■使用数字传感器技术(iPREDICT)开发了个性化预测疾病控制计划,用于使用数字技术进行哮喘管理。将设备集成到患者的日常生活中,以最小的设备负担通过测量基线健康状况的变化来建立哮喘控制的预测模型。
■为了建立研究参与者的基线疾病特征,检测与哮喘事件相关的基线变化,并评估能够从基线数据识别触发因素和预测哮喘控制变化的算法。还探索了患者的经验和对设备的依从性。
■这是一个多中心,观察,24周,在美国进行的概念验证研究。
■患者(12年)患有严重,不受控制的哮喘与肺活量计有关,生命体征监测仪,睡眠监视器,连接的吸入器装置,和两个带有嵌入式患者报告结果(PRO)问卷的移动应用程序。将预期数据与电子健康记录中的数据相关联,并传输到安全平台以开发预测算法。主要终点是哮喘事件:患者症状恶化(PRO);呼气峰值流量(PEF)<65%或1s内用力呼气量<80%;短效β2激动剂(SABA)使用增加(>8次/24小时或>4次/天/48小时)。对于每个端点,预测模型是在人群中构建的,子组,和个人水平。
■总的来说,选择了108例患者:66例(61.1%)完成,42例(38.9%)因未能响应/数据缺失而被排除。预测准确性取决于终点选择。人口水平模型在预测终点如PEF<65%方面的准确性较低。与特定过敏相关的亚组,哮喘触发因素,哮喘类型,加重治疗显示出很高的准确性,用最准确的,预测终点>4SABA抽吸/天/48小时。个体模型,为具有高终点重叠的患者构建,表现出显著的预测准确性,特别是对于PEF<65%和>4SABA粉扑/天/48小时。
■此多维数据集启用了population-,子组-,和个人层面的分析,为哮喘波动控制预测模型的发展提供概念验证证据.
UNASSIGNED: The individualized PREdiction of DIsease Control using digital sensor Technology (iPREDICT) program was developed for asthma management using digital technology. Devices were integrated into daily lives of patients to establish a predictive model of asthma control by measuring changes from baseline health status with minimal device burden.
UNASSIGNED: To establish baseline disease characteristics of the
study participants, detect changes from baseline associated with asthma events, and evaluate algorithms capable of identifying triggers and predicting asthma control changes from baseline data. Patient experience and compliance with the devices were also explored.
UNASSIGNED: This was a multicenter, observational, 24-week, proof-of-concept
study conducted in the United States.
UNASSIGNED: Patients (⩾12 years) with severe, uncontrolled asthma engaged with a spirometer, vital sign monitor, sleep monitor, connected inhaler devices, and two mobile applications with embedded patient-reported outcome (PRO) questionnaires. Prospective data were linked to data from electronic health records and transmitted to a secure platform to develop predictive algorithms. The primary endpoint was an asthma event: symptom worsening logged by patients (PRO); peak expiratory flow (PEF) < 65% or forced expiratory volume in 1 s < 80%; increased short-acting β2-agonist (SABA) use (>8 puffs/24 h or >4 puffs/day/48 h). For each endpoint, predictive models were constructed at population, subgroup, and individual levels.
UNASSIGNED: Overall, 108 patients were selected: 66 (61.1%) completed and 42 (38.9%) were excluded for failure to respond/missing data. Predictive accuracy depended on endpoint selection. Population-level models achieved low accuracy in predicting endpoints such as PEF < 65%. Subgroups related to specific allergies, asthma triggers, asthma types, and exacerbation treatments demonstrated high accuracy, with the most accurate, predictive endpoint being >4 SABA puffs/day/48 h. Individual models, constructed for patients with high endpoint overlap, exhibited significant predictive accuracy, especially for PEF < 65% and >4 SABA puffs/day/48 h.
UNASSIGNED: This multidimensional dataset enabled population-, subgroup-, and individual-level analyses, providing proof-of-concept evidence for development of predictive models of fluctuating asthma control.