Patient-Generated Health Data to Predict Childhood Cancer Survivorship Outcomes

项目总结/摘要 今天，美国大约有50万儿童癌症幸存者。儿童癌症 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。 预测生存者特有的晚期效应风险，讨论如何管理这些风险，并提供早期 预防和干预是幸存者护理的关键组成部分。超过75%的儿童癌症 幸存者有普遍的症状，持续的不良或恶化的症状与发病有关。 医学晚期效应然而，定期症状监测是罕见的生存或初级保健。的 R 01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， 这些数据可用于预测生存者特异性晚期效应风险，以改善生存护理和结局。 拟议的申请将招募620名儿童癌症成年幸存者从圣裘德终身 诊断后≥5年且入组时年龄≥18岁的队列研究， 以下3个具体目标：目标1）使用移动的健康平台收集3个月内的动态PGHD数据， 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 风险预测模型，并为其他结果（计划外护理）建立个性化的风险预测评分 使用与目标1相同的方法; 和目标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网站和医院的电子健康记录（EHR）中。 我们有一系列的初步数据来支持R 01拨款提案：a）在一项评估20 常见症状与一个移动的健康平台，儿童癌症幸存者完成了90%的所有要求 3个月内的评估;和B）在来自正在进行的儿童癌症幸存者队列的预测分析中， 纵向症状数据的纳入产生了预测未来QOL的上级模型性能 与仅使用年龄、性别和儿童癌症类型（AUC=0.63）相比，预测测量（AUC=0.85）。 通过移动的健康平台链接，我们将使用智能手机收集症状数据， 用于收集瞬时活动/行为数据的加速度计，以及用于收集心率变异性的手指传感器 数据我们将预测患者报告的结局（不良QOL，计划外医疗保健利用）和临床- 12日和24日的评估结果（体能缺陷、慢性健康状况发作） 收集风险因素后的几个月。我们将应用最先进的机器/统计学习技术， 捕获PGHD动态变化的特征，以预测这些结果。我们将建立一个中央癌症 生存率平台将预测的风险与可解释的评分整合到患者门户中， EHR，并告知临床医生和幸存者有关风险管理/干预的潜在不良事件风险。

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.