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IMPROVING PATIENT CARE

Differences Between Early and Late Readmissions Among Patients: A Cohort StudyDifferences Between Early and Late Readmissions

Kelly L. Graham, MD, MPH; Elissa H. Wilker, ScD; Michael D. Howell, MD, MPH; Roger B. Davis, ScD; and Edward R. Marcantonio, MD, SM
[+] Article, Author, and Disclosure Information

From Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, and Center for Quality and Section of Pulmonary and Critical Care, University of Chicago, Chicago, Illinois.

Note: Dr. Graham had full access to all of the data and takes responsibility for its integrity and the accuracy of the data analysis. Dr. Wilker did the data analysis.

Acknowledgment: The authors thank George Silva (Data Analyst, Beth Israel Deaconess Medical Center) and the Insight Core at Beth Israel Deaconess Medical Center for their invaluable assistance in helping to create the data set and Kenneth Mukamal, MD, MPH (Associate Professor of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School), and Long Ngo, PhD (Associate Professor of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School) for insight and assistance with the statistical analysis. They received no compensation for their contributions.

Grant Support: Dr. Graham was supported by the Health Resources and Services Administration (training grant T32 HP12706). Dr. Marcantonio was supported by the National Institute on Aging (Midcareer Investigator Award in Patient-Oriented Research K24 AG035075). Dr. Wilker was supported by the National Institutes of Health (award K99 ES022243). This work was also conducted with support from the Harvard Catalyst (National Institutes of Health award 1UL1 TR001102-01) and financial contributions from Harvard University and its affiliated academic health care centers.

Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M14-2159.

Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that she has no financial relationships or interests to disclose. Darren B. Taichman, MD, PhD, Executive Deputy Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer.

Reproducible Research Statement:Study protocol, statistical code, and data set: Available from Dr. Graham (e-mail,kgraham@bidmc.harvard.edu).

Requests for Single Reprints: Kelly L. Graham, MD, MPH, Instructor of Medicine, Harvard Medical School, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, E/Shapiro 607D, Boston, MA 02215; e-mail, kgraham@bidmc.harvard.edu.

Current Author Addresses: Dr. Graham: Beth Israel Deaconess Medical Center, 330 Brookline Avenue, E/Shapiro 607D, Boston, MA 02215.

Dr. Wilker: Cardiovascular Epidemiology Research Unit, Beth Israel Deaconess Medical Center, 375 Longwood Avenue, Boston, MA 02215.

Dr. Howell: 5841 South Maryland Avenue, MC 1135, Chicago, IL 60637.

Dr. Davis: Beth Israel Deaconess Medical Center, 330 Brookline Avenue, CO-217, Boston, MA 02215.

Dr. Marcantonio: Beth Israel Deaconess Medical Center, 330 Brookline Avenue, CO-218, Boston, MA 02215.

Author Contributions: Conception and design: K.L. Graham, E.R. Marcantonio.

Analysis and interpretation of the data: K.L. Graham, E.H. Wilker, M.D. Howell, R.B. Davis.

Drafting of the article: K.L. Graham.

Critical revision of the article for important intellectual content: K.L. Graham, E.H. Wilker, M.D. Howell, R.B. Davis, E.R. Marcantonio.

Final approval of the article: K.L. Graham, E.H. Wilker, M.D. Howell, R.B. Davis, E.R. Marcantonio.

Provision of study materials or patients: K.L. Graham.

Statistical expertise: R.B. Davis.

Obtaining of funding: E.R. Marcantonio.

Administrative, technical, or logistic support: K.L. Graham, M.D. Howell.

Collection and assembly of data: K.L. Graham, M.D. Howell.


Ann Intern Med. 2015;162(11):741-749. doi:10.7326/M14-2159
Text Size: A A A

Background: Early and late readmissions may have different causal factors, requiring different prevention strategies.

Objective: To determine whether predictors of readmission change within 30 days after discharge.

Design: Retrospective cohort study.

Setting: Academic medical center.

Participants: Patients admitted between 1 January 2009 and 31 December 2010.

Measurements: Factors related to the index hospitalization (acute illness burden, inpatient care process factors, and clinical indicators of instability at discharge) and unrelated factors (chronic illness burden and social determinants of health) and how they affect early readmissions (0 to 7 days after discharge) and late readmissions (8 to 30 days after discharge).

