Physical Resilience Prediction in Advanced Renal Disease

晚期肾病的身体弹性预测

• 批准号: 9913382
• 负责人: Christopher Barrett Bowling
• 金额: --
• 依托单位: DURHAM VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9913382
• 关键词: Activities of Daily Living; Acute; Address; Advance Care Planning; Advisory Committees; Affect; Aftercare; Back; Caring; Characteristics; Chronic Kidney Failure; Clinical; Clinical Trials; Cohort Studies; Complex; Data; Decision Making; Dialysis procedure; Disease Outcome; Disease Progression; Elderly; Emergency department visit; Event; Face; Family; Future; Geriatrics; Glomerular Filtration Rate; Goals; Growth; Health; Health Status; Healthcare Systems; Home Nursing Care; Hospitalization; Impairment; Injury; Kidney; Kidney Diseases; Learning; Life; Life Expectancy; Long-Term Care; Longitudinal cohort study; Measures; Medical; Medical Records; Methods; Modality; Modeling; Needs Assessment; Patient Care; Patient-Centered Care; Patients; Persons; Physical Function; Physiological; Probability; Prospective cohort study; Provider; Psychosocial Factor; Services; Surveys; Telephone; Testing; Time; Uncertainty; Veterans; Work; base; body system; clinical decision-making; data warehouse; expectation; experience; functional decline; functional status; improved; model development; mortality; novel; older patient; operation; outcome prediction; patient oriented; predictive modeling; prognostic; prospective; psychologic; resilience; stressor; support tools; tool; trait

ABSTRACT Background: Older Veterans with advanced chronic kidney disease (CKD) face complex decisions to initiate or forgo dialysis in the context of uncertainty about their future health and physical function. Making these decisions is complicated by the course of advanced CKD which is characterized by frequent health events that further worsen function. Decisions support tools are needed that are specific to the clinical course of advanced CKD and predict outcomes that matter most to these patients, such as physical function. Characterizing how patients ‘bounce back’ from health events, such as illnesses or injuries that result in emergency department (ED) visits or hospitalizations may be key to predicting future functional status. This approach draws from the novel geriatric concept of physical resilience, defined as one’s ability to resist or recover from functional decline following a ‘health stressor.’ Objectives: To help older Veterans make informed decisions about kidney disease treatment by better characterizing physical resilience and identifying patient factors associated with physical resilience to develop a prediction tool for physical resilience in advanced CKD. This addresses the HSR&D priority of Patient-Centered Care domain. To do this, we propose Physical REsilience Prediction in Advanced REnal Disease (PREPARED), a prospective cohort study of older Veterans with advanced CKD with the following Aims: 1. To characterize physical function trajectories before and after an acute health stressor in order to define physical resilience among older Veterans with advanced CKD. 2. To identify associations between patient characteristics and physical resilience trajectory and potential candidate variables for prediction model development. 3. To develop a prediction tool for physical resilience (where this quantity has been defined in Aim 1). 4. To determine the association of physical resilience with short-term mortality. Methods: We will conduct a longitudinal cohort study of 800 Veterans ≥ 70 years old, with an estimated glomerular filtration rate (eGFR) < 30 ml/min/1.73 m2 (excluding dialysis or transplant), and 90-day probability of hospitalization ≥ 50% (based on the Care Assessment Needs [CAN] score). Telephone assessments will include brief validated measures of function every 8 weeks, and within 14 days following a stressor for up to 6 calls. In Aim 1, we will characterize physical resilience, first by identifying latent classes of physical resilience trajectories using general growth mixture modeling. Next, among the subset from the physical resilience latent trajectory class we will fit a piecewise linear mixed effects model to quantify resilience. In Aim 2, we will determine how the physical function trajectory is moderated by person-level health and psychosocial factors and organ system-level physiologic factors. This information will be used to identify potential candidate variables for our prediction model in Aim 3. The purpose of Aim 4 is to determine the prognostic importance of physical resilience by examining the relationship between experiencing a stressor and physical resilience with 6-month mortality. Impact: The proposed study addresses the most pressing clinical dilemma in this complex condition that disproportionately affects older Veterans. Data on physical resilience from the proposed study will be used to develop a practical tool to address a vital question that CKD patients, their families, and providers face when making treatment decisions. Limiting uncertainty about future health by predicting resilience will support individualized and patient-centered decision-making for kidney disease. Next steps: We will develop a clinical trial to test the use of our physical resilience prediction tool and work with our local and national operations partners (Durham VA Renal Service, Office of Geriatrics and Extended Care, Renal Field Advisory Committee) to implement physical resilience assessment into care for these patients.

