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

基本信息

  • 批准号:
    10177897
  • 负责人:
  • 金额:
    --
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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方法 退伍军人中的肝病(NAFLD)。拟议的分析还将审查种族/民族差异 NAFLD 的诊断、治疗和结果,目的是确定罹患 NAFLD 风险最高的患者群体 进展为肝硬化和肝硬化相关并发症。本研究的长期目标是 这项试点研究将促进综合多学科的发展和有效瞄准 治疗算法以及简单、适合文化且具有成本效益的干预措施,以遏制 NAFLD 及其并发症在退伍军人中的流行。 背景:NAFLD 是一个严重且日益严重的健康问题,与 2 型肥胖密切相关 糖尿病(T2DM)、高血压和血脂异常。在 VA,NAFLD 患病率 估计高达46%。 NAFLD 的患病率因人群而异 研究和使用的测试。在达拉斯心脏研究中,估计超过 30% 的患者患有 通过 MR 波谱检测 NAFLD。重要的是,研究人员发现 NAFLD 的患病率最高 发生在西班牙裔(58%)和 T2DM 患者(超过 70%)中。西班牙裔人口比例更高 与非非酒精性脂肪肝 (NAFLD) 发生率相比,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 因素(即 T2DM、高血压、高脂血症、肥胖、吸烟等)。饮食驱动的生活方式改变 干预和运动是诱导和维持体重减轻、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 结果。 VA NAFLD 诊所提供强化减肥,包括营养、运动、 行为学、VA 批准的药物(例如安非他酮/纳尔曲、Lorcascerin)和减肥手术。 因此,识别那些有进展为相关不良结果的高风险的患者非常重要 患有晚期 NAFLD 并在 VA NAFLD 诊所提供治疗。 目标:在这个为期 1 年的试点中,我们建议使用 VA NAFLD 团队策划的队列(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 已接受肝活检。数据集将得到扩充,包括 8 年前和 1-1 年前的医疗记录 活检后一年。我们将使用聚类和机器学习预测分析方法来识别 发生肝硬化、肝硬化相关并发症和心血管事件风险较高的患者 对种族和民族差异进行重点分析。 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、实验室值和合并症来识别 NAFLD 患者 VINCI 系统中的患者记录。为了识别快速进展的 NAFLD 患者,我们将聚类 纤维化风险评分趋势数据。我们将定制 NAFLD 识别和进展的方法, 通过机器学习分析来增强它。我们试点的结果将是 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 进展为肝硬化、肝硬化并发症和心血管事件的风险更高,因此, 从临床干预中受益,主动降低风险。下一步是后续研究 使用在试点中得出的预测高风险患者的模型作为干预措施的一部分来改善 患有肝脏并发症和心血管事件的高风险退伍军人可以获得 VA NAFLD 诊所提供适当的护理。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Lewis James Frey其他文献

Lewis James Frey的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Lewis James Frey', 18)}}的其他基金

Data-Driven Methods to Identify Social Determinants of Health
识别健康社会决定因素的数据驱动方法
  • 批准号:
    10314508
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:
Data-Driven Methods to Identify Social Determinants of Health
识别健康社会决定因素的数据驱动方法
  • 批准号:
    10491762
  • 财政年份:
    2021
  • 资助金额:
    --
  • 项目类别:
Techniques to Integrate Disparate Data: Clinical Personalized Pragmatic Predictio
整合不同数据的技术:临床个性化实用预测
  • 批准号:
    8599828
  • 财政年份:
    2013
  • 资助金额:
    --
  • 项目类别:
BIGDATA: Mid-Scale: DA: Techniques to Integrate Disparate Data: Clinical Personalized Pragmatic Predictions of Outcomes (C3PO)
BIGDATA:中等规模:DA:整合不同数据的技术:临床个性化实用结果预测 (C3PO)
  • 批准号:
    8914880
  • 财政年份:
    2013
  • 资助金额:
    --
  • 项目类别:
BIGDATA: Mid-Scale: DA: Techniques to Integrate Disparate Data: Clinical Personalized Pragmatic Predictions of Outcomes (C3PO)
BIGDATA:中等规模:DA:整合不同数据的技术:临床个性化实用结果预测 (C3PO)
  • 批准号:
    8840825
  • 财政年份:
    2013
  • 资助金额:
    --
  • 项目类别:

相似国自然基金

地表与大气层顶短波辐射多分量一体化遥感反演算法研究
  • 批准号:
    42371342
  • 批准年份:
    2023
  • 资助金额:
    52 万元
  • 项目类别:
    面上项目
高速铁路柔性列车运行图集成优化模型及对偶分解算法
  • 批准号:
    72361020
  • 批准年份:
    2023
  • 资助金额:
    27 万元
  • 项目类别:
    地区科学基金项目
随机密度泛函理论的算法设计和分析
  • 批准号:
    12371431
  • 批准年份:
    2023
  • 资助金额:
    43.5 万元
  • 项目类别:
    面上项目
基于全息交通数据的高速公路大型货车运行风险识别算法及主动干预方法研究
  • 批准号:
    52372329
  • 批准年份:
    2023
  • 资助金额:
    49 万元
  • 项目类别:
    面上项目
高效非完全信息对抗性团队博弈求解算法研究
  • 批准号:
    62376073
  • 批准年份:
    2023
  • 资助金额:
    51 万元
  • 项目类别:
    面上项目

相似海外基金

Building predictive algorithms to identify resilience and resistance to Alzheimer's disease
构建预测算法来识别对阿尔茨海默病的恢复力和抵抗力
  • 批准号:
    10659007
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
Studying the Genetics of Aging, Behavioral, and Social Phenotypes in Diverse Populations
研究不同人群的衰老、行为和社会表型的遗传学
  • 批准号:
    10638152
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
Moving Beyond the Individual- A Data-driven Approach to Improving the Evidence on the Role of Community and Societal Determinants of HIV among Adolescent Girls and Young Women in Sub-Saharan Africa
超越个人——采用数据驱动的方法来改善关于艾滋病毒在撒哈拉以南非洲地区少女和年轻妇女中的社区和社会决定因素的作用的证据
  • 批准号:
    10619319
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
ARISE (Achieving Routine Intervention and Screening for Emotional health)
ARISE(实现情绪健康的常规干预和筛查)
  • 批准号:
    10655877
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
Artificial Intelligence and Counterfactually Actionable Responses to End HIV (AI-CARE-HIV)
人工智能和反事实可行的终结艾滋病毒应对措施 (AI-CARE-HIV)
  • 批准号:
    10699171
  • 财政年份:
    2023
  • 资助金额:
    --
  • 项目类别:
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了