R01CA258193

Patient-Generated Health Data to Predict Childhood Cancer Survivorship Outcomes - Project Summary/Abstract

There are approximately 500,000 childhood cancer survivors in the U.S. today. Childhood cancer survivors are vulnerable to late effects of therapy, including chronic health conditions and premature death. Predicting survivor-specific risk of late effects, discussing how to manage these risks, and offering early preventions and interventions are critical components of survivorship care.

Over 75% of childhood cancer survivors have prevalent symptoms, and constantly poor or worsening symptoms are associated with the onset of medical late effects. However, regular symptom monitoring is uncommon in survivorship or primary care.

The core concept of this R01 grant proposal is to enable regular monitoring of patient-generated health data (PGHD), including symptoms, physical activity, energy expenditure, sleep behavior, and heart rate variability, and utilize these data in predicting survivor-specific risk of late effects to improve survivorship care and outcomes.

The proposed application will enroll 620 adult survivors of childhood cancer from the St. Jude Lifetime Cohort Study who are 5 years post-diagnosis and currently 18 years of age at enrollment to achieve the following 3 specific aims:

Aim 1) Use a mobile health platform to collect dynamic PGHD data over 3 months and use them to develop and validate risk prediction models for future quality-of-life (QOL).

Aim 2) Develop/validate risk prediction models and establish personalized risk prediction scores for other outcomes (unplanned care utilization, physical performance deficits, onset of chronic health conditions) using the same approach as Aim 1.

Aim 3) Create a web-based tool to calculate and report personalized outcome-specific risks and facilitate integration of risk scores into the survivor's patient portal and hospital's electronic health record (EHR).

We have a series of preliminary data to support this R01 grant proposal: A) In a pilot study assessing 20 common symptoms with a mobile health platform, childhood cancer survivors completed 90% of all required evaluations over 3 months; and B) In a prediction analysis from an ongoing cohort of childhood cancer survivors, the inclusion of longitudinal symptom data generated a superior model performance in predicting future QOL (prediction measure, AUC=0.85) compared to the use of only age, sex, and childhood cancer type (AUC=0.63).

Linking through a mobile health platform, we will use a smartphone to collect symptom data, a wrist-worn accelerometer to collect momentary activity/behavioral data, and a finger sensor to collect heart rate variability data. We will predict patient-reported outcomes (poor QOL, unplanned healthcare utilization) and clinically-assessed outcomes (physical performance deficits, onset of chronic health conditions) on the 12th and 24th months after collecting risk factors. We will apply state-of-the-art machine/statistical learning techniques to capture features of dynamic changes in PGHD to predict these outcomes.

We will build a central cancer survivorship platform to integrate predicted risks presented with interpretable scores into a patient portal and EHR, and to inform clinicians and survivors about potential adverse-event risks for risk management/intervention.

St. Jude Children's Research Hospital

NOT APPLICABLE

93.393 - Cancer Cause and Prevention Research

National Cancer Institute (NCI) [HHS - NIH]

Memphis, Tennessee 38105 United States

Single Zip Code

Research Answers to National Cancer Institute's (NCI) Provocative Questions (R01 Clinical Trial Optional) (RFA-CA-20-004)

Amendment Since initial award the End Date has been extended from 06/30/26 to 06/30/27 and the total obligations have increased 378% from $742,807 to $3,551,950.