Developing Models to Identify Veterans with Nonalcoholic Fatty Liver Disease and Predict Progression

开发模型来识别患有非酒精性脂肪肝的退伍军人并预测病情进展

• 批准号: 10177897
• 负责人: Lewis James Frey
• 金额: --
• 依托单位: RALPH H JOHNSON VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10177897
• 关键词: African American; Algorithms; Behavioral; Biopsy; Body Weight decreased; Body fat; Bupropion; Cardiovascular system; Caring; Characteristics; Cirrhosis; Clinic; Clinical; Data; Data Set; Development; Diagnosis; Dietary Intervention; Disease; Disease Progression; Dyslipidemias; Early identification; Enzymes; Epidemic; Ethnic Origin; Ethnic group; Event; Exercise; Fatty acid glycerol esters; Fibrinogen; Fibrosis; Goals; Health; Health Care Costs; Healthcare; Heart; High Prevalence; Hispanics; Hyperinsulinism; Hyperlipidemia; Hypertension; Improve Access; Incidence; Informatics; Infrastructure; Insulin Resistance; Intervention; Laboratories; Lead; Left; Life; Life Style; Link; Liver; Liver Cirrhosis; Liver Fibrosis; Liver diseases; Longitudinal trends; Machine Learning; Magnetic Resonance Spectroscopy; Malignant neoplasm of liver; Measures; Medical Records; Metabolic; Metabolic syndrome; Methodology; Methods; Modeling; Natural Language Processing; Non-Insulin-Dependent Diabetes Mellitus; Not Hispanic or Latino; Obesity; Outcome; Patient risk; Patients; Pharmacologic Substance; Pilot Projects; Population; Population Characteristics; Population Study; Prediabetes syndrome; Predictive Analytics; Prevalence; Race; Radiology Specialty; Recording of previous events; Records; Reporting; Research; Research Personnel; Risk; Risk Factors; Sensitivity and Specificity; Severities; Smoking; Subgroup; System; Testing; Time; Treatment outcome; Veterans; advanced disease; bariatric surgery; cardiovascular risk factor; caucasian American; cohort; comorbidity; convolutional neural network; cost; cost effective; deep learning; demographics; disease diagnosis; disorder risk; effective intervention; ethnic difference; ethnic disparity; high risk; improved; liver biopsy; liver injury; machine learning method; multidisciplinary; non-alcoholic fatty liver disease; nonalcoholic steatohepatitis; nutrition; personalized approach; predictive modeling; racial and ethnic; racial disparity; radiological imaging; random forest; tool; trend

Anticipated Impacts on Veterans Health Care: This proposal will use natural language processing (NLP) methods and machine learning approaches to provide and compare predictive models of non-alcoholic fatty liver disease (NAFLD) among Veterans. Proposed analyses will also examine racial/ethnic differences in NAFLD diagnosis, treatment, and outcomes with the goal of identify patient groups at highest risk of progression to liver cirrhosis and cirrhosis-related complications. The long-term goal of this research, which this pilot study will facilitate, is the development and effective targeting of integrated multidisciplinary treatment algorithms alongside simple, culturally appropriate, and cost-effective interventions to curb the epidemic of NAFLD and its complications among Veterans. Background: NAFLD is a significant and growing health problem closely associated with obesity, type 2 diabetes mellitus (T2DM), hypertension, and dyslipidemia. In the VA, NAFLD prevalence has been estimated as high as 46%. The prevalence of NAFLD varies significantly depending on the population studied and on the tests used. In the Dallas Heart Study, it was estimated that over 30% of patients had NAFLD by MR spectroscopy. Importantly, investigators found that the highest prevalence of NAFLD occurred among Hispanics (58%), and those with T2DM (over 70%). Hispanic populations have higher incidence of NAFLD and potentially higher rates of progression to advanced fibrosis, compared to non- Hispanic White (NHW) patients. Current therapy aims to optimize both cardiovascular and liver-related risk factors (i.e. T2DM, hypertension, hyperlipidemia, obesity, smoking etc.). Lifestyle changes driven by dietary intervention and exercise are the first line of therapy to induce and maintain weight loss, reducing fat mass, hyperinsulinemia and insulin resistance, thus decreasing lipotoxic liver damage and multisystem metabolic consequences. The VA NAFLD Clinic provides Intensive Weight Loss that includes nutrition, exercise, behavioral, VA approved pharmaceuticals (e.g., Bupropion/Naltrex, Lorcascerin) and bariatric surgery. Hence it is important to identify patients that are at high risk of progression to the poor outcomes associated with advanced NAFLD and provide treatments available at VA NAFLD Clinics. Objectives: In this 1-year pilot, we propose using the VA NAFLD Team curated cohort (n=61,900) of Veterans from the national Veteran Affairs Informatics and Computing Infrastructure (VINCI) system who have received liver biopsies. The dataset will be augmented to include medical records 8-years prior and 1- year post biopsy. We will use clustering and machine learning predictive analytic approaches to identify patients with higher risk of developing cirrhosis, cirrhosis-related complications, and cardiovascular events with a focused analysis on racial and ethnicity disparities. Methods: The machine learning methodology of convolutional neural networks and random forests will be used to identify NAFLD patients using NLP variables, laboratory values and comorbidities available in the patient records in the VINCI system. In order to identify rapidly progressing NAFLD patients we will cluster fibrosis risk score trend data. We will tailor the approach to identification of NAFLD and progression and augment it with machine learning analysis. The outcome of our pilot will be predictive models of NAFLD patients along with their severity estimate that can be used to determine which groups of patients are at higher risk of progression to cirrhosis, cirrhosis complications and cardiovascular events and thus, would benefit from a clinical intervention to proactively reduce their risk. The next steps is a follow on study that uses the models predicting high risk patients, derived in the pilot, as part of an intervention to improve access of Veterans with a high risk of progression to liver complications and cardiovascular events to appropriate care in VA NAFLD Clinics.

