Developing a Prediction Model to Improve End‐of‐Life Prognostication and Hospice Referral in Parkinson's Disease

项目摘要/摘要 帕金森病(PD)是第二种最常见的神经退行性疾病，影响约1.5 在美国，是第14位主要死因。目前没有医疗保险 专门针对PD的临终关怀转诊指南，我们认为缺乏适当的模式或指南 临床医生是导致住院死亡率不成比例高和低的主要因素之一 这一人群中临终关怀转诊的比率。我们的长期目标是改善人们的临终结局 与PD及其家庭照顾者生活在一起，包括提高临终关怀转诊率和持续时间 临终关怀用途。我们假设，目前可用的临终关怀指南没有得到充分利用，即使在 使用，严重低估了6个月的死亡率，并错过了死亡率的重要预测因素 人口。我们进一步假设当前在最小数据集(MDS-a联邦)中可用的数据 养老院居民的强制临床数据库)和国家死亡指数(NDI)可用于开发 一个更准确的预测模型。值得注意的是，米切尔等人。在痴呆症中使用了类似的方法来开发 高级痴呆症预测工具(ADEPT)，一种被证明比联邦医疗保险更准确的风险评分 痴呆症指南。为了开发有效的预测模型和风险评分，我们将遵循以下原则 Steyerberg概述的模型构建和验证结构，其中包括 考虑缺失值、模型规范、参数估计、性能测量、 可重复性、可推广性和临床实用性。我们将实现这项建议的目标 通过两个具体目标：1)建立护理人员6个月死亡率的预测模型 使用最小数据集和国家死亡指数对患有帕金森病的家庭进行比较；以及2)比较 我们的PD特定预测模型适用于当前可用的联邦医疗保险临终关怀指南。方法是 作为第一个将这种数据驱动方法应用于PD寿命结束预测和使用状态的研究，具有创新性 的艺术机器学习和建模方法，以及评估和解释工具，以实现 具有临床实用价值的模型。这项拟议的研究意义重大，因为这项提议的结果将创建第一个 为临终关怀转诊提供基于证据的和特定于PD的风险评分，并将提供首次系统分析 目前可用的临终关怀指南在该人群中的准确性和使用情况。我们预计结果会是 这项工作将对患者护理产生快速和直接的影响，并将为未来奠定基础 为改善这一人群的临终关怀转诊和预测以及为未来奠定基础的研究 疗养院和社区居所队列中的前瞻性研究。

Project Summary/Abstract Parkinson's disease (PD) is the second most common neurodegenerative illness, affecting approximately 1.5 million Americans, and is the 14th leading cause of death in the United States. There are currently no Medicare Guidelines for hospice referral specific for PD and we believe the lack of appropriate models or guidelines for clinicians is one of the prime factors leading to the disproportionately high rates of in-hospital deaths and low rates of hospice referrals in this population. Our long-term goal is to improve end-of-life outcomes for persons living with PD and their family caregivers, including improving the rates of hospice referrals and duration of hospice use. We hypothesize that currently available hospice guidelines are underutilized and that, even when used, significantly underestimate 6-month mortality and miss important predictors of mortality in this population. We further hypothesize that data currently available in the Minimal Data Set (MDS – a federally mandated clinical database of nursing home residents) and National Death Index (NDI) can be used to develop a more accurate prediction model. Notably, Mitchell et al. utilized a similar approach in dementia to develop the Advanced Dementia Prognostic Tool (ADEPT), a risk score demonstrated to be more accurate than Medicare Guidelines for dementia. Toward developing a valid prognostication model and risk score, we will follow a model building and validation structure outlined by Steyerberg that includes statistical methodology for the consideration of missing values, model specification, parameter estimation, performance measurement, reproducibility, generalizability, and clinical usefulness. We will accomplish the objectives of this proposal through two Specific Aims: 1) Develop a predictive model of 6-month mortality in persons living in nursing homes with PD using the Minimum Data Set and the National Death Index; and 2) Compare the accuracy of our PD-specific prediction model to currently available Medicare Hospice Guidelines. The approach is innovative as the first study to apply this data-driven approach to PD end-of-life prognostication and uses state of the art machine learning and modeling approaches, and evaluation and interpretation tools, to achieve a clinically useful model. The proposed research is significant as the results of this proposal will create the first evidence-based and PD-specific risk scores for hospice referral and will provide the first systematic analysis of the accuracy and use of currently available hospice guidelines in this population. We anticipate that the results of this work will have a rapid and direct impact on patient care and will also create a foundation for future studies to improve hospice referrals and prognostication in this population as well as a foundation for future prospective studies in both nursing home and community-dwelling cohorts.