期刊
JOURNAL OF GENERAL INTERNAL MEDICINE
卷 24, 期 9, 页码 1043-1048出版社
SPRINGER
DOI: 10.1007/s11606-009-1061-2
关键词
body mass index; chronic obstructive pulmonary disease; dyspnoea; length of hospital stay; mortality
资金
- Thorax Foundation, Athens, Greece
BACKGROUND: The outcome after hospitalization for an exacerbation of chronic obstructive pulmonary disease (COPD) is unfavorable and uncertainty exists about factors predicting short and long-term prognosis. OBJECTIVE: To identify clinical predictors of length of hospital stay (LOS) and three-year mortality after COPD exacerbations requiring hospitalization. DESIGN: Retrospective analysis of prospectively collected data. PARTICIPANTS AND METHODS: All consecutive patients hospitalized with COPD exacerbation were enrolled. Disease severity was estimated by FEV1, body mass index (BMI), Medical Research Council (MRC) chronic dyspnoea scale, previous hospitalizations, need for long-term oxygen treatment (LTOT), arterial oxygen and carbon dioxide partial pressures (PaO2 and PaCO2), pH and respiratory rate. Outcome was assessed by LOS and three-year mortality. MAIN RESULTS: Out of 81 patients enrolled, three-year mortality data were available for 61. LOS was related to BMI, MRC scale and respiratory rate. Three-year mortality was related to FEV1, BMI, MRC scale, LTOT, and PaCO2. Multiple logistic regression analysis demonstrated that MRC scale was the only independent determinant of LOS, [p=0.001, odds ratio (OR) 7.67 (95% CI 2.50-23.41)], whereas MRC scale and BMI predicted three-year mortality, [p=0.001, OR 8.28 (95% CI 2.25-30.47) and p=0.006, OR 6.91 (95% CI 1.74-27.48), respectively]. Cox regression analysis demonstrated identical results. Using receiver-operator-optimized thresholds for these variables (MRC > 2 and BMI < 25 kg/m(2)), we propose a prediction model that accurately determines three-year mortality risk. CONCLUSIONS: In this study, MRC scale and BMI predicted outcome after COPD hospitalization. Pending further validation, this predictive model may contribute to identify patients with poor outcome even when spirometric data are unavailable.
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