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.

项目概要/摘要目前美国约有 500,000 名儿童癌症幸存者。幸存者很容易受到治疗的后期影响，包括慢性健康问题和过早死亡。预测幸存者特定的后期影响风险，讨论如何管理这些风险，并尽早提供建议预防和干预是超过 75% 的儿童癌症生存护理的重要组成部分。幸存者有普遍的症状，并且症状持续恶化或恶化与疾病的发作有关然而，定期症状监测在生存或初级保健中并不常见。这项 R01 拨款提案的核心概念是定期监测患者生成的健康数据 (PGHD)，包括症状、体力活动、能量消耗、睡眠行为和心率变异性以及利用率这些数据可预测幸存者特定的后期影响风险，以改善幸存者护理和结果。拟议的申请将招募 St. Jude Lifetime 的 620 名儿童期癌症成年幸存者诊断后≥5年且入组时当前年龄≥18岁的队列研究，以达到以下 3 个具体目标：目标 1) 使用移动健康平台收集 3 个月内的动态 PGHD 数据，以及使用它们来开发和验证未来生活质量 (QOL) 的风险预测模型目标 2) 开发/验证建立风险预测模型和其他结果的个性化风险预测评分（计划外护理使用与目标 1 相同的方法；目标 3) 创建一个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幸存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这项 R01 拨款提案：a) 在一项评估 20 的试点研究中通过移动健康平台，儿童癌症幸存者完成了所有要求的 90% 超过 3 个月的评估；以及 b) 对正在进行的儿童癌症幸存者队列的预测分析，纳入纵向症状数据在预测未来生活质量方面产生了卓越的模型性能（预测指标，AUC=0.85）与仅使用年龄、性别和儿童癌症类型（AUC=0.63）相比。通过移动健康平台连接，我们将使用智能手机收集症状数据，加速度计用于收集瞬时活动/行为数据，手指传感器用于收集心率变异性我们将预测患者报告的结果（不良的生活质量、计划外的医疗保健利用）和临床- 12 日和 24 日评估结果（身体表现缺陷、慢性健康状况发作）收集风险因素几个月后，我们将应用最先进的机器/统计学习技术。我们将捕捉 PGHD 动态变化的特征来预测这些结果。生存平台将预测风险与可解释的分数集成到患者门户中电子病历，并告知参议员和幸存者有关风险管理/干预的潜在不良事件风险。