后如何急剧变化的理解。此外，我们还将 获得洞察力以指导未来的多部位纵向研究，以评估新的、更有效的CPR。这个心肺复苏术 将通过允许最佳的靶向治疗来帮助脊髓损伤患者的临床决策 在整个康复过程中使用，从而改善长期功能结果。

After a spinal cord injury (SCI), clinicians must quickly decide where to focus therapy time to maximize an individual's functional mobility by discharge: either towards gait training or wheeled mobility interventions. Clinical prediction rules (CPRs) can assist clinicians in making those difficult decisions, but literature has shown that for individuals with moderate impairments, current CPRs that use age, strength, and sensation are not sufficient in predicting independent ambulation. Further, existing CPRs do not provide insight into clinically important descriptive measures of gait quality, efficiency, and endurance that contribute to functional ambulation. Our recent work demonstrated individuals who received gait training, but primarily used a wheelchair one year after SCI received less transfer and wheeled mobility training and had lower measures of participation than non-ambulatory individuals who never received gait training. In the context of decreasing inpatient rehabilitation length of stays, it is crucial that time in therapy be used efficiently to maximize function at discharge and avoid those long-term consequences. Lower limb movement (LLM) captured using activity monitors may provide a more sensitive measure of strength and sensation than traditional methods such as manual muscle and light touch sensation testing. This technique is novel in that LLM has not yet been reported in literature for individuals with SCI. Our preliminary analysis has shown promise for the association between LLM, strength, and ambulatory ability (as defined by measures of gait quality, efficiency, and endurance). Using machine learning techniques, we are able to determine which factors have the strongest association with ambulatory ability, among LLM, subject demographics, clinical characteristics, and other covariates. Our long-term goal is to improve CPRs that predict ambulation after SCI, thus enabling appropriately targeted functional mobility training. As a first step towards this goal, we will build a foundational knowledge of LLM and its relationship as a potential biomarker for ambulatory ability cross-sectionally among individuals with chronic SCI and known, diverse functional abilities (Aim 1). We will also explore longitudinal LLM data and ambulatory ability for a population with acute SCI (Aim 2) to evaluate changes in LLM over time and create a preliminary prediction model. Achieving the proposed aims will provide new insights into factors that predict mobility in individuals with SCI and provide understanding as to how these factors change acutely following injury. Further, we will gain insight to guide a future multisite longitudinal study that will assess a new, more effective CPR. This CPR will aid clinical decision-making for individuals with SCI by allowing for optimally targeted therapies to be employed throughout the rehabilitation continuum, thus improving long-term functional outcomes.