Evaluation of clinical and social factors associated with deficit accumulation trajectories in older survivors with early-stage non-small cell lung cancer

Project Summary Aging involves a gradual clinical decline in cognitive and physical function and often the development of various comorbidities. However, significant life events, such as cancer diagnosis and treatment, can potentially accelerate clinical decline and aging. Various individual-level and neighborhood-level social determinants of health (SDOH) can also accelerate aging in cancer survivors, but a comprehensive approach to identifying and intervening on multilevel aging risk factors for cancer survivors is lacking. To investigate multilevel aging risk factors, a reliable measure is needed to quantify aging and monitor dynamic changes in aging-related health status over time. An electronic deficit accumulation index (eDAI) measures aging-related clinical declines over time by counting the accumulation of aging-related deficits using electronic health records (EHRs). However, the eDAI in cancer research has primarily been used at the time of diagnosis to manage comorbidity and reduce treatment toxicity, but it has been understudied in the context of cancer survivorship and aging trajectories. The broad goal of this project is to evaluate dynamic changes in aging-related clinical decline in cancer survivors using eDAI through real-world data analytics, identify multilevel determinants associated with SDOH disparities in clinical decline, and highlight potential interventions for healthy aging. Lung cancer is an ideal disease setting for achieving these goals. Lung cancer is the leading cause of cancer death, with a median age at diagnosis of 71 years. With recent increases in survival due to screening and new treatment paradigms, this disease is becoming a chronic disease of older age. More importantly, while cancer survivors are traditionally defined as people who survive long after treatment completion, many lung cancer patients live for a long time while still undergoing active treatment, highlighting the importance of longitudinal exposures to treatment or other time-varying risk factors on aging. Given that aging is also an evolving process, studying dynamic longitudinal risk factors for aging in lung cancer survivors could serve as a useful model in aging epidemiologic research. The study cohort will comprise >89,000 survivors (age≥65) with early-stage non-small cell lung cancer who are healthy (i.e., including both robust [eDAI<0.2] and pre-clinical decline [eDAI: 0.2- 0.34]) at diagnosis in SEER-Medicare (2013-2017). The primary outcome will be the time to clinical decline (eDAI≥0.35). The aims are (AIM 1) to identify key individual-level risk factors affecting clinical decline, (AIM 2) to investigate neighborhood-level SDOH to develop a comprehensive risk prediction model for clinical decline, and (AIM 3) to conduct real-world validation using an integrated EHR database of academic and community healthcare systems. Completing these research aims and training goals (in real-world data analytics, SDOH disparities, and aging epidemiology) under the strong mentorship team in Stanford’s rich environment will be critical for me to become an independent investigator at the intersection of aging, cancer survivorship, and disparities with key skills for the era of expanding real-world big data and comprehensive disparities research.