Objectives To assess the prognostic value of blood sugar level for acute respiratory failure patients undergoing mechanical ventilation. Methods The study collected 139 acute respiratory failure patients undergoing mechanical ventilation admitted between February 2012 and October 2013. The patients were divided into a hyperglycemic group (n=123, blood sugar ≥143 mg/dl) and a non-hyperglycemic group (n=16, blood sugar <143 mg/dl). The data for basic clinical pathological characteristics and the blood sugar levels were collected, and the correlation between the blood sugar level and the prognosis was assessed using single factor analysis and logistic regression method. Results In the study, 88.49% of patients with acute respiratory failure undergoing mechanical ventilation had hyperglycemia (blood sugar ≥143 mg/dl). The proportions of patients with APACHEⅡ score ≥10, chronic obstructive pulmonary disease (COPD) or hypoxemia in the hyperglycemic group were significantly higher than those in the non-hyperglycemic group (P<0.05). APACHEⅡ ≥10, COPD and hypoxemia were significant risk factors for hyperglycemia. At the same time, the proportions of patients in the death group with hyperglycemia ≥143 mg/dl ( OR=8.354, 95%CI 1.067-65.388, P=0.018), APACHEⅡ≥10 ( OR=2.545, 95%CI 1.109-6.356, P=0.046), COPD ( OR=2.871, 95%CI 1.203-6.852, P=0.015), and hypoxemia ( OR=3.500, 95%CI 1.556-7.874, P=0.002) were significantly higher than those in the survival group. Kaplan-Meier curve analysis found that the overall survival of the hyperglycemic patients with acute respiratory failure was significantly lower than that in the non-hyperglycemic patients (P<0.001). Conclusion Blood sugar level can be used as an independent predictor for acute respiratory failure patients undergoing mechanical ventilation.
It is a challenge for clinicians and diagnostic systematic reviewers to determine the best test in clinical diagnosis and screening. Meanwhile, it also becomes the new chance and challenge for diagnostic test meta-analysis. Network meta-analysis has been commonly used in intervention systematic reviews, which can compare the effect size of all available interventions and to choose the best intervention. Network meta-analysis of diagnostic test can be defined as comparing all available diagnostic technologies in the same conditions based on the common reference tests. In order to provide the guide for diagnostic systematic reviewers, we aims to introduce four methods of conducting diagnostic test accuracy network meta-analysis, and to explore two ranking methods of network meta-analysis of diagnostic test accuracy.