Factors Associated with Differential Readmission Diagnoses Following Acute Exacerbations of Chronic Obstructive Pulmonary Disease
BACKGROUND: Readmissions after exacerbations of chronic obstructive pulmonary disease (COPD) are penalized under the Hospital Readmissions Reduction Program (HRRP). Understanding attributable diagnoses at readmission would improve readmission reduction strategies.
OBJECTIVES: Determine factors that portend 30-day readmissions attributable to COPD versus non-COPD diagnoses among patients discharged following COPD exacerbations.
DESIGN, SETTING, AND PARTICIPANTS: We analyzed COPD discharges in the Nationwide Readmissions Database from 2010 to 2016 using inclusion and readmission definitions in HRRP.
MAIN OUTCOMES AND MEASURES: We evaluated readmission odds for COPD versus non-COPD returns using a multilevel, multinomial logistic regression model. Patient-level covariates included age, sex, community characteristics, payer, discharge disposition, and Elixhauser Comorbidity Index. Hospital-level covariates included hospital ownership, teaching status, volume of annual discharges, and proportion of Medicaid patients.
RESULTS: Of 1,622,983 (a weighted effective sample of 3,743,164) eligible COPD hospitalizations, 17.25% were readmitted within 30 days (7.69% for COPD and 9.56% for other diagnoses). Sepsis, heart failure, and respiratory infections were the most common non-COPD return diagnoses. Patients readmitted for COPD were younger with fewer comorbidities than patients readmitted for non-COPD. COPD returns were more prevalent the first two days after discharge than non-COPD returns. Comorbidity was a stronger driver for non-COPD (odds ratio [OR] 1.19) than COPD (OR 1.04) readmissions.
CONCLUSION: Thirty-day readmissions following COPD exacerbations are common, and 55% of them are attributable to non-COPD diagnoses at the time of return. Higher burden of comorbidity is observed among non-COPD than COPD rehospitalizations. Readmission reduction efforts should focus intensively on factors beyond COPD disease management to reduce readmissions considerably by aggressively attempting to mitigate comorbid conditions.
© 2020 Society of Hospital Medicine
METHODS
Data Source
The Nationwide Readmissions Database (NRD) is a nationally representative, all-payer, 100% sample of community acute care hospital discharges from multiple states.30 We pooled COPD discharge records spanning 2010-2016, excluding those where the patients were not residents of the state in which they were hospitalized to minimize loss to follow-up.
Inclusion/Exclusion Criteria
Selection criteria mirrored the methodology used by the HRRP,31,32 defining an index discharge as a patient ≥40 years of age with a qualifying COPD diagnosis (Appendix Tables 1-2), discharged alive, with at least 30 days elapsed since previous hospitalization. We excluded discharges against medical advice or those from a hospital with fewer than 25 COPD discharges in that calendar year as per HRRP,31,32 as well as those involving lung transplants. In this pooled cross-sectional analysis, record identifiers were not reliably unique across years. We restricted to observations originating February-November because January stays may not have had the requisite HRRP 30-day washout period from last admission and December stays could not be tracked into the subsequent January.
Outcomes
We defined a readmission as subsequent hospitalization for any cause within 30 days of the index discharge, with exemptions defined by the HRRP (Appendix Figure 1).31,32 We segmented the readmission outcome into two parts: those readmitted with diagnoses that met the COPD HRRP criteria versus for any other diagnoses. We also tabulated diagnosis-related groups (DRGs) coded for the readmission observation to capture attributable cause for rehospitalization.
Our main independent variable was the Elixhauser Comorbidity Index score,33 constructed using adaptations of published software,34,35 having previously validated its use for modeling COPD readmissions.36 We involved covariates provided with the database, including sociodemographic variables (eg, age, sex, community characteristics, payer, and median income at patient’s ZIP code) and hospital characteristics (eg, size, ownership, teaching status). We constructed additional covariates to account for in-hospital events by aggregating ICD diagnosis and procedure codes (eg, mechanical ventilation), hospital discharge volume, and proportion of annual within-hospital Medicaid patient days as a surrogate marker for safety-net hospitals. A detailed explanation of database construction and selection criteria is found in the Supplemental Methods Appendix.
Statistical Analysis
We tabulated patient-level descriptive statistics across the three outcomes of interest (ie, not readmitted, readmitted for a stay that would have qualified as COPD-related by HRRP criteria and readmitted for any other diagnosis). Continuous variables were compared using Welch’s t-tests (ie, unequal variance) and categorical variables using Chi-squared tests. At the hospital level, we tabulated the proportions of hospitals within categories in key variables of interest and a sub-population readmission rate for that particular characteristic, compared using Chi-squared tests.
We fit a multilevel multinomial logistic regression with random intercepts at the hospital cluster level, with the tripartite readmission outcome described above with “not readmitted” as the reference group. We included fixed effects for year, Elixhauser score, and patient- and hospital-level covariates as described above. Time to readmission for each group was plotted to assess the time distribution for each outcome. In-hospital mortality during each readmission event was tabulated.
