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

使用基于移动健康数据的数学模型预测儿童镰状细胞病急性疼痛

• 批准号: 10599401
• 负责人: Reginald McGee
• 金额: $ 42.04万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10599401
• 关键词: Acute; Acute Pain; Adult; Affect; Analgesics; Baseline Surveys; Biological Factors; Childhood; Computational Technique; Coupled; Data; Data Analyses; Data Set; Demographic Factors; Development; Differential Equation; Ecological momentary assessment; Effectiveness; Formulation; Foundations; Frequencies; Goals; Health; Health Technology; Healthcare; Hematological Disease; Hospitalization; Hyperalgesia; Individual; Intervention; Link; Mathematics; Measures; Medical Care Costs; Modeling; Nature; Opiate Addiction; Opioid; Outcome; Pain; Pain management; Participant; Patient Self-Report; Patients; Pattern; Persons; Population; Prospective Studies; Quality of life; Risk; Sampling; Severities; Sickle Cell Anemia; Sleep; Statistical Models; Testing; Time; United States; Youth; actigraphy; addiction; aged; base; clinical practice; experience; family genetics; feature selection; handheld mobile device; health care service utilization; health data; health related quality of life; implementation intervention; improved; individual patient; mHealth; machine learning algorithm; mathematical model; mortality risk; pain-related disability; pediatric patients; predictive modeling; prevent; prophylactic; risk minimization; web-based intervention

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.

项目摘要 镰状细胞疾病（SCD）影响全球超过2000万人，大约有100,000人 住在美国。患有SCD的人死亡风险增加，与健康相关的质量差 生活和高度医疗保健利用。疼痛是与健康结果不良和增加有关的主要因素 SCD患者的医疗费用。大多数SCD疼痛发作是意外的。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以及生活质量差。 准确的数学模型预测小儿患者的SCD疼痛将有助于发育， 测试，并最大化实施干预措施的时间以提高其有效性，并 减少阿片类药物的使用；因此，将阿片类药物依赖性的风险降至最低。我们的中心假设是 合并时间变化的移动健康（MHealth）变量的动态数学模型将增加 预测单个每日小儿SCD疼痛特征的准确性 - 疼痛严重程度，发作和恶化。 我们还假设MHealth数据的变化是小儿SCD疼痛变化的重要前体。 我们的短期目标是开发和测试一个动态的数学建模框架，其中包括 MHealth变量的组合，以识别预测单个每日个人的最佳模型公式 小儿SCD疼痛特征。拟议的研究将利用以前的项目的现有数据 睡眠与SCD疼痛之间的关系 - 迄今为止，最大的研究结合了生态 SCD青年的瞬时评估（EMA）和行为措施。以前的分析 这些数据没有考虑关系的动态性质或检查MHealth数据的范围 可用的。为了实现这一目标，我们的目标是（1）构建一个由动态数学组成的框架 侧重于预测可利用MHealth各种组合的小儿SCD疼痛严重程度的模型 变量，（2）确定哪些建模框架实例 - MHealth数据组合与 该模型 - 可有效预测单个SCD疼痛严重程度模式。然后（3）我们将使用 框架实例被选为预测小儿SCD疼痛发作和疼痛加剧的风险和 确定哪些MHealth变量组合最成功地预测这些疼痛特征。 具体而言，我们将应用机器学习算法并评估建模实例的能力 第二天预测疼痛发作或加剧的框架。