血液透析中的预测分析：为 ESKD 患者提供精准护理-猫眼课题宝

血液透析中的预测分析：为 ESKD 患者提供精准护理

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

项目状态：

未结题

起止时间：

2020-07-01 至 2025-04-30

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

项目成果

Benjamin Alan Goldstein的其他基金

相似基金

相关文献

中文摘要

翻译

点击翻译按钮获取中文摘要

英文摘要

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.

期刊论文(10)

专著(0)

科研奖励(0)

会议论文