Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, Toronto, Canada.
Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Toronto, Canada.
Sci Rep. 2019 Jul 15;9(1):10214. doi: 10.1038/s41598-019-46751-2.
Identifying common patterns in capnography waveform abnormalities and the factors that influence these patterns could yield insights to optimize responses to sedation-induced respiratory depression. Respiratory state sequences for 102 patients who had a procedure in a cardiac catheterisation laboratory with procedural sedation and analgesia were developed by classifying each second of procedures into a state of normal breathing or other capnography waveform abnormalities based on pre-specified cut-offs for respiratory rate and end-tidal CO concentration. Hierarchical clustering identified four common patterns in respiratory state sequences, which were characterized by a predominance of the state assigned normal breathing (n = 42; 41%), hypopneic hypoventilation (n = 38; 38%), apnea (n = 15; 15%) and bradypneic hypoventilation (n = 7; 7%). A multivariable distance matrix regression model including demographic and clinical variables explained 28% of the variation in inter-individual differences in respiratory state sequences. Obstructive sleep apnea (R = 2.4%; p = 0.02), smoking status (R = 2.8%; p = 0.01), Charlson comorbidity index score (R = 2.5%; p = 0.021), peak transcutaneous carbon dioxide concentration (R = 4.1%; p = 0.002) and receiving an intervention to support respiration (R = 5.6%; p = 0.001) were significant covariates but each explained only small amounts of the variation in respiratory state sequences. Oxygen desaturation (SpO < 90%) was rare (n = 3; 3%) and not associated with respiratory state sequence trajectories.
识别 capnography 波形异常的常见模式以及影响这些模式的因素,可能有助于优化对镇静诱导性呼吸抑制的反应。通过根据预设的呼吸频率和呼气末 CO 浓度的截止值,将每个过程的第二秒分类为正常呼吸或其他 capnography 波形异常状态,为在心脏导管插入术实验室进行有镇静和镇痛的程序的 102 名患者制定了呼吸状态序列。分层聚类确定了呼吸状态序列中的四种常见模式,其特征是正常呼吸(n=42;41%)、低通气性通气不足(n=38;38%)、呼吸暂停(n=15;15%)和呼吸过缓性通气不足(n=7;7%)状态占主导地位。包括人口统计学和临床变量的多变量距离矩阵回归模型解释了个体间呼吸状态序列差异的 28%。阻塞性睡眠呼吸暂停(R=2.4%;p=0.02)、吸烟状况(R=2.8%;p=0.01)、Charlson 合并症指数评分(R=2.5%;p=0.021)、经皮二氧化碳峰值浓度(R=4.1%;p=0.002)和接受支持呼吸的干预措施(R=5.6%;p=0.001)是显著的协变量,但每个协变量仅解释了呼吸状态序列变化的一小部分。氧饱和度降低(SpO<90%)(n=3;3%)很少见,与呼吸状态序列轨迹无关。