||1R01CA258193-01 Interpret this number
||St. Jude Children'S Research Hospital
||Patient-Generated Health Data to Predict Childhood Cancer Survivorship Outcomes
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 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;
and 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 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.