Understanding the impact of survivorship care plans on health outcomes in cancer survivors

PROJECT SUMMARY Cancer survivors, even when cured from their cancer, are at an increased risk of excess mortality, attributable to higher rates of treatment-related health conditions, most notably secondary malignancies, cardiovascular disease, stroke and late health complications. Growing recognition of these health conditions and their resultant morbidity and mortality has led to the development of standardized, cancer survivor-specific screening and surveillance recommendations, with the goals of risk reduction, early detection, reduced mortality, and improved quality of life. Cancer survivorship care plans (SCPs), which outline prior cancer treatments, provide a comprehensive plan to address potential long-term effects. Despite the widespread implementation of SCPs into general practice, controversy exists on whether SCPs improve screening and surveillance practices, or reduce mortality. Given the resource burden the creation of SCPs places on the healthcare system and the lack of available efficacy data for their use, it is critical that key measurable outcomes, specifically mortality, be investigated to support their ongoing use. The proposal’s objective is to investigate the impact of SCPs on reducing cancer mortality among adult cancer survivors. The long-term goal is to optimize survivors’ health, minimizing chronic health conditions and improving overall mortality in cancer survivors. Based on our preliminary data, our central hypothesis is that SCPs when applied to the general population will not improve mortality. However, enhanced SCPs (SCP+) when applied to high risk populations will improve outcomes and mortality. We will test our central hypothesis and attain our objective via the following specific aims: Specific aim 1a: Quantify the effectiveness of receiving a SCP (versus no SCP) in reducing cancer mortality using institutional 2018-2023 data. Specific aim 1b: Quantify the effectiveness of SCP+ and its contribution to mortality in AYA patients, aged 15-39 years (versus no SCP+), using institutional data 2011-2023. Specific aim 2: Using tree-based and random forest causal survival machine learning (ML) methods, identify heterogeneous treatment effects of SCP and SCP+ and characterize those that receive maximum survival benefit relative to all others. The proposed work is innovative in that it compares SCP and SCP+ to examine the role of SCPs on mortality, integrates multiple data sources, and uses a novel machine learning technology to identify heterogeneity in group differences in survival. Our research is poised to make a significant impact by delving into crucial socioeconomic and geographic disparities in care delivery, optimizing the effectiveness of SCPs in minimizing mortality rates.