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Patient-Generated Health Data to Predict Childhood Cancer Survivorship Outcomes

患者生成的健康数据可预测儿童癌症生存结果

基本信息

批准号：
10445095
负责人：
I-Chan Huang
金额：
$ 72.74万
依托单位：
ST. JUDE CHILDREN'S RESEARCH HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-05 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10445095
关键词：
18 year old Accelerometer Adult Age Applications Grants Attention Behavioral Biometry Cancer Survivorship Caring Cellular Phone Cessation of life Chronic Clinical Clinical Management Clinical assessments Cohort Studies Consultations Data Data Collection Devices Diagnosis Dose Early Intervention Education Electronic Health Record Eligibility Determination Emergency department visit Energy Metabolism Enrollment Environment Evaluation Fingers Future Genetic Goals Health Health behavior Hospitalization Hospitals Income Intervention Late Effects Link Malignant Childhood Neoplasm Malignant Neoplasms Measures Medical Modeling Monitor Outcome Patient Monitoring Patient Outcomes Assessments Patients Pattern Performance Physical Performance Physical activity Pilot Projects Population Prevention Primary Care Process Qualitative Methods Quality of life Reporting Risk Risk Factors Risk Management Saint Jude Children&apos s Research Hospital Series Severities Specific qualifier value Survivors Symptoms Techniques Testing Training Validation Wrist actigraphy adverse event risk adverse outcome associated symptom behavioral health cancer diagnosis cancer therapy cancer type care providers childhood cancer survivor clinical outcome assessment cohort common symptom dashboard health care service utilization health data heart rate variability improved learning strategy mHealth outcome prediction patient portal patient variability personalized risk prediction predictive tools premature preventive intervention risk prediction risk prediction model sensor sex sleep behavior sociodemographic factors sociodemographics statistical learning support tools survivorship user-friendly web-based tool

项目摘要

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 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.

项目总结/文摘