Among patients admitted for chronic obstructive pulmonary disease (COPD) at Veterans Affairs hospitals, hospital-level length of stay was not associated with 30-day readmission.
Objectives: Recent financial penalties for high risk-adjusted chronic obstructive pulmonary disease (COPD) readmissions are causing hospitals to search for ways to reduce COPD readmissions. Although some have advocated for increasing the length of stay (LOS) as a method to decrease readmissions, the association between LOS and readmission is unclear. Our primary objective was to examine the association between LOS and readmission among patients admitted for COPD.
Study Design: We conducted an observational study of 33,558 veterans admitted to 130 Veterans Affairs hospitals for COPD from October 1, 2008, to September 30, 2011.
Methods: We used multivariable regression to separately examine the associations of patient and hospital LOS with 30-day all-cause readmission.
Results: At the patient level, compared with short LOS (<3 days), a longer LOS was associated with increased risk for readmission. The adjusted odds ratio was 1.39 (95% confidence interval [CI], 1.18-1.63) for medium LOS (3-4 days) and 2.03 (95% CI, 1.72-2.40) for long LOS (>4 days). On the hospital level, there was no association between LOS and readmission.
Conclusions: On a patient level, a longer LOS for COPD hospitalizations was associated with higher risk for readmission, which is likely confounded by the severity of the illness. On a hospital level, LOS was not associated with readmission. These findings imply that, independent of other transitional care practices, altering the hospital LOS may not influence the risk of readmission.
Am J Manag Care. 2017;23(8):e253-e258
In a nationwide study of patients admitted for chronic obstructive pulmonary disease (COPD) to Veterans Affairs hospitals, we examined the association between length of stay (LOS) and readmission on the patient level and the hospital level separately.
With healthcare costs continuing to rise, health systems are searching for ways to improve value-based care. Recently, CMS included readmissions for chronic obstructive pulmonary disease (COPD) in the Hospital Readmission Reduction Program, in which hospitals with excessive risk-adjusted readmission rates are financially penalized.1 Some have argued that keeping patients in the hospital longer would allow more time for optimal medical management and reduce the risk for readmission.2 However, the relationship between length of stay (LOS) and readmission is still unclear, and it is unknown whether increasing LOS is associated with lower readmission rates.
COPD is one of the most common chronic diseases leading to hospitalization in the United States.3 There are more than 700,000 COPD hospitalizations in the United States each year, accounting for more than $6 billion in healthcare spending4,5; average LOS for patients admitted with COPD varies widely between hospital systems.6,7 Furthermore, clinical trials on COPD hospitalizations have shown that early hospital discharge with home healthcare can effectively reduce LOS as well as readmissions in select patients.8-10 These findings suggest that opportunities may exist to safely improve hospital efficiency by reducing LOS for COPD.
Observational studies examining the relationship between LOS and readmission are frequently confounded by the severity of the illness,11,12 which makes it difficult to study this association. Patients who are sicker tend to require longer LOS and have higher risk of readmission. Several studies have attempted to overcome these challenges by observing variations in readmission rates corresponding to temporal trends in LOS.13-15 However, these studies have been unable to account for other temporal trends in clinical practice that may impact the outcome. Randomized trials on LOS could provide additional evidence, but they would be methodologically challenging to design and conduct.
The primary objective of this study was to examine the association between LOS and all-cause 30-day hospital readmission among patients who were admitted to Veterans Affairs (VA) hospitals for COPD. We first examined how individual patient LOS was associated with patient-level readmission risk. To determine the impact of organizational influences on patient outcomes, we then assessed how median hospital LOS was associated with patient-level readmission risk. Lastly, we examined the relationship between hospital-level risk-adjusted readmission rates and risk-adjusted LOS.
Study Design and Population
We performed an observational study of patients admitted to VA hospitals for COPD during the fiscal years 2009 to 2011 (October 1, 2008, to September 30, 2011). We only included veterans who had least 1 year of VA care prior to index hospitalization. We identified 38,128 unique patients with COPD admissions by a principal International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) discharge diagnosis of COPD (491.0, 491.1, 491.2, 491.8, 491.9, 492.0, 492.8, 493.2, and 496), which is a validated approach that has been used in other VA studies.16 We excluded hospitals that admitted fewer than 25 patients for COPD during the study period (n = 11) to ensure stability in hospital-level covariate measures and readmission rates. We also excluded patients who died during hospitalization (n = 518), were discharged to a nursing hospital (n = 683), were transferred to a non-VA hospital (n = 2773), had an LOS of less than 1 day (n = 502), or had an LOS longer than 30 days (n = 83). Patients who died within 30 days of hospital discharge represented a small fraction of the total sample (n = 710), and they were included in the analysis. Repeating the analysis after excluding these patients did not substantially change our findings. Data on readmissions were collected from VA administrative databases and Medicare claims. The VA Puget Sound and VA Connecticut Healthcare Systems institutional review boards approved the study.
