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）的人来说，压力损伤（PRI）是主要的次要并发症。 PRI的开发和/或复发限制了日常生活的活动，通常会导致住院甚至死亡。除了对受影响个人及其护理人员的毁灭性影响外，PRI管理还有一个对退伍军人希思管理医疗保健费用的重大影响，这为我们提供了终生护理有科幻的退伍军人。拟议的研究将解决一个难题，说明某些有SCI的退伍军人遭受经常性PRI的连续循环，而其他人则保持不含PRI。研究策略将以模型为基础由生物标志物的转向架实验室开发，用于早期确定压力损伤风险（Beipir）与科幻。 Beipir Unifieds临床因素，健康行为和肌肉之间的分层关系作品。我们已经表明，肌内脂肪组织（IMAT）是至关重要的临床危险因素用于PRI开发。在该队列中，IMAT水平和积累率差异很大。有些人表现快 IMAT在SCI之后积累，而其他人则没有。重要的是要解释是什么推动了这些变化。我们的初步发现为中心假设提供了基础：DNA变体倾向于某些个体 SCI后IMAT的沉积增加，并增加PRI风险。拟议的研究将更新贝皮尔模型通过与与DNA变体的投资一起检查与IMAT相关的投资 IMAT沉积的加速和/或更高水平。 Trusight™一个扩展的测序面板（Illumina，圣地亚哥CA）将适用于38人的50个现有血液样本的下一代测序随着时间的流逝，臀部肌肉成分已经是完整或不完整的SCI（AIS A-D）评估。遗传概况信息，特别是人与人之间不同活性的DNA变异在有或没有统计学意义的P <0.05的情况下，有或没有PRI史，将被选中并纳入用于早期鉴定PRI风险的多量表贝皮尔模型。更新的Beipir模型将在内部进行，并且在外部验证以建立谓词有效性。 Beipir模型的内部验证将由用确定的遗传生物标志物测试模型。将雇用分裂的引导程序，以便得出稳定的估计，偏置低。将进行四年的重复措施研究验证贝皮尔模型。将采用分层研究设计来实现100名退伍军人的研究队列有SCI（AIS A-D），包括有或没有PRI史的参与者。研究参与者将从路易斯·斯托克斯·克利夫兰弗吉尼亚州医疗中心和詹姆斯·J·彼得斯弗吉尼亚州医疗中心（现场PI：Galea博士）。使用Trusight™在处理之前从研究参与者中收集全血和DNA 一个扩展的测序面板。将携带非常低剂量的骨盆区域CT扫描与对比度扫描根据我们既定的协议。肌肉组成和横截面将使用我们的建立的Hounsfield单位量表分类方案，以确定相对瘦的肌肉和IMAT含量。 3D 重建将用于显示整个肌肉的IMAT分布。研究参与者将每月通过电话调查我们的标准化皮肤状况调查表，以确定组织健康状况。将监控组织妥协或分解的事件，并应用数据以完善贝皮尔模型。抽血和CT扫描将每年重复或当PRI发生时。纵向重复测量从头研究队列将用于评估贝皮尔模型性能并提供外部验证模型。 Beipir模型的更新和验证将为优化个性化护理提供临床工具，认识到每个患有SCI的人都是个人。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。在从长远来看，Beipir模型可以为PRI风险开发血液测试试剂盒的开发提供基础。这研究还将扩大该人群不足的人群公共可用的遗传数据。