Baty Florent, Ritz Christian, Jensen Signe Marie, Kern Lukas, Tamm Michael, Brutsche Martin Hugo
Department of Pulmonary Medicine, Cantonal Hospital St. Gallen, St. Gallen, Switzerland.
Department of Nutrition, Exercise and Sports, University of Copenhagen, Copenhagen, Denmark.
PLoS One. 2017 Nov 8;12(11):e0187548. doi: 10.1371/journal.pone.0187548. eCollection 2017.
6-min walk tests (6MWT) are routinely performed in patients with chronic obstructive pulmonary disease (COPD). Oxygen uptake ([Formula: see text]) kinetics during 6MWT can be modeled and derived parameters provide indicators of patients' exercise capacity. Post-exercise [Formula: see text] recovery also provides important parameters of patients' fitness which has not been extensively investigated in COPD. Several nonlinear regression models with different underlying biological assumptions may be suitable for describing recovery kinetics. Multimodel inference (model averaging) can then be used to capture the uncertainty in considering several models. Our aim was to apply multimodel inference in order to better understand the physiological underpinnings of [Formula: see text] recovery after 6MWT in patients with COPD. 61 patients with COPD (stages 2 to 4) were included in this study. Oxygen kinetics during 6MWT were modeled using nonlinear regression. Three statistical approaches (mixed-effects, meta-analysis and weighted regression) were compared in order to summarize estimates obtained from multiple kinetics. The recovery phase was modeled using 3 distinct equations (log-logistic, Weibull 1 and Weibull 2). Three models were fitted to the set of 61 kinetics. A significant model-averaged difference of 40.39 sec (SE = 17.1) in the time to half decrease of [Formula: see text] level ([Formula: see text]) was found between stage 2 and 4 (p = 0.0178). In addition, the Weibull 1 model characterized by a steeper decrease at the beginning of the recovery phase showed some improvement of goodness of fit when fitted to the kinetics of patients with stage 2 COPD in comparison with the 2 other models. Multimodel inference was successfully used to model [Formula: see text] recovery after 6MWT in patients with COPD. Significant model-averaged differences in [Formula: see text] were found between moderate and very severe COPD patients. Furthermore, specific patterns of [Formula: see text] recovery could be identified across COPD stages.
6分钟步行试验（6MWT）常用于慢性阻塞性肺疾病（COPD）患者。6MWT期间的摄氧量（[公式：见正文]）动力学可以建模，导出的参数可作为患者运动能力的指标。运动后[公式：见正文]恢复情况也能提供患者健康状况的重要参数，但在COPD患者中尚未得到广泛研究。几种具有不同潜在生物学假设的非线性回归模型可能适用于描述恢复动力学。然后可以使用多模型推断（模型平均）来捕捉考虑多个模型时的不确定性。我们的目的是应用多模型推断，以便更好地理解COPD患者6MWT后[公式：见正文]恢复的生理基础。本研究纳入了61例COPD（2至4期）患者。使用非线性回归对6MWT期间的氧动力学进行建模。比较了三种统计方法（混合效应、荟萃分析和加权回归），以总结从多个动力学获得的估计值。恢复阶段使用3个不同的方程（对数逻辑斯蒂、威布尔1和威布尔2）进行建模。对61个动力学数据集拟合了三个模型。在第2期和第4期之间发现，[公式：见正文]水平（[公式：见正文]）下降一半的时间存在显著的模型平均差异，为40.39秒（SE = 17.1）（p = 0.017）。此外，与其他两个模型相比，威布尔1模型在恢复阶段开始时下降更陡峭，对2期COPD患者的动力学进行拟合时，拟合优度有所改善。多模型推断成功用于对COPD患者6MWT后的[公式：见正文]恢复进行建模。在中度和极重度COPD患者之间发现了[公式：见正文]的显著模型平均差异。此外，在COPD各阶段可以识别出[公式：见正文]恢复的特定模式。
