# Personalized dynamic risk-stratification model for childhood cancer survivors

> **NIH NIH U01** · ST. JUDE CHILDREN'S RESEARCH HOSPITAL · 2020 · $137,876

## Abstract

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.

## Key facts

- **NIH application ID:** 10166232
- **Project number:** 3U01CA195547-06S1
- **Recipient organization:** ST. JUDE CHILDREN'S RESEARCH HOSPITAL
- **Principal Investigator:** MELISSA M HUDSON
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $137,876
- **Award type:** 3
- **Project period:** 2020-08-10 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10166232

## Citation

> US National Institutes of Health, RePORTER application 10166232, Personalized dynamic risk-stratification model for childhood cancer survivors (3U01CA195547-06S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10166232. Licensed CC0.

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