Kinematic signature of high-risk labored breathing revealed by novel signal analysis

medRxiv (Cold Spring Harbor Laboratory)(2023)

引用 0|浏览8
暂无评分
摘要
Breathing patterns (respiratory kinematics) contain vital prognostic information. They report on a dimension of physiology that is not captured by conventional vital signs. But for an informative physiomarker to become clinically valuable, it must be measureable with ease, accuracy, and reproducibility. We sought to enable the quantitative characterization of respiratory kinematics at the bedside. Using inertial sensors, we analyzed upper rib, lower rib, and abdominal motion of 108 patients with respiratory symptoms during a hospital encounter (582 two-minute recordings). We measured the average respiratory rate and 33 other signal characteristics that had an explainable correspondence with clinically significant breathing patterns. K-means clustering revealed that the respiratory kinematic information was optimally represented by adding 3 novel measures to the average respiratory rate. We selected measures representing respiratory rate variability, respiratory alternans (rib-predominant breaths alternating with abdomen-predominant ones), and recruitment of accessory muscles (increased upper rib excursion). Latent profile analysis of these measures revealed a phenotype consistent with labored breathing. Poisson regression showed that the rate at which a patient’s recordings exhibited the labored breathing phenotype was significantly associated (p<0.01) with the severity of illness (discharge home v/s acute-care hospitalization v/s critical-care hospitalization). Notably, labored breathing was frequently detectable (21%) when the respiratory rate was normal, and it improved discrimination for critical illness. These findings validate the feasibility of respiratory kinematic phenotyping in routine healthcare settings, and demonstrate its clinical value. Further research into respiratory kinematic characteristics may reveal novel pathophysiologic mechanisms, advance the efficacy of predictive analytics, and enhance patient safety. ### Competing Interest Statement SMG, WBA, RDW, SJR, and JRM are co-inventors in United States Patent Application Serial No. 18/018,469 ### Funding Statement This study was funded by Ivy foundation COVID-19 translational research fund ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Health Sciences IRB of University Of Virginia have ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors
更多
查看译文
关键词
labored breathing,kinematic signature,high-risk
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要