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发性硬化症（CMM）是老年人心脏病的最大原因。 发病率和死亡率都在美国。多年来，越来越多的 有证据表明，在临床中包括身体功能的重要性 评估老年人。然而，目前的风险预测模型主要集中在 合并症，不包括身体功能，并且在老年人中显示出不一致的准确性。 成年人了最近的多变量研究表明，需要对 身体和功能状况对健康结果的影响，而不是仅仅依靠 疾病的增加。 步态速度和握力是实用和成本效益的筛选工具， 这些方法简单、便宜、可靠、易于执行，并且可以集成到社区中 作为筛选测试，几乎没有训练。由于多药疗法对健康结果的影响是 显著受身体功能、可靠功能生物标志物检查-步态速度的影响 和握力是关键。然而，在老年CMM患者中， 速度和握力没有被检查。 本研究的目的是：1）描述老年人在四个预先确定的 使用社会人口统计学特征、步态速度、握力 强度和健康结果（住院，长期安置和死亡率）; 2）识别 并通过检查轨迹来比较各组之间功能性生物标志物的变化率 步态速度和握力在四个预先确定的相互排斥的CMM集群 在5年的时间内; 3）建立步态速度和手握力的截止点， 1)住院风险，2）长期护理安置和3）死亡率在四个相互 专属CMM集群，并确定敏感性，特异性和似然比的 确定的切割点。 为了回答这些问题，我们将国家健康和老龄化趋势研究的数据 （NHATS）到医疗保险索赔数据，以纵向评估功能性生物标志物和健康 结果。潜在类增长曲线建模将用于检查步态速度的轨迹 和握力在10年的过程中，老年人与CMM。操作的接收机 将计算特征性（ROC）曲线和曲线下面积（AUC）以鉴定 具有最佳灵敏度、特异性和似然比的截止点，用于定义步态速度， 握力预测住院、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.