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. 2020 Aug 1:2020:1324348.
doi: 10.1155/2020/1324348. eCollection 2020.

Predictive Factors for Failure of Noninvasive Ventilation in Adult Intensive Care Unit: A Retrospective Clinical Study

Qimin Chen  1 , Ming Liu  1 , Bo Liu  1 , Wei Li  1 , Daixiu Gao  1 , Lulu Xie  1 , Yu Wu  1 , Liang Li  1 , Ying Liu  1 , Ying Wang  1 , Tang Yan  1 , Yuanyi Liu  1 , Yumei Cheng  1 , Xu Liu  1 , Feng Shen  1
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Predictive Factors for Failure of Noninvasive Ventilation in Adult Intensive Care Unit: A Retrospective Clinical Study

Qimin Chen et al. Can Respir J. .

Abstract

Background: Noninvasive ventilation (NIV) has been reported to be beneficial for patients with acute respiratory failure in intensive care unit (ICU); however, factors that influence the clinical outcome of NIV were unclarified. We aim to determine the factors that predict the failure of NIV in critically ill patients with acute respiratory failure (ARF). Setting. Adult mixed ICU in a medical university affiliated hospital. Patients and Methods. A retrospective clinical study using data from critical adult patients with initial NIV admitted to ICU in the period August 2016 to November 2017. Failure of NIV was regarded as patients needing invasive ventilation. Logistic regression was employed to determine the risk factor(s) for NIV, and a predictive model for NIV outcome was set up using risk factors.

Results: Of 101 included patients, 50 were unsuccessful. Although more than 20 variables were associated with NIV failure, multivariate logistic regression demonstrated that only ideal body weight (IBW) (OR 1.110 (95%1.027-1.201), P=0.009), the maximal heart rate during NIV period (HR-MAX) (OR 1.024 (1.004-1.046), P=0.021), the minimal respiratory rate during NIV period (RR-MIN) (OR 1.198(1.051-1.365), P=0.007), and the highest body temperature during NIV period (T-MAX) (OR 1.838(1.038-3.252), P=0.037) were independent risk factors for NIV failure. We set up a predictive model based on these independent risk factors, whose area under the receiver operating characteristic curve (AUROC) was 0.783 (95% CI: 0.676-0.899, P < 0.001), and the sensitivity and specificity of model were 68.75% and 71.43%, respectively, with the optimal cut-off value of 0.4863.

Conclusion: IBW, HR-MAX, RR-MIN, and T-MAX were associated with NIV failure in patients with ARF. A predictive model based on the risk factors could help to discriminate patients who are vulnerable to NIV failure.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
ROC of predictive model for NIV outcome.
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