The 2 independent variables of interest were: 1) patient LOS and 2) hospital median LOS. Patient LOS was calculated as the difference between the calendar date of admission and discharge. Given the skewness of LOS, we classified patient-level LOS into short (<3 days), medium (3-4 days), and long (>4 days), based on tertiles. Hospital LOS was calculated as the median LOS for all patients with COPD admitted within each hospital and was analyzed separately to determine the impact of organizational variation in LOS. To avoid the assumption that the relationship between hospital LOS and readmission was linear, hospital LOS was also broken into 3 categories for analysis. Given that the majority of patients were admitted to a hospital that had a median LOS of 3 days, we examined the following categories: fewer than 3 days, 3 days, and more than 3 days.
The primary outcome of interest was hospital readmission for any cause within 30 days of discharge from index hospitalization. This outcome is consistent with the focus of the Hospital Readmissions Reduction Program (HRRP) on improving diverse readmissions. For each patient, only the first readmission within 30 days after index hospitalization was included in the analysis. Readmission data was collected from VA electronic health records and Medicare claims.
We controlled for baseline patient characteristics including age, gender, race, marital status, distance to index hospital, Medicare and Medicaid status, co-payment status, number of home zip codes in the year prior to index hospitalization (1 vs >1), and discharge against medical advice in the prior year. To measure severity of illness, we included more than 30 diseases using inpatient and outpatient ICD-9-CM codes from the year prior to index hospitalization based on a previously validated risk score.17 The comorbidities included multiple conditions that frequently affect patients with COPD, such as heart disease, pulmonary vascular disease, and cancer. Each comorbidity was included in the model separately, and we also included an overall measure of comorbidity burden prior to index hospitalization using the Hierarchical Condition Category (HCC).18
The number of VA hospitalizations and clinic visits in the prior year were divided into quartiles and included as covariates in the analyses. Information on baseline COPD therapy included number of short-acting beta-agonist (SABA) canisters dispensed and prescriptions (yes/no) for nebulized SABAs, ipratropium, tiotropium, long-acting beta-agonists (LABAs), inhaled corticosteroids, and oral steroids in the year prior to index hospitalization. We did not have information on combined LABAs and inhaled corticosteroid medications. We also included information on whether patients were started on new invasive or noninvasive mechanical ventilation during index hospitalization as a proxy for severity of illness. Finally, to account for differences in LOS and readmission rates over time, we included the fixed effects of the fiscal year of patient discharge in the analyses.
We conducted unadjusted analyses, including χ2 tests and t tests, to examine the association between LOS and readmission at the patient level and hospital level. We then estimated separate mixed effects multivariable logistic regression models to examine: 1) patient-level LOS and 2) hospital-level LOS. We calculated all-cause risk-adjusted readmission rates for each hospital using multivariable logistic regression and calculated risk-adjusted LOS using multivariable negative binomial regression. For both risk-adjusted models, we controlled for patient characteristics, included fixed effects for the hospital, and clustered at the hospital level. Risk-adjusted rates were predicted for each hospital, with patient characteristics held constant at national mean levels. We graphed the hospital-level risk-adjusted readmission rate and risk-adjusted LOS against each other and calculated Pearson’s correlation coefficient.
In a sensitivity analysis, we repeated our regression analyses to examine the association between LOS and all-cause 30-day readmission among the frailest populations in our cohort, including: 1) patients older than 75 years and 2) patients who had the most comorbidities based on the highest HCC quartile. We also repeated our analysis using COPD-specific 30-day readmissions.
