Identifying Patients With COPD at High Risk of Readmission

Abstract

Background: Readmission within 30 days of a COPD hospitalization is a common measure of performance for COPD care. However, most studies of COPD readmission risk have been constrained to a single data source, private payer claims, or Medicare claims data, making it difficult to generalize results from these studies to other populations. The purpose of this study was to examine the risk for readmission within 30 days from time of discharge in patients with COPD using the Healthcare Cost and Utilization Project (HCUP) State Inpatient Database for California for the years [2005][2006][2007][2008][2009][2010][2011]. This statewide dataset allows us to consider all readmissions for COPD regardless of age or payer status. Methods: The total dataset included 28,265,070 visits among 17,918,374 patients over 480 hospitals. We identified patients with a hospitalization, a primary diagnosis related to COPD, age 40 or older, and discharged alive. We found 286,313 hospitalizations that matched this definition and included information on covariates such as comorbidities, age, and insurance status. To characterize the joint associations of these covariates with readmission within 30 days, we used a generalized linear model. Results: Patients aged 40-64 are more likely to be readmitted to the hospital within 30 days of a COPD-related hospitalization than patients 65 and older. This effect persists after adjustment for patient severity, comorbidities, payer, and demographics. Our model featured an interaction of age with insurance type. We found that younger patients (aged 40-64) on public insurance have the highest readmission rates: 14.77% for Medicare and 16.27% for Medicaid. However, younger patients with private insurance have the lowest readmission rates at 8.25%. Additional significant covariates included whether or not the patient left against medical advice, and diagnoses of congestive heart failure and diabetes. In addition, we found that although admissions for COPD were highest in the winter, this is not true for COPD readmissions, which peak in summer. Also, inpatient mortality for patients admitted for COPD decreased from approximately 3% to 1.25% over the study period. Conclusion: Our results demonstrate that many of the risk factors for readmission may be dependent on the data source used. Furthermore, many of the strongest predictors are clearly related to the patients themselves. This observation may help explain why prior programs to reduce readmissions have had limited success. AbstractAbbreviations: chronic obstructive pulmonary disease,

<p>Multi-morbidities are Not a Driving Factor for an Increase of COPD-Related 30-Day Readmission Risk</p>

“…30 Second, by using SID and Elixhauser comorbidities variables, previous studies have shown that among comorbid COPD patients-including congestive heart failure, lung cancer, weight loss, deficiency anemias, hypertension, diabetes, hypothyroidism, lymphoma, alcohol abuse, obesity, chronic kidney disease and obstructive sleep apnea-the risk of 30-day all-cause or COPD-related readmission did not differ from patients without comorbidities. 13,16,17 One possible explanation is that a primary COPD diagnosis among comorbid patients could motivate them to modify their health behaviors by trying to increase physical activities and monitoring their diet. 38 Furthermore, COPD patients with comorbidities often consulted family physicians more, which may in turn have resulted in better health outcomes.…”

“…Individual-level factors, obtained from HCUP SID, included gender, race, age and types of primary insurance payer. 7,16,31 Hospital-level factors included number of beds; teaching hospital; residency training approval hospital; hospital location and Medicare Accountable Care Organization (ACO) types: Pioneer or Medicare Shared Savings Program (MSSP); 32,33 information collected from the AHA Annual Survey and the level of implementation of health information technology, collected from the Health Information and Management System Society. Geographic level factors consisted of median household income (reported by quartiles of the patient's ZIP code and acquired from AHRF), Herfindahl-Hirschman index (defined as the sum of square market shares of inpatient days measured in a county, 34 which was obtained from AHA survey), and state-fixed effects.…”

“…Even though comorbidities are important factors in predicting the risk of 30-day readmission, 10,14 the management and the medical intervention in COPD patients with comorbidities is not clearly established worldwide. 15 Research has shown that COPD comorbid with other diseases, such as heart disease, 16,17 depression 16,17 and diabetes, 18 could increase the risk of 30-day readmission. However, none of these studies were representative, and none took multimorbidity into consideration.…”

“…However, none of these studies were representative, and none took multimorbidity into consideration. [16][17][18] Two studies analyzed only one state within the US (one in Florida and the other in California), 16,17 and the other study only used data from private health insurance plans. 18 Furthermore, they solely considered the impact of one comorbidity and did not evaluate how the increasing number of comorbidities would affect 30-day readmission.…”

Assessment of symptom burden and adherence to respiratory medications in individuals self-reporting a diagnosis of COPD within a community pharmacy setting

“…38 Reasons for low adherence observed here are unclear. The prevalence of obesity and use of Medicaid (a program helping to pay health care costs for patients with limited income) were high in this population compared with COPD populations in other studies [39][40][41] and the general population (44.9% of participants were obese vs. an estimated prevalence of 39.8% among U.S. adults in 2015e2016, 42 and 47.1% of participants had Medicaid coverage vs. approximately 19.4% of the U.S. population in 2016). 43 These observations might suggest a Table 2 Distributions of participants according to the GOLD 2016 classifications of airflow limitation, mMRC and CAT scores, and exacerbation history (percentages of evaluable sample unless otherwise stated) 25 682) and out of the subgroup of participants with available spirometry data (n ¼ 251).…”