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

开发预测模型以改善帕金森病的临终预测和临终关怀转诊

• 批准号: 10524354
• 负责人: BENZI M KLUGER
• 金额: $ 23.1万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10524354
• 关键词: Adult; Affect; Algorithms; American; Amyotrophic Lateral Sclerosis; Area; Award; Breathing; Cause of Death; Cessation of life; Characteristics; Clinical; Communities; Complication; Data; Data Set; Decision Making; Dementia; Development; Diagnosis; Enrollment; Equilibrium; Evaluation; Family Caregiver; Foundations; Future; Goals; Guidelines; Hospice Care; Hospitals; Impairment; Individual; Infection; Intervention; Logistic Regressions; Machine Learning; Measurement; Measures; Medical; Medicare; Methodology; Mind; Modeling; Neurodegenerative Disorders; Nursing Homes; Nutritional status; Outcome; Parkinson Disease; Patient Care; Patients; Performance; Persons; Population; Predictive Value; Probability; Procedures; Prognosis; Progressive Disease; Prospective Studies; Quality of life; ROC Curve; Reproducibility; Research; Research Project Grants; Risk; Sensitivity and Specificity; Series; Services; Structure; Testing; Time; United States; Validation; Work; advanced dementia; base; beneficiary; clinical database; cohort; cost; end of life; evidence base; falls; family support; feature selection; functional status; hospice environment; human old age (65+); improved; indexing; innovation; machine learning algorithm; machine learning model; meetings; model building; mortality; multilayer perceptron; nervous system disorder; predictive modeling; predictive tools; prognostic model; prognostic tool; prognostication; random forest; secondary analysis; support vector machine; tool

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.

项目摘要/摘要 帕金森氏病（PD）是第二大最常见的神经退行性疾病，影响约1.5 百万美国人，是美国第14大死亡原因。目前没有医疗保险 针对PD的临终关怀转诊指南，我们认为缺乏适当的模型或指南 临床医生是导致院内死亡率不成比例和低率的主要因素之一 该人群中的临终关怀转诊率。我们的长期目标是改善人们的临终结果 与PD及其家庭护理人员一起生活，包括提高临终关怀的转诊率和持续时间 临终关怀。我们假设当前可用的临终关怀指南未被充分利用，即使 使用的是，明显低估了6个月的死亡率，并错过了死亡率的重要预测指标 人口。我们进一步假设最小数据集中当前可用的数据（MDS - 联邦政府 可以使用授权的护士家庭居民临床数据库和国家死亡指数（NDI）来发展 一个更准确的预测模型。值得注意的是，Mitchell等。利用类似的痴呆症方法来发展 高级痴呆预后工具（Adept），这一风险评分比Medicare更准确 痴呆指南。为了开发有效的提示模型和风险评分，我们将遵循 Steyerberg概述的模型建设和验证结构，其中包括用于 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。我们将实现该提议的目标 通过两个具体目标：1）在护士中建立一个6个月死亡率的预测模型 使用最低数据集和国家死亡指数的PD房屋； 2）比较 我们针对当前可用的Medicare临终关怀指南的PD特异性预测模型。方法是 创新性是第一个将这种数据驱动方法应用于PD寿命终止编程的研究，并使用状态 艺术机器学习和建模方法以及评估和解释工具，以实现 临床上有用的模型。拟议的研究很重要，因为该提案的结果将创建第一个 临终关怀转诊的循证和PD特定风险评分，将提供首次系统分析 该人群中当前可用的临终关怀准则的准确性和使用。我们预计结果 这项工作将对患者护理产生快速而直接的影响，也将为未来创造基础 改善该人群中临终关怀和预后的研究以及未来的基础 在护士家庭和社区居民队列中的前瞻性研究。