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：
10178979
负责人：
I-Chan Huang
金额：
$ 74.28万
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/10178979
关键词：
18 year old Accelerometer Adult Age Applications Grants Attention Behavioral Behavioral Genetics Biometry Cancer Survivorship Caring Cellular Phone Cessation of life Chronic Clinical Clinical Management Cohort Studies Consultations Data Data Collection Devices Diagnosis Dose Early Intervention Electronic Health Record Emergency department visit Energy Metabolism Enrollment Environment Evaluation Fingers Future 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 Health 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 outcomes care providers childhood cancer survivor 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 premature preventive intervention risk prediction risk prediction model sensor sex sleep behavior sociodemographic factors sociodemographics statistical and machine learning support tools survivorship tool 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.

项目摘要/摘要今天，美国大约有500,000个儿童癌症存活。儿童癌幸存者容易受到治疗的后期影响，包括慢性健康状况和过早死亡。预测特定于延迟影响的冲浪特定风险，讨论如何管理这些风险并提早提供预防和干预是生存护理的关键组成部分。超过75％的儿童癌症幸存者患有普遍的症状，并且症状不断地或恶化症状与发作有关医疗迟到的效果。但是，定期症状监测在生存或初级保健方面并不常见。这该R01赠款提案的核心概念是实现对患者生成的健康数据（PGHD）的定期监控，包括症状，体育锻炼，能量消耗，睡眠行为和心率变异性，并利用这些数据在预测幸存者特定的迟到效应风险方面，以改善生存护理和结果。拟议的申请将从圣裘德一生中招募620个儿童癌症的成人生存诊断后≥5岁且目前≥18岁的队列研究以实现以下3个具体目标：目标1）使用移动健康平台在3个月内收集动态PGHD数据，然后使用它们来开发和验证未来质量质量（QOL）的风险预测模型；目标2）开发/验证风险预测模型并为其他结果建立个性化的风险预测分数（计划外护理利用，身体性能不足，慢性健康状况的发作）使用与AIM 1相同的方法；目标3）创建基于网络的工具来计算和报告个性化的特定结果风险，并促进将风险评分整合到冲浪者的患者门户和医院的电子健康记录（EHR）中。我们有一系列初步数据来支持此R01赠款提案：a）在试点研究评估中20 带有移动健康平台的常见符号，儿童癌症的存活量完成了所有所需的一切的90％评估3个月； b）在持续的儿童癌症存活群体的预测分析中，纵向症状数据的包含在预测未来的质量方面产生了卓越的模型性能（预测度量，AUC = 0.85）与仅使用年龄，性别和儿童癌症类型（AUC = 0.63）相比。通过移动健康平台链接，我们将使用智能手机收集症状数据加速度计收集瞬时活动/行为数据，以及一个手指传感器来收集心率变异性数据。我们将预测患者报告的结果（QoL差，计划外的医疗保健利用率）和临床上 - 评估结果（身体表现不足，慢性健康状况发作）在12日和24日收集危险因素几个月。我们将把最先进的机器/统计学习技术应用于捕获PGHD中动态变化的特征，以预测这些结果。我们将建立一个中央癌生存平台，将可解释的分数带入患者门户和 EHR，并告知临床医生和生存有关潜在的风险管理/干预风险的不利风险。