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

摘要 终末期肾病（ESKD）是一种复杂的疾病，个体具有可变的寿命， 预期，25%在1年内死亡，41%存活至少5年。虽然供应商 认识到病人是不同的-应该是不同的-没有可靠的工具， 预测个体预期寿命，并帮助治疗个体化。相反，供应商 关于如何最好地管理患者的指导方针往往不明确或不完整。为了 为血液透析（HD）患者提供精确护理，迫切需要能够（1） 动态评估医疗决策的预期寿命;（2）识别不同的临床 表型，以加强临床监测和护理规划。我们的核心假设是， 患者生存率和疾病轨迹存在异质性，当已知时， 用于提供更加个性化和有效的护理。通过结合新颖的机器学习 通过HD患者的粒度临床数据进行生存预测的方法，我们将能够 开发必要的分析工具，以支持精确护理。在完成这项工作后， 建议我们将有工具来动态评估病人的预期寿命和见解， ESKD患者的异质性疾病表型。这些工具将允许供应商 做出明智的治疗决定，并为进一步的精确研究奠定基础 优化病人护理

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.