We included 33,558 patients who were admitted for COPD at 130 hospitals in our analyses. The mean patient LOS was 3.99 days (standard deviation [SD] = 4.06). There was notable skewness in patient LOS (Figure 1). Dividing patient LOS into tertiles yielded 13,194 patients who had an LOS shorter than 3 days; 10,760 patients who had an LOS of 3 to 4 days; and 9604 patients who had an LOS longer than 4 days. There was less skewness in median hospital LOS (Figure 2). Dividing hospital LOS into 3 categories yielded 6414 patients at 25 hospitals with a median LOS shorter than 3 days; 20,506 patients at 70 hospitals with a median LOS of 3 days; and 6638 patients at 35 hospitals with a median LOS longer than 3 days.
Patients who had a longer LOS tended to be older, were more likely to be unmarried, were more likely to be white, lived farther from the hospital, had more prior hospitalizations, and had more comorbidities (Table 1). Longer LOS was also associated with more severe markers of COPD, including higher numbers of prior COPD medications and more frequent use of mechanical ventilation during index hospitalization. In unadjusted analyses, patient LOS was associated with increased 30-day readmission (15.9% for <3 days LOS vs 21.5% for >4 days LOS; P <.001) (Table 2). In contrast, there was no significant association between hospital median LOS and readmission.
In multivariable regression models with patient LOS as the independent variable of interest, longer LOS was associated with increased risk for readmission. Relative to short-LOS patients, the odds of readmission were 1.39 (95% confidence interval [CI], 1.18-1.63) and 2.03 (95% CI, 1.72-2.40) times greater for medium- and long-LOS patients, respectively. In contrast, multivariable logistic regression with hospital LOS as an independent variable did not reveal a significant association with readmission. Relative to patients discharged from short-LOS hospitals, the odds of readmission were 1.12 (95% CI, 0.91-1.38) and 1.12 (95% CI, 0.86-2.04) times greater for patients discharged from medium- and long-LOS hospitals, respectively. Graphing the risk-adjusted readmission rate against risk-adjusted LOS did not reveal a significant association between the 2 variables (Pearson correlation = 0.14; P = .10) (Figure 3).
Results from sensitivity analyses coincided with our primary results. Among 10,376 patients older than 75 years, we found increased risk of readmission on a patient level (medium LOS: odds ratio [OR], 1.51; 95% CI, 1.15-2.00; long LOS: OR, 2.34; 95% CI, 1.78-3.10), but no significant increased risk on a hospital level (medium LOS: OR, 0.91; 95% CI, 0.64-1.31; long LOS: OR, 1.03; 95% CI, 0.71-1.52). Among 8290 patients in the highest HCC quartile, we also found increased risk of readmission on a patient level (medium LOS: OR, 1.63; 95% CI, 1.23-2.16; long LOS: OR, 1.84; 95% CI, 1.39-2.44), but no significant increased risk on a hospital level (medium LOS: OR, 1.47; 95% CI, 0.97-2.23; long LOS: OR, 1.26; 95% CI, 0.80-1.98). Repeating the analysis using COPD-specific 30-day readmissions also did not reveal substantial differences in our results (data not shown).
This study showed that on a patient level, longer LOS for COPD hospitalizations were significantly associated with a higher risk for readmission, which is likely explained by unmeasured sociodemographic factors and greater severity of illness. Patients who are sicker and have less social support tend to have longer LOS and greater risk for readmission. In our study, patients who had longer LOS tended to be older and unmarried, to live farther from the VA hospital, and to have more comorbidities, prior hospitalizations, prescriptions for COPD medications, and greater use of mechanical ventilation during index hospitalization. Even after controlling for these variables, we still found a significant association between patient LOS and readmission, which most likely reflects residual confounding. After using the median hospital LOS in the model, we found no association between LOS and readmission risk. This was true not only in our general analysis, but also in our sensitivity analysis, which examined the association between LOS and readmission for the frailest patients. Variations in median hospital LOS likely reflect differences in organizational culture and practices that impacted decisions to keep patients in the hospital for longer periods of time. Our findings suggest that a strategy of simply keeping patients in the hospital longer is not likely to be an effective approach for reducing the risk of readmissions.
