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加权低氧血症指数:一种用于量化低氧血症严重程度的适应性方法。

Weighted Hypoxemia Index: An adaptable method for quantifying hypoxemia severity.

作者信息

Lim Diane C, Chen Cheng-Bang, Paul Ankita, Wang Yujie, Kim Jinyoung, Yook Soonhyun, Kim Emily Y, Kim Edison Q, Das Anup, Wangpaichitr Medhi, Somers Virend K, Lee Chi Hang, Zee Phyllis C, Imamura Toshihiro, Kim Hosung

机构信息

Department of Medicine, University of Miami, Miami, Florida, United States of America.

Department of Medicine, Miami VAHS, Miami, Florida, United States of America.

出版信息

PLoS One. 2025 Jul 10;20(7):e0328214. doi: 10.1371/journal.pone.0328214. eCollection 2025.

Abstract

OBJECTIVE

To quantitate hypoxemia severity.

METHODS

We developed the Weighted Hypoxemia Index to be adapted to different clinical settings by applying 5 steps to the oxygen saturation curve: (1) Identify desaturation/resaturation event [Formula: see text] by setting the upper threshold; (2) Exclude events as artifact by setting a lower threshold; (3) Calculate weighted area for each [Formula: see text] as [Formula: see text]; (4) Calculate a normalization factor [Formula: see text] for each subject; (5) Calculate the Weighted Hypoxemia Index as the summation of all weighted areas multiplied by [Formula: see text]. We assessed the Weighted Hypoxemia Index predictive value for all-cause mortality and cardiovascular mortality using the Sleep Heart Health Study (enrollment 1995-1998, 11.1 years mean follow-up).

RESULTS

We set varying upper thresholds at 92%, 90%, 88%, and 86%, a lower threshold of 50%, calculated area under the curve and area above the curve, with and without a linear weighted factor (duration of each event [Formula: see text]), and used the same normalization factor of total sleep time <90% divided by total sleep time. After excluding subjects with missing data, we analyzed 4,509 participants (Alive: N = 3,769; All-cause mortality: N = 1,071; cardiovascular mortality: N = 330). Since the Weighted Hypoxemia Index-Area Under the Curve set at upper threshold of 90% (WHI-AUC90) had the best results in predicting all-cause mortality, we then compared it to the Apnea-Hypopnea Index and Total Sleep Time <90%. WHI-AUC90 showed statistical significance across quintiles for all-cause mortality, but not cardiovascular mortality, in adjusted Cox regression models.

CONCLUSION

The Weighted Hypoxemia Index offers a versatile and clinically relevant method for quantifying hypoxemia severity, with potential applications to evaluate mechanisms and outcomes across various patient populations.

摘要

目的

量化低氧血症的严重程度。

方法

我们开发了加权低氧血症指数,通过对氧饱和度曲线应用5个步骤来适应不同的临床情况:(1)通过设置上限阈值识别去饱和/再饱和事件[公式:见正文];(2)通过设置下限阈值将事件排除为伪像;(3)计算每个[公式:见正文]的加权面积为[公式:见正文];(4)为每个受试者计算归一化因子[公式:见正文];(5)计算加权低氧血症指数,即所有加权面积之和乘以[公式:见正文]。我们使用睡眠心脏健康研究(1995 - 1998年入组,平均随访11.1年)评估加权低氧血症指数对全因死亡率和心血管死亡率的预测价值。

结果

我们将不同的上限阈值设置为92%、90%、88%和86%,下限阈值设置为50%,计算曲线下面积和曲线上面积,有和没有线性加权因子(每个事件的持续时间[公式:见正文]),并使用总睡眠时间<90%除以总睡眠时间的相同归一化因子。在排除有缺失数据的受试者后,我们分析了4509名参与者(存活:N = 3769;全因死亡率:N = 1071;心血管死亡率:N = 330)。由于将上限阈值设置为90%时的加权低氧血症指数 - 曲线下面积(WHI - AUC90)在预测全因死亡率方面具有最佳结果,我们随后将其与呼吸暂停低通气指数和总睡眠时间<90%进行比较。在调整后的Cox回归模型中,WHI - AUC90在全因死亡率的五分位数中显示出统计学意义,但在心血管死亡率中未显示。

结论

加权低氧血症指数为量化低氧血症严重程度提供了一种通用且与临床相关的方法,在评估各种患者群体的机制和结局方面具有潜在应用价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a86b/12244826/14f20c6b6871/pone.0328214.g001.jpg

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