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Sequence analysis of capnography waveform abnormalities during nurse-administered procedural sedation and analgesia in the cardiac catheterization laboratory

机译:护士在心脏导管实验室进行的程序性镇静和镇痛过程中二氧化碳图波形异常的序列分析

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摘要

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 CO2 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 (R2 = 2.4%; p = 0.02), smoking status (R2 = 2.8%; p = 0.01), Charlson comorbidity index score (R2 = 2.5%; p = 0.021), peak transcutaneous carbon dioxide concentration (R2 = 4.1%; p = 0.002) and receiving an intervention to support respiration (R2 = 5.6%; p = 0.001) were significant covariates but each explained only small amounts of the variation in respiratory state sequences. Oxygen desaturation (SpO2 < 90%) was rare (n = 3; 3%) and not associated with respiratory state sequence trajectories.
机译:识别二氧化碳图波形异常中的常见模式以及影响这些模式的因素可能会产生见解,以优化对镇静性呼吸抑制的反应。根据预先设定的呼吸频率临界值,将每一秒钟的手术分为正常呼吸或其他二氧化碳图波形异常状态,从而得出102名在心脏导管实验室进行了手术镇静和镇痛的患者的呼吸状态序列和潮气末二氧化碳浓度。分层聚类确定了呼吸状态序列中的四种常见模式,这些模式的特征主要是分配了正常呼吸的状态(n = 42; 41%),呼吸不足的通气不足(n = 38; 38%),呼吸暂停(n = 15; 15) %)和缓缓通气不足(n = 7; 7%)。包括人口统计学和临床​​变量在内的多变量距离矩阵回归模型解释了呼吸状态序列个体间差异的28%的变化。阻塞性睡眠呼吸暂停(R 2 = 2.4%; p = 0.02),吸烟状态(R 2 = 2.8%; p = 0.01),查尔森合并症指数评分(R < sup> 2 = 2.5%; p = 0.021),峰值经皮二氧化碳浓度(R 2 = 4.1%; p = 0.002)并接受干预以支持呼吸(R 2 = 5.6%; p = 0.001)是显着的协变量,但每个变量仅解释了呼吸状态序列的少量变化。氧饱和度降低(SpO2 <90%)很少(n = 3; 3%),并且与呼吸状态序列轨迹无关。

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