Biomarkers for pressure injury risk following spinal cord injury: Development of a multi-scalar predictive model for personalized preventive health care

脊髓损伤后压力性损伤风险的生物标志物：开发用于个性化预防保健的多标量预测模型

基本信息

批准号：
10832456
负责人：
KATH BOGIE
金额：
--
依托单位：
LOUIS STOKES CLEVELAND VA MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-11-01 至 2024-09-30
项目状态：
已结题

项目摘要

Pressure injures (PrI) are a major secondary complication for far too many people with spinal cord injury (SCI). Development and/or recurrence of a PrI limits activities of daily living, often leading to hospitalization and even death. In addition to the devastating impact on affected individuals and their caregivers, PrI management has a significant effect on Veterans Heath Administration healthcare costs, which provides lifetime care for our Veterans with SCI. The proposed study will address the conundrum of why some Veterans with SCI suffer from a continuous cycle of recurring PrI, while others remain PrI free. The research strategy will build on the model developed by the Bogie lab of Biomarkers for Early Identification of Pressure Injury Risk (BEIPIR) for persons with SCI. BEIPIR unifies hierarchical relationships between clinical factors, health behaviors and muscle composition. We have shown that intramuscular adipose tissue (IMAT) is a critical clinically significant risk factor for PrI development. IMAT levels and accumulation rates vary greatly in this cohort. Some people exhibit rapid IMAT accumulation following SCI, while others do not. It is important to explain what is driving these changes. Our preliminary findings provide the basis for the central hypothesis: DNA variants predispose some individuals to increased deposition of IMAT following SCI, and resultant increased PrI risk. The proposed study will update the BEIPIR model by examining IMAT in conjunction with investigation of DNA variants associated with accelerated and/or higher levels of IMAT deposition. The TruSight™ One Expanded Sequencing panel (Illumina, San Diego CA) will be applied for Next Generation Sequencing of 50 existing blood samples from 38 persons with complete or incomplete SCI (AIS A-D) for whom gluteal muscle composition over time has already been evaluated. Genetic profile information, specifically DNA variants which are differentially active between persons with and without a history of PrI at a statistically significant level of p<0.05, will be selected and incorporated into the multi-scalar BEIPIR model for early identification of PrI risk. The updated BEIPIR model will be internally and externally validated to establish predicative efficacy. Internal validation of the BEIPIR model will be provided by testing the model with the genetic biomarkers identified. Split bootstrap procedures will be employed in order to derive stable estimations with low bias. A four year repeated measures study will be carried out to externally validate the BEIPIR model. A stratified study design will be employed to achieve a study cohort of 100 Veterans with SCI (AIS A-D) including participants with and without a history of PrI. Study participants will be recruited from Louis Stokes Cleveland VA Medical Center and the James J. Peters VA Medical Center (Site PI: Dr. Galea). Whole blood will be collected from study participants and DNA extracted prior to processing using the TruSight™ One Expanded Sequencing panel. Very low dose transverse pelvic region CT scans with contrast will be carried out based on our established protocol. Muscle composition and cross-sectional area will be determined using our established Hounsfield Unit scale classification protocol to determine relative lean muscle and IMAT content. 3D reconstruction will be applied to show IMAT distribution throughout the muscle. Study participants will be surveyed monthly by phone using our standardized skin status questionnaire to determine tissue health status. Incidences of tissue compromise or breakdown will be monitored and data applied to refine the BEIPIR model. Blood draw and CT scans will be repeated annually or when a PrI occurs. Longitudinal repeated measures of the de novo study cohort will be used to evaluate BEIPIR model performance and provide external validation of the model. Update and validation of the BEIPIR model will provide a clinical tool to optimize personalized care, recognizing that every person with SCI is an individual. Our proposed study has great potential to improve PrI risk assessment, enhance health status and quality of life for Veterans with SCI and reduce VHA costs. In the longer term, the BEIPIR model may provide the basis for development of a blood test kit for PrI risk. This research will also expand the de-identified genetic data publicly available for this underrepresented population.

对于太多脊髓损伤 (SCI) 患者来说，压力损伤 (PrI) 是主要的继发并发症。 PrI 的发展和/或复发限制了日常生活活动，常常导致住院，甚至导致死亡除了对受影响的个人及其护理人员造成毁灭性影响外，PrI 管理还具有以下特点：对退伍军人健康管理局的医疗费用产生重大影响，为我们提供终身护理患有 SCI 的退伍军人拟议的研究将解决为什么一些患有 SCI 的退伍军人患有 SCI 的难题。重复性 PrI 的连续循环，而其他则保持无 PrI 研究策略将建立在该模型的基础上。由 Bogie 实验室开发的用于早期识别压力损伤风险 (BEIPIR) 的生物标记物 BEIPIR 与 SCI 统一了临床因素、健康行为和肌肉之间的层次关系。我们已经证明，肌内脂肪组织（IMAT）是一个关键的临床重要危险因素。某些人的 IMAT 水平和积累率差异很大。 IMAT 的积累遵循 SCI，而其他的则不然。解释推动这些变化的因素很重要。我们的初步研究结果为中心假设提供了基础：DNA 变异使某些个体易感 SCI 后 IMAT 沉积增加，以及由此导致的 PrI 风险增加。拟议的研究将更新。 BEIPIR 模型通过检查 IMAT 并结合与相关 DNA 变异的研究 TruSight™ One Expanded 测序面板（Illumina、加利福尼亚州圣地亚哥）将用于对 38 人的 50 份现有血液样本进行下一代测序患有完全或不完全 SCI (AIS A-D) 的患者，其臀肌成分随时间的变化已经评估遗传图谱信息，特别是在人与人之间活性差异的 DNA 变异。有或没有 P<0.05 的统计显着水平的 PrI 病史，将被选择并纳入用于早期识别 PrI 风险的多标量 BEIPIR 模型更新后的 BEIPIR 模型将在内部进行。 BEIPIR 模型的内部验证将由外部验证来确定预测功效。将采用分离引导程序来测试已识别的遗传生物标记的模型。将对外部进行为期四年的重复测量研究，得出稳定的估计。将采用分层研究设计来验证 BEIPIR 模型，以实现 100 名退伍军人的研究队列。 SCI (AIS A-D) 包括有或没有 PrI 病史的参与者研究参与者将从中招募。 Louis Stokes Cleveland VA 医疗中心和 James J. Peters VA 医疗中心（站点 PI：Dr. Galea）。将从研究参与者收集全血，并在使用 TruSight™ 进行处理之前提取 DNA 将进行一张具有对比效果的极低剂量横向骨盆区域 CT 扫描。将根据我们既定的协议确定肌肉成分和横截面积。建立了 Hounsfield 单位量表分类协议来确定相对瘦肌肉和 IMAT 3D 含量。将应用重建来显示 IMAT 在整个肌肉中的分布。使用我们的标准化皮肤状态调查问卷每月进行一次电话调查，以确定组织健康状况。将监测组织受损或损坏的发生率，并应用数据来完善 BEIPIR 模型。每年或在发生 PrI 时重复进行抽血和 CT 扫描。 de novo 研究队列将用于评估 BEIPIR 模型性能并提供外部验证 BEIPIR 模型的更新和验证将为优化个性化护理提供临床工具，认识到每个 SCI 患者都是独立的个体，我们提出的研究具有改善 PrI 的巨大潜力。风险评估，提高 SCI 退伍军人的健康状况和生活质量，并降低 VHA 成本。从长远来看，BEIPIR 模型可能为开发 PrI 风险血液检测试剂盒提供基础。研究还将扩大针对这一代表性不足的人群公开的去识别化遗传数据。