Factors associated with a prolonged length of stay after acute exacerbation of chronic obstructive pulmonary disease (AECOPD)

Abstract

Background

Early identification of patients with a prolonged stay due to acute exacerbation of chronic obstructive pulmonary disease (COPD) may reduce risk of adverse event and treatment costs. This study aimed to identify predictors of prolonged stay after acute exacerbation of COPD based on variables on admission; the study also looked to establish a prediction model for length of stay (LOS).

Methods

We extracted demographic and clinical data from the medical records of 599 patients discharged after an acute exacerbation of COPD between March 2006 and December 2008 at Oslo University Hospital, Aker. We used logistic regression analyses to assess predictors of a length of stay above the 75th percentile and assessed the area under the receiving operating characteristic curve to evaluate the model’s performance.

Results

We included 590 patients (54% women) aged 73.2±10.8 years (mean ± standard deviation) in the analyses. Median LOS was 6.0 days (interquartile range [IQR] 3.5–11.0). In multivariate analysis, admission between Thursday and Saturday (odds ratio [OR] 2.24 [95% CI 1.60–3.51], P<0.001), heart failure (OR 2.26, 95% CI 1.34–3.80), diabetes (OR 1.90, 95% CI 1.07–3.37), stroke (OR 1.83, 95% CI 1.04–3.21), high arterial PCO₂ (OR 1.26 [95% CI 1.13–1.41], P<0.001), and low serum albumin level (OR 0.92 [95% CI 0.87–0.97], P=0.001) were associated with a LOS >11 days. The statistical model had an area under the receiver operating characteristic curve of 0.73.

Conclusion

Admission between Thursday and Saturday, heart failure, diabetes, stroke, high arterial PCO₂, and low serum albumin level were associated with a prolonged LOS. These findings may help physicians to identify patients that will need a prolonged LOS in the early stages of admission. However, the predictive model exhibited suboptimal performance and hence is not ready for clinical use.

Keywords:

• exacerbation
• COPD
• predictors
• length of stay
• predicting model

Acknowledgments

This study was supported by Grant 2779038 from the South-Eastern Norway Regional Health Authority and Grant 2779010 from the Research Council of Norway.

Disclosure

The authors report no conflicts of interest in this work.