Sarcopenia: computable phenotypes and clinical outcomes.

肌肉减少症：可计算的表型和临床结果。

• 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
• 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

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，导致肌肉质量低，虚弱和身体功能受损。肌肉减少 导致残疾，增加住院治疗，医疗保健费用和死亡风险。尽管在- 临床上公认，肌肉减少症是一个主要的公共卫生问题，预计全球范围内的患病率将 在未来30年内增长72%。然而，临床医生对肌肉减少症的认识有限， 与临床遇到的时间压力相结合，延迟了其检测，并限制了干预的机会， 临床试验招募。为了克服这一障碍，检测肌肉减少症，我们建议使用先进的 大数据和机器学习方法来识别预测肌肉减少症的其他成分变量 在丰富的电子健康记录（EHR）数据，并开发一个有效的和便携式肌肉减少症 可计算表型（使用计算机算法检测患者特征或结果， EHR）。这项创新的提案利用了印第安纳州大学及其附属机构的关键资源 与Regenstrief研究所和印第安纳州病人护理网络（INPC）合作，INPC是一个全州范围的多健康系统 临床数据仓库，包括> 100个医疗保健实体和> 1800万个独特的患者， 基于文本的数据，结合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）核心通过NIAMS核心中心资助， 临床研究补助金（P30AR 072581）。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，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创新，可计算表型可以识别患者 EHR的肌肉减少症，预测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> 2000名成人参与者分类为 具有可访问EHR数据的MSK-FIT核心，根据以下标准， 肌肉力量、肌肉质量和身体表现的测量。我们将使用75%的MSK- FIT核心队列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预测患者是否肌肉减少。结果的性能 然后将在其余25%的MSK-FIT核心参与者中测试可计算表型。在目标2中， 将测试肌肉减少症可计算表型的性能，以检测有临床意义的表型 在整个INPC成年人口（> 1800万）中，通过评估预测住院率的能力， 和死亡率之间的关系。这种可计算的表型将 从而实现大规模的靶向招募、务实的临床试验、临床评估和干预。