Literature DB >> 32520890

Central Venous-to-Arterial PCO2 Difference and Central Venous Oxygen Saturation in the Detection of Extubation Failure in Critically Ill Patients.

Jihad Mallat1,2,3, Fawzi Ali Baghdadi4, Usman Mohammad5, Malcolm Lemyze5, Johanna Temime1, Laurent Tronchon1, Didier Thevenin1, Marc-Olivier Fischer6.   

Abstract

OBJECTIVES: To evaluate the ability of central venous-to-arterial carbon dioxide pressure difference, central venous oxygen saturation, and the combination of these two parameters to detect extubation failure in critically ill patients.
DESIGN: Multicentric, prospective, observational study.
SETTING: Three ICUs. PATIENTS: All patients who received mechanical ventilation for more than 48 hours and tolerated spontaneous breathing trials with a T-piece for 60 minutes.
INTERVENTIONS: Extubation after successful spontaneous breathing trials. Extubation failure was defined as the need for mechanical ventilation within 48 hours.
MEASUREMENTS AND MAIN RESULTS: The oxygen delivery index, oxygen consumption index, central venous oxygen saturation, central venous-to-arterial carbon dioxide pressure difference, and oxygen extraction were measured immediately before spontaneous breathing trials and at 60 minutes after spontaneous breathing trials initiation. Seventy-five patients were enrolled, and extubation failure was noted in 18 (24%) patients. Oxygen consumption index increased significantly during spontaneous breathing trials in the failure group. Oxygen delivery index increased in both success and failure groups. Oxygen extraction increased in the failure group (p = 0.005) and decreased in the success group (p = 0.001). Central venous oxygen saturation decreased in the failure group and increased in the success group (p = 0.014). ΔPCO2 value increased in the extubation failure group and decreased in the success group (p = 0.002). Changes in ΔPCO2 (Δ - ΔPCO2) and central venous oxygen saturation (ΔScvO2) during spontaneous breathing trials were independently associated with extubation failure (odds ratio, 1.02; 95% CI, 1.01-1.05; p = 0.006, and odds ratio, 0.84; 95% CI, 0.70-0.95; p = 0.02, respectively). Δ - ΔPCO2 and central venous oxygen saturation could predict extubation failure with areas under the curve of 0.865 and 0.856, respectively; however, their combined areas under the curve was better at 0.940.
CONCLUSIONS: We found that Δ - ΔPCO2 and central venous oxygen saturation, during spontaneous breathing trials, were independent predictors of weaning outcomes. Combination analysis of both parameters enhanced their diagnostic performance and provided excellent predictability in extubation failure detection in critically ill patients.

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Year:  2020        PMID: 32520890     DOI: 10.1097/CCM.0000000000004446

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  4 in total

1.  Weaning critically ill patients from mechanical ventilation: a protocol from a multicenter retrospective cohort study.

Authors:  Yingzhi Wang; Liming Lei; Huawei Yang; Songbin He; Junhai Hao; Tao Liu; Xingdong Chen; Yongbo Huang; Jing Zhou; Zhimin Lin; Haichong Zheng; Xiaoling Lin; Weixiang Huang; Xiaoqing Liu; Yimin Li; Linxi Huang; Wenbing Qiu; Huangyao Ru; Danni Wang; Jianfeng Wu; Huifang Zheng; Liuer Zuo; Peiling Zeng; Jian Zhong; Yanhui Rong; Min Fan; Jianwei Li; Shaoqing Cai; Qiuye Kou; Enhe Liu; Zhuandi Lin; Jingjing Cai; Hong Yang; Fen Li; Yanhong Wang; Xinfeng Lin; Weitao Chen; Youshan Gao; Shifang Huang; Ling Sang; Yuanda Xu; Kouxing Zhang
Journal:  J Thorac Dis       Date:  2022-01       Impact factor: 2.895

2.  A Novel Non-invasive Index of Cardiopulmonary Reserve for the Prediction of Failure of Weaning From Mechanical Ventilation.

Authors:  George T Nikitas; Stylianos Kykalos; Evangelia Ntikoudi; Ioannis Vasileiadis; Antonia Koutsoukou; Nikolaos I Nikiteas
Journal:  Cureus       Date:  2022-07-22

3.  Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model.

Authors:  Huan Wang; Qin-Yu Zhao; Jing-Chao Luo; Kai Liu; Shen-Ji Yu; Jie-Fei Ma; Ming-Hao Luo; Guang-Wei Hao; Ying Su; Yi-Jie Zhang; Guo-Wei Tu; Zhe Luo
Journal:  BMC Pulm Med       Date:  2022-08-08       Impact factor: 3.320

4.  Development and Validation of a Machine-Learning Model for Prediction of Extubation Failure in Intensive Care Units.

Authors:  Qin-Yu Zhao; Huan Wang; Jing-Chao Luo; Ming-Hao Luo; Le-Ping Liu; Shen-Ji Yu; Kai Liu; Yi-Jie Zhang; Peng Sun; Guo-Wei Tu; Zhe Luo
Journal:  Front Med (Lausanne)       Date:  2021-05-17
  4 in total

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