Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKD

血液透析中的预测分析：为 ESKD 患者提供精准护理

基本信息

批准号：
10605248
负责人：
Benjamin Alan Goldstein
金额：
$ 53.61万
依托单位：
DUKE UNIVERSITY
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：
2020-07-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10605248
关键词：
Biological Markers Cardiac Caring Cessation of life Chronic Classification Clinical Clinical Data Clinical Management Clinical Research Collaborations Companions Complex Coupled Coupling Data Decision Making Dialysis procedure Disease Electronic Health Record End stage renal failure Event Excision Functional disorder Future Guidelines Health Hemodialysis Heterogeneity Hospitalization Hypertension Hypotension Individual Infection Laboratories Left Life Life Expectancy Longevity Machine Learning Malnutrition Medical Methods Modeling Monitor Nature Outcome Patient Care Patients Persons Phenotype Predictive Analytics Preventive Preventive care Prognosis Provider Quality of Life Assessment Research Risk Risk Assessment System Testing Time Translations Transplantation Update Weight Gain Work analytical tool associated symptom clinical care clinical decision-making clinical phenotype deep learning disease phenotype experience hemodynamics individual patient innovation insight learning strategy member mortality novel personalized care personalized decision predictive tools prospective survival prediction survivorship tool usability web-based tool

项目摘要

ABSTRACT End stage kidney disease (ESKD) is a complex disease with individuals having variable life- expectancies, with 25% dying within 1 year and 41% surviving at least 5 years. While providers recognize that patients are different – and ought to be differently – there are no tools to reliably forecast individual life expectancy and aid in treatment individualization. Instead, providers are left with often unclear or incomplete guidelines on how best to manage patients. In order to provide precision care for patients on hemodialysis (HD), there is a critical need to be able to (1) dynamically assess life expectancy for medical decision-making; and (2) identify distinct clinical phenotypes to enhance clinical monitoring and care planning. Our central hypothesis is that there is heterogeneity in patient survivorship and disease trajectory that, when known, can be used to provide more personalized and effective care. By coupling novel machine learning approaches for survival prediction with granular clinical data on HD patients, we will be able to develop the analytic tools necessary to support precision care. At the completion of this proposal we will have tools to dynamically assess a patient's life expectancy and insights into heterogeneous disease phenotypes for patients with ESKD. These tools will allow providers to make informed treatment decisions as well as lay the groundwork for further precision research into optimized patient care.

抽象的终末期肾病 (ESKD) 是一种复杂的疾病，个体的寿命各不相同。 25% 的人在 1 年内死亡，41% 的人至少存活 5 年。认识到患者是不同的——而且应该是不同的——没有可靠的工具相反，提供者是预测个人预期寿命并帮助个体化治疗。关于如何最好地管理患者的指南往往不明确或不完整。为血液透析 (HD) 患者提供精准护理，迫切需要能够 (1) 动态评估预期寿命以做出医疗决策；(2) 识别不同的临床我们的中心假设是，加强临床监测和护理计划。患者生存率和疾病轨迹存在异质性，一旦已知，就可以通过结合新颖的机器学习来提供更加个性化和有效的护理。通过 HD 患者的详细临床数据进行生存预测的方法，我们将能够完成此任务后，开发支持精准护理所需的分析工具。建议我们将拥有工具来动态评估患者的预期寿命并深入了解这些工具将使提供者能够了解 ESKD 患者的异质疾病表型。做出明智的治疗决定并为进一步的精准研究奠定基础优化患者护理。