Assessing the importance of predictors in unplanned hospital readmissions for chronic obstructive pulmonary disease

Abstract

Purpose: The all-cause readmission rate within 30 days of index admissions for chronic obstructive pulmonary disease (COPD) was approximately 21% in the United States in 2008. This study aimed to examine patient and clinical characteristics predicting 30-day unplanned readmissions for an initial COPD hospitalization and to determine those predictors’ importance. Conclusion: Overall, COPD severity score and heart failure/heart disease emerged as important factors in predicting 30-day unplanned readmissions across all three outcomes. Results from dominance analysis suggest looking beyond COPD-specific complications and focusing on comorbid conditions highly associated with COPD in order to lower all-cause unplanned readmissions. Results: After applying the inclusion and exclusion criteria, 18,282 patients with index COPD-related admissions were identified. Among them, the rates of unplanned readmissions with COPD as primary diagnosis, COPD as any diagnosis, and all-cause were 2.6%, 5.6%, and 7.3%, respectively. For each outcome, the readmission group was slightly older, had a greater COPD severity score, and required a longer length of stay. Moreover, the readmission group had larger proportions of patients with comorbidities, dyspnea/shortness of breath, intensive care unit stay, or ventilator use, compared to the non-readmission group. Dominance analysis revealed that the three most important predictors – heart failure/heart disease, anemia, and COPD severity score – accounted for 56% of the predicted variance in all-cause unplanned readmissions. Patients and methods: A retrospective study was conducted in patients with COPD-related hospitalizations using commercial claims data from 2010 to 2012. The primary outcome was all-cause unplanned readmission, with secondary outcomes being COPD as primary diagnosis and COPD as any diagnosis unplanned readmissions. Factors predicting unplanned readmissions encompassed demographic, pharmacy, and medical variables identified at baseline and during the index hospitalization. Dominance analysis was conducted to rank the predictors in terms of importance, defined as the contribution to change in model ft of a predictor by itself and in combination with other predictors.