Parkinson’s disease (PD) is a progressive neurodegenerative disease that affects more than one million people
in North America. While symptomatic disease is diagnosed when patients develop the classic motor features,
many non-motor symptoms predate the motor symptoms, including constipation, sleep disturbances, poor sense
of smell, depression, and anxiety. This period of the disease has been termed the prodromal period. Although
medical treatments improve symptoms, there are currently no proven therapy to slow disease progression,
possibly due to failure to diagnose prior to irreversible brain damage. As a result, developing methods to
identify PD patients earlier, during the prodromal period, are critical to begin to find medications that modify
the inexorable disease progression. We previously developed a computer model that identifies people who are
going to develop PD, during the prodromal period, by using only medical diagnosis and procedure codes
obtained from the Center for Medicare and Medicaid Studies (CMS). This computer model used a diagnosis
system [International Classification of Disease version 9 (ICD9)] that was changed in 2015 to International
Classification of Disease version 10 (ICD10). This system has four times as many diagnoses so our computer
model must be redeveloped using this new system prior to being implemented. We will also use Medicare Part
D prescription drug data to identify medications associated with a lower risk of developing PD. This proposal
addresses the Parkinson’s Research Program focus area of using digital health technology, specifically digital
medical claims data, to develop a predictive model for early identification of PD. Forbes lists big data analytics
as the digital technology with the greatest impact on healthcare in 2019. As such, this research proposal
represents a timely and highly responsive application in response to the Department of Defense digital health
technology focus area. The studies outlined in this proposal are an important first step in developing a
prodromal PD cohort using an ICD10-based PD predictive model. Upon completion of these studies, the final
computer model we develop will be ready to be implemented using contemporary Medicare claims data and
will allow researchers to identify those Medicare recipients who are at highest risk of developing PD in the next
three years. Developing a computer model from digital medical claims data could potentially impact all patients
with PD. Early identification could result in early treatment when patients are beginning to experience disease
complications, such as falls, fractures, or traumatic brain injuries. This would have an immediate impact on
patients with PD. Clinical use of this type of computer model would have little risk to patients since they are all
Medicare age and are guaranteed insurance coverage. In addition, researchers have been trying to identify
people with PD during the prodromal disease phase, in order to study the effect of medications that could
potentially slow disease progression. So far, these efforts have only been modestly successful. This study has
the potential to be a major advance in these efforts. Finally, any medications we find that are associated with a
lower risk of developing PD may represent potential disease slowing medical therapies. |