对退伍军人医疗保健的预期影响：该提案将使用自然语言处理(NLP) 提供和比较非酒精性脂肪预测模型的方法和机器学习途径 退伍军人的肝病(NAFLD)。拟议的分析还将审查种族/民族差异 非酒精性脂肪肝的诊断、治疗和结果，目的是确定最高风险的患者组 进展为肝硬变和与肝硬变相关的并发症。这项研究的长期目标是 这项先导性研究将促进综合多学科的发展和有效定向 治疗算法与简单、适合文化和成本效益的干预措施相结合，以遏制 退伍军人中非酒精性脂肪肝及其并发症的流行。 背景：非酒精性脂肪肝是一个重要且日益严重的健康问题，与2型肥胖密切相关。 糖尿病(T2 DM)、高血压和血脂异常。在退伍军人事务部，NAFLD的患病率一直是 估计高达46%。非酒精性脂肪肝的患病率因人口而异。 研究并研究了所使用的测试。在达拉斯心脏研究中，估计超过30%的患者患有 核磁共振光谱分析非酒精性脂肪肝。重要的是，研究人员发现，NAFLD的最高患病率 发生在拉美裔(58%)和患有T2 DM的人(超过70%)。西班牙裔人口的比例更高 与非酒精性脂肪肝相比，非酒精性脂肪肝的发病率和进展为晚期纤维化的可能性更高 西班牙裔白人(NHW)患者。目前的治疗旨在优化心血管和肝脏相关风险 危险因素(如2型糖尿病、高血压、高脂血症、肥胖、吸烟等)。饮食驱动的生活方式变化 干预和锻炼是诱导和保持体重减轻、减少脂肪量、 高胰岛素血症和胰岛素抵抗，从而减少脂毒性肝损伤和多系统代谢 后果。VA NAFLD诊所提供密集的减肥服务，包括营养、运动、 行为、退伍军人管理局批准的药物(如安非他酮/纳曲酮、洛卡斯林)和减肥手术。 因此，重要的是要识别出进展到相关不良结局的高风险患者。 提供先进的非酒精性脂肪肝，并在退伍军人管理局非酒精性脂肪肝诊所提供治疗。 目标：在这项为期一年的试验中，我们建议使用退伍军人管理局NAFLD团队管理的队列(n=61,900) 来自国家退伍军人事务信息学和计算基础设施(Vinci)系统的退伍军人 已经接受了肝脏活检。数据集将扩大到包括8年前和1年前的病历 活组织检查后一年。我们将使用聚类和机器学习预测分析方法来识别 发生肝硬变、肝硬变相关并发症和心血管事件风险较高的患者 重点分析种族和族裔差异。 方法：卷积神经网络和随机森林的机器学习方法将是 用于使用NLP变量、化验值和可在 芬奇系统里的病人记录。为了识别进展迅速的非酒精性脂肪肝患者，我们将 纤维化风险评分趋势数据。我们将量身定制识别NAFLD和进展的方法，并 用机器学习分析来增强它。我们的试验结果将是NAFLD的预测模型 患者及其严重程度估计可用于确定哪组患者处于 进展为肝硬变、肝硬变并发症和心血管事件的风险更高，因此 从主动降低风险的临床干预中受益。下一步是后续研究，即 使用试点中得出的预测高危患者的模型作为干预措施的一部分，以改善 有高进展风险的退伍军人发生肝脏并发症和心血管事件的机会 退伍军人非酒精性脂肪肝诊所的适当护理。