1 Department of Pediatrics-Allergy and Immunology.
2 Tueo Health, Inc., San Francisco, California.
Am J Respir Crit Care Med. 2018 Aug 1;198(3):320-328. doi: 10.1164/rccm.201712-2606OC.
Asthma management depends on prompt identification of symptoms, which challenges both patients and providers. In asthma, a misapprehension of health between exacerbations can compromise compliance. Thus, there is a need for a tool that permits objective longitudinal monitoring without increasing the burden of patient compliance.
We sought to determine whether changes in nocturnal physiology are associated with asthma symptoms in pediatric patients.
Using a contactless bed sensor, nocturnal heart rate (HR), respiratory rate, relative stroke volume, and movement in children with asthma 5-18 years of age (n = 16) were recorded. Asthma symptoms and asthma control test (ACT) score were reported every 2 weeks. Random forest model was used to identify physiologic parameters associated with asthma symptoms. Elastic net regression was used to identify variables associated with ACT score.
The model on the full cohort performed with sensitivity of 47.2%, specificity of 96.3%, and accuracy of 87.4%; HR and respiratory parameters were the most important variables in this model. The model predicted asthma symptoms 35% of the time on the day before perception of symptoms, and 100% of the time for a select subject for which the model performed with greater sensitivity. Multivariable and bivariable analyses demonstrated significant association between HR and respiratory rate parameters and ACT score.
Nocturnal physiologic changes correlate with asthma symptoms, supporting the notion that nocturnal physiologic monitoring represents an objective diagnostic tool capable of longitudinally assessing disease control and predicting asthma exacerbations in children with asthma at home.
哮喘管理取决于对症状的及时识别,这对患者和医生都构成了挑战。在哮喘中,对缓解期健康状况的误解可能会影响依从性。因此,需要一种工具,可以在不增加患者依从性负担的情况下,进行客观的纵向监测。
我们旨在确定儿童哮喘患者夜间生理变化是否与哮喘症状相关。
使用无接触式床传感器记录 5-18 岁哮喘儿童的夜间心率(HR)、呼吸频率、相对心搏量和运动。每两周报告一次哮喘症状和哮喘控制测试(ACT)评分。使用随机森林模型识别与哮喘症状相关的生理参数,使用弹性网络回归识别与 ACT 评分相关的变量。
全队列模型的灵敏度为 47.2%,特异性为 96.3%,准确性为 87.4%;HR 和呼吸参数是该模型中最重要的变量。该模型在症状出现前一天预测哮喘症状的准确率为 35%,对于特定的患者,预测准确率为 100%,而这些患者的模型表现出更高的灵敏度。多变量和双变量分析表明 HR 和呼吸率参数与 ACT 评分之间存在显著关联。
夜间生理变化与哮喘症状相关,这支持了夜间生理监测是一种能够在家中客观评估疾病控制并预测哮喘发作的诊断工具的观点。