A multidimensional grading system (BODE index) as predictor of hospitalization for COPD

Kian-Chung Ong, Arul Earnest, Suat-Jin Lu
Chest 2005, 128 (6): 3810-6

STUDY OBJECTIVES: We hypothesized that the BODE (body mass index, airflow obstruction, dyspnea, and exercise capacity) index would better predict hospitalization for COPD than FEV1 alone, and the purpose of this study was to test this hypothesis in a cohort of patients with COPD.

DESIGN: Historical cohort study.

SETTING: University-affiliated hospital.

PATIENTS: One hundred twenty-seven patients with COPD recruited from the outpatient clinic of a single institution were followed up for a mean period of 16.2 months.

MEASUREMENTS: The BODE index was calculated for each patient using variables obtained within 4 weeks of enrollment. The main outcome measure was the number of hospital admissions for COPD during follow-up. We used the Poisson regression model to quantify and compare the relationship between FEV1 and BODE scores with the number of hospital admissions.

RESULTS: During the follow-up period, 47% of patients required at least one hospital admission and 17% died. Using Poisson regression analysis, a significant effect of BODE score on the number of hospital admissions was found (incidence rate ratio, 1.20; 95% confidence interval [CI], 1.15 to 1.25; p < 0.001). In comparison, there was a significant but smaller effect of the FEV1 percentage of predicted on the number of hospital admissions (incidence rate ratio, 0.08; 95% CI, 0.04 to 0.16; p < 0.001). When categorizing the BODE scores into four quartiles, we found that the BODE index is also a better predictor of hospital admissions than the staging system of COPD as defined by the Global Initiative for Chronic Obstructive Lung Disease. The pseudo r2 using quartiles of the BODE index as the predictor was 0.16, as compared to 0.04 for stages of severity based on FEV1.

CONCLUSIONS: The BODE staging system, which includes in addition to FEV1 other physiologic and clinical variables, helps to better predict hospitalization for COPD.

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