Personalized Life Expectancy to Encourage High Value Prostate Cancer Care

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As an integrated system with access to granular and longitudinal data, the Veterans Health administration (VHA) is ideally positioned to advance the understanding of the life expectancy, to improve prostate cancer screening strategies, and to generate models of personalized risk-adjusted life expectancy estimates to provide critical information to inform prostate cancer treatment decisions. Veterans receiving care in the Veterans Health Administration may have higher prostate cancer risk due to a family history, race, or exposure to toxins such as Agent Orange and burn pits. Prostate cancer presents a clinical, health policy, and population health challenge. Prostate cancer is the most common male cancer, presents in older men that may have additional medical conditions, and often follows an indolent course. It is estimated that 60% of all prostate cancer cases represent an “overdiagnosis” of clinically insignificant tumors. For prostate cancer patients, “overdiagnosis” refers to the diagnosis of a disease process that would otherwise not go on to cause symptoms or death. Similarly, “overtreatment” refers to the treatment of prostate cancers that would not otherwise go on to cause symptoms or death. Our objective is to leverage the power of the standardized electronic health record in the VHA to generate personalized risk-adjusted life expectancy estimates. We will use these estimates to provide critical information to inform prostate cancer screening and treatment medical decision-making. These efforts have the potential to deliver higher quality prostate cancer care, by treating patients most likely to benefit, and while avoiding futile treatment and minimizing treatment-related side effects. We will work to develop life expectancy estimates for all male veterans receiving care in the VHA and evaluate how a diagnosis of prostate cancer may modify these estimates. (Aim 1) Next, we use several approaches to generate personalized life expectancy estimates for patients with a diagnosis of prostate cancer. These estimates will use data from the electronic health record including age, race/ethnicity, prior medical claims, disease severity, exposure, health habits, pharmacy, and laboratory data for military beneficiaries receiving care in the VHA. (Aim 2) These estimates will include efforts to use machine learning approaches to generate the best-fitting model of overall survival. Finally, we will estimate the overdiagnosis and overtreatment of prostate cancer in the VHA using the general and personalized life expectancy estimates. (Aim 3)