There is limited research on the association between LOS and hospital readmission. A few studies have examined this relationship using temporal trend analyses.13-15 They found that although LOS decreased for several conditions in the 1990s and early 2000s, there were no corresponding increases in readmission rates. However, during this same period of time, several changes may have independently reduced readmissions. For example, there was a growing financial focus on reducing excess readmissions that resulted in the CMS implementing the HRRP to financially penalize hospitals with high risk-adjusted readmission rates.1 Improvements in quality of outpatient chronic disease management may also affect readmissions, independent of hospital LOS.19
We examined the relationship between LOS and readmission on both the patient level and hospital level, independent of temporal trends. We saw notable variation in hospital LOS, even after adjusting for baseline characteristics of the patient population; however, we did not find an association between hospital LOS and readmission rates. In contrast, a Norwegian study of elderly patients admitted in 1996 found that hospitals with shorter LOS had higher readmission rates.20 There are several reasons that we may have found different results from the Norwegian study. First, it is probable that there is an appropriate therapeutic window for hospital LOS. Reducing hospital LOS below a threshold may lead to adverse postdischarge outcomes, including higher readmissions. Second, the Norwegian study occurred in a different setting and at a different time. There are notable differences in the healthcare delivery systems that make cross-cultural comparisons challenging. Third, all patients in our study were admitted with an index hospitalization for COPD, which may represent a unique disease that offers more opportunity to reduce LOS. Multiple studies have identified home health interventions that effectively reduce LOS for COPD without increasing the risk for readmission.8,9,21 Wider application of these interventions could improve hospital efficiency for COPD.
Our study has several limitations. We did not have spirometric confirmation of COPD in our dataset, and although we used a validated approach of identifying veterans with COPD, the cause of index hospitalization may have been misclassified. The dataset did not include patients who had a primary discharge diagnosis of respiratory failure and a secondary diagnosis of COPD, thus limiting detection of severe COPD admissions.22 The dataset also lacked key baseline variables, including smoking status and oxygen use. We also did not have access to laboratory, vital signs, or therapy data during index admission necessary to calculate a comprehensive measure of COPD severity. Hospitals could have systematically admitted less severely ill patients, which would impact both the patient LOS and the risk of subsequent readmission. We controlled for baseline patient characteristics and mechanical ventilation during index hospitalization, and we conducted a sensitivity analysis on the frailest patients, but other variables relating to severity of COPD exacerbation could have impacted the outcome. In addition, hospital-level practices and characteristics could have impacted LOS and readmission. For example, rather than reflecting a tendency to delay discharge until patients have returned closer to baseline, a longer LOS may be a marker of less organized inpatient care management and planning, so that any benefit derived from a longer LOS would be negated by less effective transitional care planning. All patients in our study had an index hospitalization to a VA hospital. VA hospitals represent a unique environment devoid of the same financial incentives and pressures that other healthcare systems have in place to reduce LOS or readmissions.
The results of this study add to the evidence on the relationship between LOS and readmission. Although longer patient LOS was associated with increased risk for readmission, median hospital LOS was not associated with readmission. These findings suggest that practices influencing hospital LOS may not impact readmission. Further studies are needed to explore the organizational factors associated with LOS and how these factors can be modified to safely improve hospital efficiency.
Author Affiliations: CHOIR, Edith Nourse Rogers Memorial VA (STR); The Pulmonary Center, Boston University School of Medicine (STR); PRIME Center, VA Connecticut Healthcare System Department of Veterans Affairs (LAB), New Haven, CT; Department of Medicine, Yale University (LAB), New Haven, CT; Health Services Research and Development, VA Puget Sound Healthcare System, Department of Veterans Affairs (MCG, ESW, PLH, C-FL), Seattle, WA; Department of Health Services, University of Washington (MCG, ESW, PLH, C-FL), Seattle, WA; Department of Medicine, Baystate Medical Center (PKL), Springfield, MA.
Source of Funding: This work was supported by Veterans Affairs clinical research grant IIR 09-354. Dr Wong was supported by a VA Career Development Award. The views expressed here are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs and their affiliated institutions.
Author Disclosures: Dr Wong has ownership of stock in the SPDR Exchange Traded Fund. All other authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (STR, ESW, CFL, PLH); acquisition of data (STR, CFL); analysis and interpretation of data (STR, ESW, CFL, PLH); drafting of the manuscript (STR, LAB); critical revision of the manuscript for important intellectual content (STR, PKL, ESW, LAB, CFL, PLH); statistical analysis (STR, ESW, PLH); provision of patients or study materials (STR); obtaining funding (STR, CFL); administrative, technical, or logistic support (STR, CFL); and supervision (LAB, CFL).
Address Correspondence to: Seppo T. Rinne, MD, PhD, 200 Springs Rd, Bedford, MA 01730. E-mail: Seppo.Rinne@va.gov.
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