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

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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.