Sarcopenia: computable phenotypes and clinical outcomes.

肌肉减少症：可计算的表型和临床结果。

基本信息

批准号：
10378772
负责人：
Erik Allen Imel
金额：
$ 16.72万
依托单位：
INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-04-20 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10378772
关键词：
Adult Aging Algorithms Automated Clinical Decision Support Awareness Big Data Big Data Methods Birth Cessation of life Characteristics Chronic Chronic Disease Chronic Kidney Failure Clinical Clinical Data Clinical Trials Code Computational algorithm Data Data Element Data Set Data Sources Detection Diagnosis Disease Electronic Health Record Exercise Funding Goals Grant Hand Strength Health Care Costs Health system Healthcare Healthcare Systems Hospitalization Image Impairment Indiana Individual Institutes Intervention Knowledge Machine Learning Measurement Measures Medical Informatics Methods Muscle Musculoskeletal National Institute of Arthritis and Musculoskeletal and Skin Diseases Natural Language Processing Outcome Participant Patient Care Patient Recruitments Patient-Focused Outcomes Patients Performance Pharmacology Phenotype Physical Function Physical Performance Physicians Population Pragmatic clinical trial Prevalence Process Provider Public Health Public Health Informatics Publishing Race Reporting Research Personnel Research Project Grants Resources Risk Supervision Testing Text Time Tissues Training Universities age group base biomedical informatics clinical center clinical data warehouse clinical encounter cohort comorbidity computable phenotypes deep learning algorithm detection limit dietary disability electronic data experience hospitalization rates improved improved outcome innovation machine learning method mortality risk muscle form muscle strength performance tests physical conditioning population health portability pressure prevent prospective ranpirnase recruit reduced muscle mass research clinical testing sarcopenia sex text searching tool

项目摘要

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.

项目摘要肌肉减少症是一种普遍的肌肉状况，随着衰老的衰老而发展，许多常见慢性疾病，导致肌肉质量低，身体机能受损。肌肉减少症有助于残疾，住院增加，医疗保健费用和死亡风险。尽管不足在临床上认可的肌肉减少症是一个主要的公共卫生问题，全球患病率预计在未来30年中，最多增加72％。但是，临床医生对肌肉减少症的了解有限，结合临床遇到的时间压力，延迟其检测，并限制干预或招募临床试验。为了克服这一检测肌肉减少症的障碍，我们建议使用先进大数据和机器学习方法以识别预测肌肉减少症的其他组件变量在丰富的电子健康记录（EHR）数据中，并开发了经过验证的便携式肌肉减少症可计算表型（使用计算机算法来检测患者特征或结果 EHR）。这项创新的建议利用了印第安纳大学及其会员的关键资源与Regenstrief Institute和Indiana患者护理网络（INPC）一起，这是一个全州多健康系统临床数据仓库，包括> 100个医疗保健实体和> 1800万独特的患者，既有编码又基于文本的数据，结合了执行全面肌肉骨骼测量的能力肌肉骨骼功能成像和组织（MSK-FIT）通过NIAMS核心中心资助临床研究补助金（P30AR072581）。我们的长期目标是准确识别患有或有风险的患者肌肉减少症及其后果是为了提供有针对性的干预措施。我们通过使用医学信息和机器学习创新，可计算的表型可以识别患者 EHR的肌肉减少症预测在测得的肌肉力量和身体机能中定义，并且前瞻性预测住院和死亡的风险。在AIM 1中，我们将类别> 2000名成年参与者具有可访问的EHR数据的MSK-FIT核心，作为肌肉减少症或非核心减少元素测量肌肉力量，肌肉质量和身体性能。然后，我们将使用75％的MSK- 适合核心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预测患者是否具有肌肉减少症。由此产生的表现然后，将在其余25％的MSK-FIT核心参与者中测试可计算的表型。在AIM 2中，我们将测试可计算表型的性能以检测临床意义的表型在整个INPC成年人口（> 1800万）中，通过评估预测住院率的能力和被视为肌肉减少剂与匹配对照的患者的死亡。这样的可计算表型将然后实现大规模的针对性招募，实用临床试验，临床评估和干预。