Grant Number:	3U01CA195547-06S1 Interpret this number
Primary Investigator:	Hudson, Melissa
Organization:	St. Jude Children'S Research Hospital
Project Title:	Personalized Dynamic Risk-Stratification Model for Childhood Cancer Survivors
Fiscal Year:	2020

PROJECT SUMMARY/ABSTRACT This application is being submitted in response to the Notice of Special Interest (NOSI) identified as NOT-CA-20-038. Cardiovascular complications have emerged as the leading cause of death among long-term childhood cancer survivors. It has been well-studied that these survivors have 15- and 7-fold of increased risk of developing congestive heart failure and premature death due to cardiac events compared to the general population. Since a long latency period often occurs before the clinically evident disease, the ability to predict is critical to expand the impact of survivor-based research to inform the future design and treatment regimes. To provide proper follow-up care for survivors, it is imperative to conduct patient-centered risk predictions that incorporate patient heterogeneity in a personalized and dynamic manner. While there is a growing body of literature on risk-stratification methods, most models only utilize baseline factors associated with the disease outcome. Cardiovascular burden is well-documented among childhood cancer survivor with cardiac risk factors increasing over time. In addition, a range of neighborhood and socioeconomic factors can affect risk for specific outcomes such as obesity. Leveraging the resources of the St. Jude Lifetime Cohort study (SJLIFE), we aim to develop a novel statistical model for personalized dynamic cancer risk prediction to improve the current cancer risk classification paradigm. Given that longitudinal information is routinely measured and documented for SJLIFE survivors, it is optimal to make full use of available longitudinal data to improve risk classification and guide timely risk-based prevention interventions to reduce the burden of cardiac diseases. The proposed method is appealing for its conceptual simplicity and efficiency for conducting dynamic prediction incorporating comprehensive longitudinal information, where risk prediction/ stratification can be updated as new observations are gathered to reflect the patient's latest health and behavior status. The proposed solutions will be accessible through open-source R software that we will make publicly available, and will be applied to data from childhood cancer survivors participating in SJLIFE.





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