Respiratory Services, Auckland District Health Board, Auckland, New Zealand.
The University of Auckland, Auckland, New Zealand.
Physiol Meas. 2021 May 12;42(4). doi: 10.1088/1361-6579/abf05d.
Exercise oscillatory ventilation (EOV) is frequently observed in individuals with cardiac disease. Assessment of EOV relies on pattern recognition and this subjectivity and lack of quantification limits the widespread clinical use of EOV as a prognostic marker. Poincaré analysis quantifies the short (SD1) and long-term (SD2) variability of a signal and may provide an alternative means to identify and quantify unstable exercise breathing patterns. This study aimed to determine if Poincaré analysis can distinguish between the breathing patterns of healthy control subjects and individuals being assessed for heart transplantation with and without EOV.Thirty-nine subjects performed a cardiopulmonary exercise test as part of heart transplant assessment and were subjectively classified into two groups according to the presence of EOV: non-EOV ( = 19) and EOV ( = 20). The control group ( = 24) consisted of healthy adults. Poincaré analysis (SD1 and SD2) was performed for minute ventilation (V̇) and tidal volume (VT) normalized to forced vital capacity (V̇andV̇), and breathing frequency (BF) for breath-by-breath data over the 10-15 ml · min · kgV̇range.Poincaré analysis showed similar exercise ventilatory responses between the non-EOV and control group. BF was found to discriminate between subjects with stable and unstable ventilation. BF SD1 was significantly higher in the EOV group compared to the non-EOV (7.9 versus 4.6, < 0.01) and control (7.9 versus 4.2, < 0.01) groups. The EOV group had significantly greater BF SD2 compared to the non-EOV (5.7 versus 3.5, < 0.01) and control (5.7 versus 3.5, < 0.01) groups.We demonstrated that this novel application of Poincaré analysis can objectively distinguish and quantify unstable from stable breathing patterns during exercise. In subjects being assessed for heart transplantation the presence of EOV is associated with greater BF variability. Poincaré analysis provides an objective measure to identify and quantify EOV.As EOV may indicate abnormal ventilatory control, there is a need for an objective measure to identify and quantify unstable from stable ventilation during exercise. We developed a method of quantifying BF variation by the application of Poincaré analysis and demonstrated higher than normal variability of BF in subjects being assessed for heart transplantation who demonstrated EOV.
运动性振荡通气（EOV）在心脏病患者中经常观察到。EOV 的评估依赖于模式识别，这种主观性和缺乏量化限制了 EOV 作为预后标志物的广泛临床应用。Poincaré 分析量化了信号的短期（SD1）和长期（SD2）变异性，可能提供了一种替代方法来识别和量化不稳定的运动呼吸模式。本研究旨在确定 Poincaré 分析是否可以区分健康对照者和接受心脏移植评估的个体的呼吸模式，这些个体有无 EOV。39 名受试者进行了心肺运动测试，作为心脏移植评估的一部分，并根据是否存在 EOV 进行主观分类：非 EOV（n=19）和 EOV（n=20）。对照组（n=24）由健康成年人组成。对分钟通气量（V̇）和潮气容积（VT）归一化至用力肺活量（V̇和V̇）以及呼吸频率（BF）进行 Poincaré 分析，对 10-15 ml·min·kgV̇范围内的每一次呼吸进行逐次呼吸数据。Poincaré 分析显示，非 EOV 组和对照组的运动通气反应相似。BF 被发现可以区分稳定和不稳定的通气。与非 EOV（7.9 比 4.6，<0.01）和对照组（7.9 比 4.2，<0.01）相比，EOV 组的 BF SD1 明显更高。与非 EOV（5.7 比 3.5，<0.01）和对照组（5.7 比 3.5，<0.01）相比，EOV 组的 BF SD2 明显更高。我们证明，这种 Poincaré 分析的新应用可以客观地区分和量化运动期间不稳定和稳定的呼吸模式。在接受心脏移植评估的受试者中，EOV 的存在与更大的 BF 变异性相关。Poincaré 分析提供了一种客观的测量方法来识别和量化 EOV。由于 EOV 可能表明异常的通气控制，因此需要一种客观的测量方法来识别和量化运动期间的不稳定和稳定通气。我们开发了一种通过应用 Poincaré 分析量化 BF 变化的方法，并在接受心脏移植评估的受试者中证明了 BF 变异性高于正常。
