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

项目摘要 镰状细胞病（SCD）影响着全世界2000多万人，约10万人 生活在美国患有SCD的个体死亡风险增加，与健康相关的质量差， 寿命长，保健利用率高。疼痛是与不良健康结果相关的主要因素， SCD患者的医疗费用。大多数SCD疼痛发作是不可预期的;导致缺乏 预防性疼痛管理，阿片类药物和其他医疗保健的使用增加，生活质量差。 准确的数学模型来预测儿童患者的SCD疼痛将促进发展， 测试和最大限度地把握实施干预措施的时机，以提高其有效性， 减少类阿片的使用;从而最大限度地减少类阿片依赖的风险。我们的核心假设是 包含随时间变化的移动的健康（mHealth）变量的动态数学模型将增加 预测个体每日儿科SCD疼痛特征的准确性-疼痛严重程度、发作和加重。 我们还假设mHealth数据的变化是儿科SCD疼痛变化的重要前兆。 我们的短期目标是开发和测试一个动态数学建模框架，包括 mHealth变量的组合，以确定用于预测个人每日 儿科SCD疼痛特征。拟议的研究将利用以前一个项目的现有数据， 睡眠和SCD疼痛之间的关系-迄今为止，最大的研究， 对SCD青少年进行瞬时评估（EMA）和腕动记录测量。以前的分析 这些数据没有考虑到关系的动态性质，也没有检查移动健康数据的范围 available.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， 该模型专注于预测儿科SCD疼痛严重程度，可以利用移动健康的各种组合 变量，以及（2）确定哪些建模框架实例- mHealth数据组合与 该模型可有效预测个体SCD疼痛严重程度模式。（3）我们将使用 选择用于预测儿科SCD疼痛发作和疼痛加重风险的框架实例， 确定哪些mHealth变量组合在预测这些疼痛特征方面最成功。 具体来说，我们将应用机器学习算法，并评估每个建模实例的能力 框架来预测第二天的疼痛发作或加重。

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.