Functional Biomarkers for Prediction of Hospitalization, Long-Term Care Placement, and Mortality in Older Adults with Cardiometabolic Multimorbidity

项目摘要 老年人的心脏代谢性多病(CMM)是导致 美国的发病率和死亡率都是如此。多年来，越来越多的 有证据表明在临床中包括身体机能的重要性。 对老年人的评估。然而，当前的风险预测模型主要集中在 合并症，不包括身体功能，在老年人中表现出不一致的准确性 成年人。最近的多发病研究表明，有必要包括对 身体和机能状况对健康结果的影响，而不是仅仅依赖于 疾病的增加。 步态速度和握力是实用和经济的筛查工具，如 它们简单、廉价、可靠、易于执行，并且可以集成到社区中 因为筛查测试只需要很少的训练。因为多发病对健康结果的影响是 受身体机能的显著影响，检查可靠的功能生物标志物-步态速度 而握力是至关重要的。然而，在患有CMM的老年人中，步态的预后价值 速度和握力还没有被检查过。 这项研究的目的是1)描述四个预先确定的老年人的风险概况 使用社会人口学特征、步态速度、握力的相互排斥的三坐标测量机集群 体力和健康结果(住院、长期安置和死亡率)；2)确定 并通过检查轨迹来比较不同组之间功能生物标志物的变化率 在四个预先确定的相互排斥的CMM集群内的步速和手握力 在5年的时间里；3)建立步态速度和手握力的分界点 1)住院风险，2)长期护理安置和3)死亡率 独有的CMM集群，并确定敏感性、特异性和似然比 确定了切入点。 为了回答这些问题，我们链接了来自国家健康和老龄化趋势研究的数据 (NHATS)到Medicare索赔数据，以纵向评估功能生物标记物和健康 结果。潜在的班级增长曲线模型将被用来检查步态速度的轨迹 在10年的时间里，患有CMM的老年人的手握力。操作中的接收器 将计算特征(ROC)曲线和曲线下面积(AUC)以识别 以最佳敏感度、特异度和似然比定义步态速度的分界点 手握力可预测住院、长期安置和死亡率。

Project Summary Cardiometabolic multimorbidity (CMM) in older adults represents the largest cause of morbidity and mortality both in the United States. Over the years, an increasingly large amount of evidence has demonstrated the importance of including physical function in the clinical assessment of older adults. However, current risk-prediction models have primarily focused on comorbidities, do not include physical function, and have shown inconsistent accuracy in older adults. Recent multimorbidity research has demonstrated the need to include an examination of physical and functional status on their influence on health outcomes, rather than relying on a mere addition of diseases. Gait speed and hand-grip strength are pragmatic and cost-effective screening tools as these are simple, inexpensive, reliable, easy to perform, and can be integrated in the community as screening tests with little training. Since the impact of multimorbidity on health outcomes is notably influenced by physical function, examination of reliable functional biomarkers -gait speed and hand-grip strength is critical. However, in older adults with CMM, the prognostic value of gait speed and hand-grip strength has not been examined. This study aims to 1) characterize risk profiles of older adults in the four pre-identified mutually exclusive CMM clusters using sociodemographic characteristics, gait speed, hand-grip strength, and health outcomes (hospitalizations, long-term placement, and mortality); 2) Identify and compare rates of change in functional biomarkers between groups by examining trajectories of gait speed and hand-grip strength within the four pre-identified mutually exclusive CMM clusters over a period of 5 years and; 3) to establish gait speed and hand-grip strength cut-off points for 1) risk of hospitalization, 2) long-term care placement and 3) mortality in the four mutually exclusive CMM clusters, and determine the sensitivity, specificity, and likelihood ratios of the identified cut points. To answer these questions, we link data from the National Health and Aging Trends Study (NHATS) to Medicare claims data to longitudinally assess functional biomarkers and health outcomes. Latent class growth curve modelling will be used to examine trajectories of gait speed and hand-grip strength over the course of 10 years in older adults with CMM. A receiver operating characteristic (ROC) curve and the area under the curve (AUC) will be calculated to identify the cut-off points with optimal sensitivity, specificity, and likelihood ratios for defining gait speed and hand-grip strength to predict hospitalization, long-term placement, and mortality.