Sarcopenia: computable phenotypes and clinical outcomes.

项目总结 骨质疏松症是一种全身性肌肉疾病，随着年龄的增长而发展，并使许多常见的 慢性疾病，导致肌肉质量下降、虚弱和身体功能受损。石棺减少症 导致残疾、住院增加、医疗费用和死亡风险。尽管是在- 临床上公认，骨质疏松症是一个主要的公共卫生问题，预计全世界的流行率将 在未来30年内增长高达72%。然而，临床医生对石棺减少症的了解有限， 再加上临床上的时间压力，延误了对其的检测，并限制了干预或 招募进入临床试验。为了克服这个检测石棺减少症的障碍，我们建议使用高级 大数据和机器学习方法识别预测骨质疏松症的额外成分变量 在丰富的电子健康记录(EHR)数据中，开发一种经过验证的便携式石棺 可计算的表型(使用计算机算法来检测患者的特征或结果 电子病历)。这项创新的提案利用了印第安纳大学及其附属机构的关键资源 与Regenstrief研究所和印第安纳州患者护理网络(INPC)合作，这是一个全州范围的多医疗系统 临床数据仓库包括&gt；100个医疗保健实体和&gt；1800万个具有编码和 基于文本的数据，结合在 肌肉骨骼功能成像和组织(MSK-FIT)核心由NIAMS核心中心资助 临床研究资助(P30AR072581)。我们的长期目标是准确地识别患有或有风险的患者 石棺减少及其后果，以便提供有针对性的干预措施。我们假设，通过使用 医学信息学和机器学习的创新，可计算的表型可以识别患者 来自EHR的骨量减少，预示着测量的肌肉力量和身体功能的缺陷，以及 前瞻性预测住院和死亡风险。在目标1中，我们将对2000名成人参与者进行分类 MSK-Fit核心具有可访问的EHR数据，无论是石蜡还是非石蜡，根据 测量肌肉力量、肌肉质量和身体性能。然后我们将使用75%的MSK- Fit Core队列来训练机器深度学习算法，以检测这些变量的组合 受试者的EHR预测患者是否有石棺生成。由此产生的性能 然后，将在其余25%的MSK-Fit核心参与者中测试可计算的表型。在目标2中，我们 将测试石棺减少症可计算表型的性能以检测具有临床意义的表型 在整个INPC成年人口(1800万英镑)中，通过评估预测住院率的能力 与匹配的对照组相比，被评为石棺生成的患者的死亡率。这种可计算的表型将 从而实现大规模的定向招募、务实的临床试验、临床评估和干预。

PROJECT SUMMARY Sarcopenia is a generalized muscle condition that develops with aging and complicates many common chronic diseases, resulting in low muscle mass, weakness, and impaired physical function. Sarcopenia contributes to disability, increased hospitalizations, healthcare costs, and risk of death. Despite being under- recognized clinically, sarcopenia is a major public health concern, with the worldwide prevalence projected to increase by up to 72% in the next 30 years. However, limited knowledge of sarcopenia among clinicians, combined with time pressures in clinical encounters delay its detection, and limit opportunity for intervention or recruitment into clinical trials. To overcome this barrier to detecting sarcopenia, we propose to use advanced big data and machine learning methods to identify additional component variables predicting sarcopenia among the rich electronic health record (EHR) data and develop a validated and portable sarcopenia computable phenotype (which uses a computer algorithm to detect patient characteristics or outcomes from the EHR). This innovative proposal takes advantage of key resources at Indiana University and its affiliation with the Regenstrief Institute and the Indiana Network for Patient Care (INPC), a statewide multi-health system clinical data warehouse including >100 healthcare entities and >18 million unique patients with both coded and text-based data, combined with the ability to perform comprehensive musculoskeletal measurements in the Musculoskeletal Function Imaging and Tissue (MSK-FIT) Core funded through a NIAMS Core Center for Clinical Research grant (P30AR072581). Our long-term goal is to accurately identify patients with, or at risk for, sarcopenia and its consequences in order to provide targeted interventions. We hypothesize that by using medical informatics and machine learning innovations, computable phenotypes can identify patients with sarcopenia from the EHR, predict deficits in measured muscle strength and physical function, and prospectively predict risk of hospitalization and death. In Aim 1, we will categorize >2000 adult participants in the MSK-FIT Core with accessible EHR data, as either sarcopenic or nonsarcopenic according to measurements of muscle strength, muscle mass and physical performance. We will then use 75% of the MSK- FIT Core cohort to train machine deep learning algorithms to detect combinations of variables from these subjects’ EHR predicting whether the patient is sarcopenic or not sarcopenic. The performance of the resulting computable phenotype will then be tested in the remaining 25% of the MSK-FIT Core participants. In Aim 2, we will test the performance of the sarcopenia computable phenotype to detect a clinically meaningful phenotype in the entire INPC adult population (>18 million), by evaluating the ability to predict the rate of hospitalizations and death among patients rated as sarcopenic versus matched controls. Such a computable phenotype will then enable large scale targeted recruitment, pragmatic clinical trials, clinical evaluation and intervention.