Results: 13 334 admissions, representing 8078 patients, were included in the analysis. Early readmissions were associated with markers of acute illness burden, including length of hospital stay (odds ratio [OR], 1.02 [95% CI, 1.00 to 1.03]) and whether a rapid response team was called for assessment (OR, 1.48 [CI, 1.15 to 1.89]); markers of chronic illness burden, including receiving a medication indicating organ failure (OR, 1.19 [CI, 1.02 to 1.40]); and social determinants of health, including barriers to learning (OR, 1.18 [CI, 1.01 to 1.38]). Early readmissions were less likely if a patient was discharged between 8:00 a.m. and 12:59 p.m. (OR, 0.76 [CI, 0.58 to 0.99]). Late readmissions were associated with markers of chronic illness burden, including receiving a medication indicating organ failure (OR, 1.24 [CI, 1.08 to 1.41]) or hemodialysis (OR, 1.61 [CI, 1.12 to 2.17]), and social determinants of health, including barriers to learning (OR, 1.24 [CI, 1.09 to 1.42]) and having unsupplemented Medicare or Medicaid (OR, 1.16 [CI, 1.01 to 1.33]).

Limitation: Readmissions were ascertained at 1 institution.

Conclusion: The time frame of 30 days after hospital discharge may not be homogeneous. Causal factors and readmission prevention strategies may differ for the early versus late periods.

Primary Funding Source: Health Resources and Services Administration, National Institute on Aging, National Institutes of Health, Harvard Catalyst, and Harvard University.

Figures

Grahic Jump Location
Figure 1.

Study flow diagram.

LTAC = long-term acute care hospital.

Grahic Jump Location
Grahic Jump Location
Figure 2.

Number of readmissions by day after discharge in the first 30 d after hospitalization.

The green vertical line separates early and late readmissions.

Grahic Jump Location

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Comments

Submit a Comment/Letter
Was the choice 7 days is scientifically based?
Posted on June 9, 2015
Ronald L. Hirsch, MD, FACP, CHCQM
Accretive Health
Conflict of Interest: None Declared
In their study on the differences between early and late readmissions, Graham et al found that early readmissions were associated with markers of acute illness burden and late readmissions were associated with markers of chronic disease burden, concluding that the time frame of 30 days after hospital discharge may not be homogeneous. As noted by the authors, the 30 day time frame used by the Centers for Medicare and Medicaid Services (CMS) for their readmission reduction program likely penalizes hospitals for factors that are beyond their control and a narrower window is warranted.
In their study the authors performed a sensitivity analysis at a 5-day cut point and when they found no significant difference, they chose a 7-day cut point and found significant results, thereby using readmission within 7 days as their definition of early readmission. The authors did not provide a rationale for their choice of 5 and 7 days, nor explain why 6 or 8 days was not tested to determine if they had greater statistical significance. As with the use of 30 days by CMS, it appears that they chose numbers that were commonly accepted in statistics, with 5 being the number of working days in a week, 7 being the total number of days in a week and 30 being the average number of days in a month. This is similar to our use of 7, 10 or 14 days for many common infections such as pneumonia or pyelonephritis and 4 or 6 weeks for more serious infections such as endocarditis, based mainly on tradition and comfort than on actual science.
The financial implications of determining the cut point for readmissions is great with millions of dollars at stake for hospitals, health systems and accountable care organizations. The use of the “comfortable” numbers five and seven in this study should lead the results to be viewed with caution if used by regulatory bodies to better determine a fairer cut point for readmission reduction programs.
Response
Posted on July 23, 2015
Kelly L. Graham, MD, MPH, Edward R. Marcantonio, MD, SM
Divisions of General Medicine and Primary Care (Graham and Marcantonio) and Gerontology (Marcantonio), Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School
Conflict of Interest: None Declared
We apologize if our text was unclear. When we said that our sensitivity analysis using a cut point of 5 days was "not substantially different", we meant relative to the 7 day results, not that the results were no longer significant. In fact we performed this sensitivity analysis specifically to determine whether our findings are robust enough to justify the specific cut point used. The results show that whether 5 days, 7 days (or we suspect 6 or 8 days) is used as the cutoff, the message is still the same--early readmissions are different from late readmissions.

There is no “actual science” on which to base our choice of which day to use for our cut-off, because this issue has not yet been studied. We based our decision on clinical judgment, expert opinion, and a close look at the distribution of our data by post-discharge day. We did not simply use comfortable numbers as you suggest. Please see Figure 2, where it appears that around 7-8 days post-discharge, the distribution changes somewhat with respect to the number of readmission on any given day. We hope that this approach was an adequate way to design our outcome where there was essentially no data on which to base our decision.

Lastly, while it is reasonable to extrapolate that a narrower window may be necessary from our study, the main conclusions stated in our manuscript is that the timeframe is not homogenous, and that early and late readmissions may require different prevention strategies. Further study is needed to determine the larger health policy implications of these findings.
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