Harrington Nicholas, Barba David Torres, Bui Quan M, Wassell Andrew, Khurana Sukhdeep, Rubarth Rodrigo B, Sung Kevin, Owens Robert L, Agnihotri Parag, King Kevin R
Department of Bioengineering, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, 92093, USA.
Division of Cardiovascular Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
medRxiv. 2022 Mar 14:2022.03.10.22272238. doi: 10.1101/2022.03.10.22272238.
The days and weeks preceding hospitalization are poorly understood because they transpire before patients are seen in conventional clinical care settings. Home health sensors offer opportunities to learn signatures of impending hospitalizations and facilitate early interventions, however the relevant biomarkers are unknown. Nocturnal respiratory rate (NRR) is an activity-independent biomarker that can be measured by adherence-independent sensors in the home bed. Here, we report automated longitudinal monitoring of NRR dynamics in a cohort of high-risk recently hospitalized patients using non-contact mechanical sensors under patients' home beds. Since the distribution of nocturnal respiratory rates in populations is not well defined, we first quantified it in 2,000 overnight sleep studies from the NHLBI Sleep Heart Health Study. This revealed that interpatient variability was significantly greater than intrapatient variability (NRR variances of 11.7 brpm and 5.2 brpm respectively, n=1,844,110 epochs), which motivated the use of patient-specific references when monitoring longitudinally. We then performed adherence-independent longitudinal monitoring in the home beds of 34 high-risk patients and collected raw waveforms (sampled at 80 Hz) and derived quantitative NRR statistics and dynamics across 3,403 patient-nights (n= 4,326,167 epochs). We observed 23 hospitalizations for diverse causes (a 30-day hospitalization rate of 20%). Hospitalized patients had significantly greater NRR deviations from baseline compared to those who were not hospitalized (NRR variances of 3.78 brpm and 0.84 brpm respectively, n= 2,920 nights). These deviations were concentrated prior to the clinical event, suggesting that NRR can identify impending hospitalizations. We analyzed alarm threshold tradeoffs and demonstrated that nominal values would detect 11 of the 23 clinical events while only alarming 2 times in non-hospitalized patients. Taken together, our data demonstrate that NRR dynamics change days to weeks in advance of hospitalizations, with longer prodromes associating with volume overload and heart failure, and shorter prodromes associating with acute infections (pneumonia, septic shock, and covid-19), inflammation (diverticulitis), and GI bleeding. In summary, adherence-independent longitudinal NRR monitoring has potential to facilitate early recognition and management of pre-symptomatic disease.
住院前几天和几周的情况目前还知之甚少,因为这些情况发生在患者进入传统临床护理环境之前。家庭健康传感器为了解即将住院的特征并促进早期干预提供了机会,然而相关的生物标志物尚不清楚。夜间呼吸频率(NRR)是一种不依赖活动的生物标志物,可以通过安装在家庭病床中不依赖粘贴的传感器进行测量。在此,我们报告了使用患者床下的非接触式机械传感器对一组近期住院的高危患者的NRR动态进行自动纵向监测的情况。由于人群中夜间呼吸频率的分布尚不明确,我们首先在国家心肺血液研究所(NHLBI)睡眠心脏健康研究的2000项夜间睡眠研究中对其进行了量化。结果显示,患者间的变异性显著大于患者内的变异性(NRR方差分别为11.7次/分钟和5.2次/分钟,n = 1,844,110个时段),这促使我们在纵向监测时使用患者特异性参考值。然后,我们在34名高危患者的家中病床进行了不依赖粘贴的纵向监测,收集了原始波形(采样频率为80赫兹),并得出了3403个患者夜间(n = 4,326,167个时段)的NRR定量统计数据和动态变化情况。我们观察到23例因各种原因导致的住院情况(30天住院率为20%)。与未住院患者相比,住院患者的NRR与基线的偏差明显更大(NRR方差分别为3.78次/分钟和0.84次/分钟,n = 2,920个夜晚)。这些偏差集中在临床事件发生之前,这表明NRR可以识别即将发生的住院情况。我们分析了报警阈值的权衡,结果表明,标称值可以检测到23例临床事件中的11例,而在未住院患者中仅触发2次警报。综上所述,我们的数据表明,NRR动态变化在住院前几天到几周就会出现,前驱期较长与容量超负荷和心力衰竭相关,前驱期较短与急性感染(肺炎、感染性休克和新冠肺炎)、炎症(憩室炎)和胃肠道出血相关。总之,不依赖粘贴的纵向NRR监测有潜力促进对症状前疾病的早期识别和管理。