Selected article for: "acute exacerbation and logistic regression analysis"

Author: Choi, Juwhan; Sim, Jae Kyeom; Oh, Jee Youn; Lee, Young Seok; Hur, Gyu Young; Lee, Sung Yong; Shim, Jae Jeong; Rhee, Chin Kook; Min, Kyung Hoon
Title: Prognostic marker for severe acute exacerbation of chronic obstructive pulmonary disease: analysis of diffusing capacity of the lung for carbon monoxide (D(LCO)) and forced expiratory volume in one second (FEV(1))
  • Cord-id: 1qzhkfig
  • Document date: 2021_5_6
  • ID: 1qzhkfig
    Snippet: BACKGROUND: It is important to assess the prognosis of patients with chronic obstructive pulmonary disease (COPD) and acute exacerbation of COPD (AECOPD). Recently, it was suggested that diffusing capacity of the lung for carbon monoxide (D(LCO)) should be added to multidimensional tools for assessing COPD. This study aimed to compare the D(LCO) and forced expiratory volume in one second (FEV(1)) to identify better prognostic factors for admitted patients with AECOPD. METHODS: We retrospectively
    Document: BACKGROUND: It is important to assess the prognosis of patients with chronic obstructive pulmonary disease (COPD) and acute exacerbation of COPD (AECOPD). Recently, it was suggested that diffusing capacity of the lung for carbon monoxide (D(LCO)) should be added to multidimensional tools for assessing COPD. This study aimed to compare the D(LCO) and forced expiratory volume in one second (FEV(1)) to identify better prognostic factors for admitted patients with AECOPD. METHODS: We retrospectively analyzed 342 patients with AECOPD receiving inpatient treatment. We classified 342 severe AECOPD patients by severity of D(LCO) and FEV(1) (≤ vs. > 50% predicted). We tested the association of FEV(1) and D(LCO) with the following outcomes: in-hospital mortality, need for mechanical ventilation, need for intensive care unit (ICU) care. We analyzed the prognostic factors by multivariate analysis using logistic regression. In addition, we conducted a correlation analysis and receiver operating characteristic (ROC) curve analysis. RESULTS: In multivariate analyses, D(LCO) was associated with mortality (odds ratio = 4.408; 95% CI 1.070–18.167; P = 0.040) and need for mechanical ventilation (odds ratio = 2.855; 95% CI 1.216–6.704; P = 0.016) and ICU care (odds ratios = 2.685; 95% CI 1.290–5.590; P = 0.008). However, there was no statistically significant difference in mortality rate when using FEV(1) classification (P = 0.075). In multivariate linear regression analyses, D(LCO) (B = − 0.542 ± 0.121, P < 0.001) and FEV(1) (B = − 0.106 ± 0.106, P = 0.006) were negatively associated with length of hospital stay. In addition, D(LCO) showed better predictive ability than FEV(1) in ROC curve analysis. The area under the curve (AUC) of D(LCO) was greater than 0.68 for all prognostic factors, and in contrast, the AUC of FEV(1) was less than 0.68. CONCLUSION: D(LCO) was likely to be as good as or better prognostic marker than FEV(1) in severe AECOPD.

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