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iPREDICT:一种概念验证研究,旨在开发一种关于喘控制变化的预测模型
Mario Castro1, Merrill Zavod2, Annika Rutgersson3
1Chief, Division of Pulmonary, Critical Care and Sleep Medicine, Vice Chair for Clinical and Translational Research, University of Kansas School of Medicine, 4000 Cambridge Street, Mailstop 3007, Kansas City, KS 66160, USA.
数字传感器和移动应用程序有助于预测患者的喘控制变化. 亚组和个体模型显示出显著的准确性,特别是在预测增加短期β-agonist使用时.
科学领域:
- 数字卫生技术数字卫生技术
- 喘管理的管理
- 预测建模的预测建模.
背景情况:
- 使用数字传感器技术 (iPREDICT) 个性化的疾病控制预测计划利用数字工具进行喘管理.
- 设备被整合到患者的日常生活中,以最小的负担监测健康状况的变化.
研究的目的:
- 确定喘的基线特征.
- 检测与喘事件相关的健康状况变化.
- 评估触发器识别和预测喘控制波动的算法.
- 探索患者体验和设备合规性.
主要方法:
- 在美国进行了一项为期24周的多中心观察性研究.
- 包括12岁以上患有严重,不受控制的喘的患者.
- 使用螺旋计,生命体标志监测器,睡眠监测器,连接的吸入器,以及带有患者报告结果问卷的移动应用程序.
- 将前性数据与电子健康记录联系起来,以开发算法.
- 根据症状,峰值呼气流 (PEF),1秒内强制呼气体积 (FEV1) 和短效β-激动剂 (SABA) 的使用,定义了喘事件.
主要成果:
- 在108名患者中,66人完成了研究.
- 预测准确度因终点而异;人口级模型显示PEF准确度低 <65%.
- 小组模型对于特定的过敏,触发因素和喘类型具有很高的准确性.
- 最准确的预测终点是>4个SABA吸气/天/48小时.
- 单个模型显示PEF<65%和>4个SABA吸气/天/48小时的显著准确性.
结论:
- 多维数据集使得人口,子组和个体分析成为可能.
- 为开发波动性喘控制的预测模型提供了概念验证.
- 强调数字健康技术在个性化喘管理中的潜力.
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