Predicting Pediatric Sickle Cell Disease Acute Pain Using Mathematical Models Based on mHealth Data

项目总结 镰状细胞病(SCD)影响着生活在世界各地的2000多万人和大约10万人 生活在美国。SCD患者死亡的风险增加，与健康相关的质量差 生命力强，卫生保健利用率高。疼痛是导致健康状况不佳和增加的主要因素 SCD患者的医疗费用。大多数SCD疼痛发作是意想不到的；导致缺乏 缺乏预防性疼痛管理，阿片类药物和其他保健服务的使用增加，生活质量较差。 预测儿童SCD疼痛的准确数学模型将促进SCD疼痛的发展， 测试和最大化实施干预措施的时机，以提高其有效性；以及 减少阿片类药物的使用；从而最大限度地减少阿片类药物依赖的风险。我们的中心假设是 纳入时变移动健康(MHealth)变量的动态数学模型将增加 预测个体每日儿童SCD疼痛特征的准确性--疼痛的严重程度、发作和加重。 我们还假设，mHealth数据的变化是儿童SCD疼痛变化的重要先兆。 我们的短期目标是开发和测试一个动态数学建模框架，该框架包括 MHealth变量的组合，以确定预测个人日常生活的最佳模型公式 儿童SCD疼痛特征。拟议的研究将利用先前项目的现有数据，重点是 睡眠与SCD疼痛之间的关系--迄今为止，结合生态学的最大规模研究 对患有SCD的青少年的瞬时评估(EMAS)和活动测量措施。之前的分析 这些数据没有考虑关系的动态性质，也没有检查mHealth数据的范围 可用。为了实现这一目标，我们的目标是(1)构建一个由动态数学模型组成的框架 侧重于预测儿科SCD疼痛严重程度的模型，该模型可以利用mHealth的各种组合 变量，以及(2)确定哪些建模框架实例-mHealth数据组合与 该模型-对于预测单个SCD疼痛严重程度模式是有效的。然后(3)我们将使用 选择框架实例来预测儿童SCD疼痛发作和疼痛加重的风险 确定哪些mHealth变量组合在预测这些疼痛特征方面最成功。 具体地说，我们将应用机器学习算法并评估建模的每个实例的能力 用于预测第二天疼痛开始或加重的框架。

PROJECT SUMMARY Sickle cell disease (SCD) affects over 20 million people living worldwide and approximately 100,000 individuals living in the United States. Individuals with SCD are at increased risk of mortality, poor health-related quality of life, and high health care utilization. Pain is the primary factor linked to poor health outcomes and increased medical costs for individuals with SCD. The majority of SCD pain episodes are unanticipated; leading to a lack of prophylactic pain management, increased use of opioids and other health care, and poor quality of life. Accurate mathematical models to predict SCD pain in pediatric patients would facilitate the development, testing, and maximizing of the timing of implementation of interventions to improve their effectiveness, and reduce the use of opioids; thus, minimizing the risk of opioid dependence. Our central hypothesis is that dynamic mathematical models that incorporate time-varying mobile health (mHealth) variables will increase the accuracy of prediction of individual daily pediatric SCD pain features – pain severity, onset, and exacerbations. We also hypothesize that changes in mHealth data are important precursors to changes in pediatric SCD pain. Our short-term goal is to develop and test a dynamic mathematical modeling framework that includes combinations of mHealth variables to identify the best model formulations for predicting individual daily pediatric SCD pain features. The proposed study will leverage existing data from a previous project focused on the relationship between sleep and SCD pain – to date, the largest study that incorporates ecological momentary assessments (EMAs) and actigraphy measures for youth with SCD. The previous analyses of these data did not consider the dynamic nature of the relationships or examine the range of mHealth data available. To accomplish this goal, we aim to (1) construct a framework consisting of a dynamic mathematical model that focuses on predicting pediatric SCD pain severity that can utilize various combinations of mHealth variables, and (2) determine which modeling framework instances – mHealth data combinations coupled with the model – are effective for predicting individual SCD pain severity patterns. Then (3) we will use the framework instances selected to predict risk of pediatric SCD pain onset and pain exacerbations and determine which mHealth variable combinations are most successful at predicting these pain features. Specifically, we will apply machine learning algorithms and assess the ability of each instance of the modeling framework to predict pain onset or exacerbation the next day.