Differences Between Early and Late Readmissions Among Patients

Context Patient readmissions are used to evaluate the quality of hospital care. Whether early or late readmissions after discharge reflect equally on in-hospital care is unknown. Contribution This study found that readmissions within 7 days after discharge were associated with variables related to illness acuity and inpatient care processes, whereas those occurring later were associated with markers of chronic disease burden. Implication Early and late readmissions are not the same. Their value as hospital care quality indicators and the efforts that might prevent them may also differ. One in 5 Medicare beneficiaries who have been discharged from a hospital will be readmitted within 30 days, costing the U.S. health care system $17.6 billion per year (1). As a result, 30-day readmission rates have become a quality metric intended to gauge inpatient quality of care and unnecessary health care use, with formal public reporting beginning in 2009. Section 3025 of the Patient Protection and Affordable Care Act established the Hospital Readmissions Reduction Program (2), which became effective 1 October 2012, and requires the Centers for Medicare & Medicaid Services to reduce payments to hospitals with excess readmissions. Despite face validity, the readmission metric has fueled controversy. An ideal quality metric would accurately and reliably predict outcomes, avoid unintended consequences, and represent something preventable. Recent evidence demonstrates that hospitals with the greatest adherence to evidence-based care processes do not consistently achieve the lowest readmission rates (3) and that these rates are only weakly associated with, or are even inversely related to, inpatient mortality rates (4, 5). These factors question the metric's utility. Recent reports have also shown that since the inception of the Hospital Readmissions Reduction Program, hospitals caring for the most disadvantaged patients bear the most penalties (6). The small amount of literature on preventability suggests that preventable readmissions are rare (4, 79) and that it decreases within 30 days after discharge (9), leading experts to propose that a more appropriate window for this quality metric would be 3 to 7 days (8, 10, 11). Whether the 30-day window actually represents a homogeneous period after discharge has not been studied. Therefore, we aimed to evaluate correlates of readmission within the early (0 to 7 days after discharge) and late (8 to 30 days after discharge) periods in an internal medicine population to evaluate whether risk factors for readmission differ within this time frame. We hypothesized that early readmissions were associated with factors related to the index admission, including acute illness burden, inpatient care process factors, and markers of clinical instability at discharge. We also hypothesized that late readmissions were more closely associated with chronic illness burden and social determinants of health, which were less related to the index admission. Our goal was to provide the data necessary to refine readmission prevention strategies for these 2 distinct time frames. Methods Setting and Study Population This is a retrospective, single-center cohort study set in a large urban teaching hospital. The hospital's institutional review board approved the study with a waiver of informed consent. We included all admissions (including observation status admissions) from our hospital's primary care network from 1 January 2009 to 31 December 2010. Of note, because the medical record number was not the unit of observation, patients could be represented more than once if they were readmitted at any time during the observation period. A readmission was counted only once as a readmission, relative to the previous index admission, then reentered into the cohort as a new index admission. Admissions followed by an elective readmission or death within 30 days of discharge were excluded from the data set. We also excluded patients who were discharged to long-term acute care hospitals and in-house psychiatry transfers because the nature of health care use after discharge was believed to qualitatively differ from that of patients discharged in stable condition. Data Sources We derived our cohort and variables of interest using the hospital's clinical and administrative data repositories; professional fee billing repository; electronic provider order entry data; and our electronic initial nursing assessment tool, which was used to collect clinical and socioeconomic data on patients at the time of admission. These data were prospectively collected as part of regular hospital operations. Appendix Table 1 lists the source for each variable used in our model. Appendix Table 1. Description of Variables Outcomes Our primary outcomes included early readmissions, defined as readmissions within 7 days after discharge from the index hospitalization, and late readmissions, defined as readmissions 8 to 30 days after discharge. All admissions were categorized by type in the repository; only nonelective readmissions were included in our analysis. Analysis We grouped variables into 2 conceptual categories on the basis of how we hypothesized that they would relate to early and late readmissions. The first category contained a group of variables related to the index admission that we hypothesized would be associated with early readmissions. This category included markers of acute illness burden; a set of inpatient care process factors, such as discharge timing and physician continuity; and markers of clinical instability on discharge, which is a composite variable based on treatments administered on the day of discharge (Appendix Table 2 lists all of the included treatments). To adjust for patients' physiologic instability, we used Escobar and colleagues' (12) methods to calculate a laboratory-based acute physiology score on the basis of laboratory data before or at the time of admission. The second category contained variables that we hypothesized would be associated with late readmissions and included markers of chronic illness burden and social determinants of health, including race, marital status, barriers to learning, primary language, and insurance coverage. When 2 variables from the repository were clinically redundant, we chose to keep the variable that we believed was more clinically relevant. Appendix Table 1 lists our variables, their source, and how we operationalized them. Appendix Table 2. Description of the Composite Variable for Markers of Clinical Instability at Discharge If data were missing in 5% or fewer of the cohort for any variable originating from the repository, multiple imputation was used with a chained equation approach with 50 imputed data sets and all nonmissing variables in the model. Variables in which greater than 5% of the data were missing from the repository were not used, with a few exceptions. Missing data derived from our electronic nursing assessment were treated as not conferring risk because questions were often skipped if the answer was no or there was a negative risk. Missing laboratory data used to calculate the laboratory-based acute physiology score were treated as being within normal physiologic variables, which is the standard approach for this calculation (12). We assumed that patients who did not have echocardiographic data available in the repository were unlikely to have ejection fractions less than 0.35. Appendix Table 1 shows more information on missing data for each variable. Statistical Analysis We used polytomous multinomial logistic regression to model the association between variables of interest and early and late readmissions simultaneously. We used generalized estimating equations to cluster at the patient level because the unit of observation in our cohort was the admission and individual patients could be admitted more than once. Our model-building and variable-reduction strategies were as follows: Polytomous multinomial logistic regression was used to determine univariable associations between exposures of interest and early and late readmissions. All exposures with P values less than 0.20 were included in the model, along with age, sex, race, index hospital length of stay, and discharge disposition, which were included a priori. Because the cut point for early versus late readmission is not clear, we did a sensitivity analysis using a 5-day cut point and did not see a substantial change in the results. Therefore, we present only 7-day cut point results. Data management was done using SAS, version 9.2 (SAS Institute), and data analysis was done using R, version 3.0. 1, and multgee package, version 1.3 (R Foundation for Statistical Computing). Data were imputed using the mi package, version 0.09-19 (R Foundation for Statistical Computing). Role of the Funding Source This study was funded by the Health Resources and Services Administration, National Institute on Aging, National Institutes of Health, Harvard Catalyst, and Harvard University and its affiliated academic health care centers. The funding source had no role in the design, conduct, collection, management, analysis, or interpretation of the data or in the preparation, review, or approval of the manuscript. Results Cohort Characteristics The cohort included 13355 admissions, representing 8078 patients with a mean age of 66 years (SD, 18); 54% of the patients were women (Figure 1). The overall 30-day readmission rate was 19.7%. A total of 1046 early readmissions (7.8% of index admissions) and 1586 late readmissions (11.9% of index admissions) occurred (Table 1 and Figure 1). The distribution of readmission by day after discharge is shown in Figure 2; 39.7% readmissions occurred in the early period and 60.3% in the late period. When all admissions were included in the analyses, the iterative process was unable to solve the equations in a reasonable length of time (that is, the models did not converge). Therefore, the