Faulhaber Martin, Wille Maria, Gatterer Hannes, Heinrich Dieter, Burtscher Martin
Department of Sport Science, University Innsbruck, Fürstenweg 185, 6020, Innsbruck, Austria,
Sleep Breath. 2014 Sep;18(3):669-74. doi: 10.1007/s11325-013-0932-2. Epub 2014 Jan 17.
The study evaluated the predictive value of arterial oxygen saturation (SaO2) after 30-min hypoxic exposure on subsequent development of acute mountain sickness (AMS) and tested if additional resting cardio-respiratory measurements improve AMS prognosis.
Fifty-five persons were exposed to a simulated altitude of 4,500 m (normobaric hypoxia, FiO2 = 12.5%). Cardio-respiratory parameters, SaO2, blood lactate, and blood pressure were measured after 30 min of exposure. AMS symptoms were recorded after 3, 6, 9, and 12 h (Lake-Louise Score). Three models, based on previously published regression equations for altitude-dependent SaO2 values of AMS-susceptible (SaO2-suscept = 98.34 - 2.72 ∗ alt - 0.35 ∗ alt(2)) and AMS-resistant (SaO2-resist = 96.51 + 0.68 ∗ alt - 0.80 ∗ alt(2)) persons, were applied to predict AMS. Additionally, multivariate logistic regression analyses were conducted to test if additional resting measurements improve AMS prediction.
The three models correctly predicted AMS development in 62%, 67%, and 69% of the cases. No model showed combined sensitivity and specificity >80%. Sequential logistic regression revealed that the inclusion of tidal volume or breathing frequency in addition to SaO2 improved overall AMS prediction, resulting in 78% and 80% correct AMS prediction, respectively.
Non-invasive measurements of SaO2 after 30-min hypoxic exposure are easy to perform and have the potential to detect AMS-susceptible individuals with a sufficient sensitivity. The additional determination of breathing frequency can improve success in AMS prediction.
本研究评估了30分钟低氧暴露后动脉血氧饱和度(SaO2)对急性高原病(AMS)后续发生的预测价值,并测试了额外的静息心肺测量是否能改善AMS的预后。
55名受试者暴露于模拟海拔4500米(常压低氧,FiO2 = 12.5%)环境中。暴露30分钟后测量心肺参数、SaO2、血乳酸和血压。在3、6、9和12小时后记录AMS症状(采用路易斯湖评分)。基于先前发表的针对AMS易感人群(SaO2 - 易感 = 98.34 - 2.72 * 海拔 - 0.35 * 海拔(2))和AMS抗性人群(SaO2 - 抗性 = 96.51 + 0.68 * 海拔 - 0.80 * 海拔(2))的海拔依赖性SaO2值的回归方程,应用三个模型预测AMS。此外,进行多变量逻辑回归分析以测试额外的静息测量是否能改善AMS预测。
三个模型分别在62%、67%和69%的病例中正确预测了AMS的发生。没有一个模型的综合敏感性和特异性>80%。序贯逻辑回归显示,除了SaO2外,纳入潮气量或呼吸频率可改善整体AMS预测,正确预测AMS的比例分别为78%和80%。
30分钟低氧暴露后对SaO2进行非侵入性测量易于实施,并且有潜力以足够的敏感性检测出AMS易感个体。额外测定呼吸频率可提高AMS预测的成功率。