摘要 背景：患有晚期慢性肾病（CKD）的老年退伍军人面临着复杂的决定， 或者在对他们未来的健康和身体功能不确定的情况下放弃透析。使这些 晚期CKD的病程使决策复杂化，其特征是频繁的健康事件， 进一步恶化功能。决策支持工具是需要的，具体到先进的临床过程 CKD，并预测对这些患者最重要的结果，如身体功能。如何描述 患者从健康事件中“反弹”，例如导致急诊室的疾病或伤害 (ED)访问或住院可能是预测未来功能状态的关键。这种方法借鉴了 一个新的老年医学概念的身体弹性，定义为一个人的能力，以抵抗或恢复功能下降 一个“健康压力源”。目的：帮助老年退伍军人对肾脏做出明智的决定 通过更好地表征身体弹性和识别与疾病相关的患者因素， 身体恢复力，以开发晚期CKD身体恢复力的预测工具。这解决了 以患者为中心的护理领域的HSR&D优先级。为了做到这一点，我们提出了物理阻力预测， 晚期肾脏病（PREPARED），一项在患有晚期CKD的老年退伍军人中进行的前瞻性队列研究， 以下目标： 1.描述急性健康应激源前后的身体功能轨迹，以确定 老年CKD退伍军人的身体恢复力。 2.确定患者特征与身体复原力轨迹和潜力之间的关联 预测模型开发的候选变量。 3.开发身体复原力的预测工具（目标1中对这一数量作了界定）。 4.确定身体复原力与短期死亡率之间的关系。 方法：我们将对800名≥ 70岁的退伍军人进行纵向队列研究，估计 肾小球滤过率（eGFR）< 30 ml/min/1.73 m2（不包括透析或移植），90天概率 住院率≥ 50%（基于护理评估需求[CAN]评分）。电话评估将 包括每8周进行一次简短的经验证的功能测量，并在压力刺激后14天内进行长达6次的功能测量。 电话在目标1中，我们将描述身体弹性，首先通过识别身体弹性的潜在类别 使用一般增长混合模型的轨迹。接下来，在来自潜在的物理弹性的子集中， 轨迹类，我们将拟合分段线性混合效应模型来量化弹性。在目标2中，我们将 确定身体功能轨迹如何受到个人健康和心理社会因素的调节 和器官系统水平的生理因素。这些信息将用于识别潜在的候选人 目标3中预测模型的变量。目标4的目的是确定以下预后重要性： 通过检查经历压力源和身体弹性之间的关系， 6-月死亡率影响：拟议的研究解决了这一复杂的临床难题。 这种情况不成比例地影响老年退伍军人。拟议研究中的身体复原力数据 将用于开发一种实用的工具，以解决CKD患者及其家人的重要问题， 医生在做出治疗决定时面临的问题。通过预测来减少未来健康的不确定性 弹性将支持个性化和以患者为中心的肾脏疾病决策。下一步：我们 将开发一项临床试验，以测试我们的身体弹性预测工具的使用，并与我们的当地和 国家业务合作伙伴（达勒姆VA肾脏服务，老年病和扩展护理办公室，肾脏领域 咨询委员会）实施身体复原力评估，以照顾这些病人。