Original Research

Developing a Real-Time Prediction Model for Medicine Service 30-Day Readmissions

Journal of Clinical Outcomes Management. 2020 January;27(1)

Author(s):

Michael Trautwein, MD

Stacy Schwartz, MD

Lori Lyn Price, MAS, MLA

Alexander Pavoll, MPH

Phanicharan Sistla, MD

Genève M. Allison, MD, MSc

Jana C. Leary, MD, MS

Saul N. Weingart, MD, MPP, PhD

Karen M. Freund, MD, MPH

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References

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4. Centers for Medicare and Medicaid. Readmissions-reduction-program. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html. Accessed December 8, 2019.

5. Institute for Healthcare Improvement. Readmissions. Reduce avoidable readmissions. www.ihi.org/Topics/Readmissions/Pages/default.aspx. Accessed December 8, 2019.

6. Van Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182:551-557.

7. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306:1688-1698.

8. Robinson R, Hudali T. The HOSPITAL score and LACE index as predictors of 30 day readmission in a retrospective study at a university-affiliated community hospital. Peer J. 2017;5:e3137.

9. El Morr C, Ginsburg L, Nam S, Woollard S. Assessing the performance of a modified LACE index (LACE-rt) to predict unplanned readmission after discharge in a community teaching hospital. Interactive J Med Res. 2017;6:e2.

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From Tufts Medical Center, Boston, MA (Dr. Trautwein and Dr. Schwartz contributed equally to this article).

Abstract

Objective: To examine whether an institution- and service-specific readmission prediction instrument has improved performance compared to universal tools.
Design: Retrospective cohort study.
Setting: Academic medical center located in Boston, MA.
Participants: Adult inpatients admitted to a medicine service.
Measurements: Patient attributes, inpatient service assignment, and 30-day readmission rate.
Results: Of 7972 index admissions, 12.6% were readmitted within 30 days. Thirty-day readmissions were associated with number of medications on admission (adjusted odds ratio [OR], 1.34; 95% confidence interval [CI], 1.11-1.61) for ≥ 11 compared with ≤ 5 medications; prior admission or overnight observation (OR, 1.89; 95% CI, 1.61-2.23); and discharge (OR, 2.45; 95% CI, 1.97-3.06) to an acute care facility compared to home without services. The subspecialty services with the highest risk of readmission were bone marrow transplant/hematology (OR, 2.46; 95% CI, 1.78-3.40) and oncology (OR, 2.26; 95% CI, 1.67-3.05), as compared with general medicine/geriatrics. The C statistic for the derivation cohort was 0.67, as compared with a C statistic of 0.63 for the LACE index.
Conclusion: A hospital service–specific 30-day readmission prediction tool showed incrementally improved performance over the widely used LACE index.

Keywords: rehospitalization; quality of care; predictive model; hospital medicine.

Readmissions are a costly problem in the United States. The readmission rate among Medicare beneficiaries aged 65 years and older was 17.1 per 100 live discharges in 2016.¹ Although both the relationship between readmissions and quality of care and the use of readmissions as a quality measure are contested,² efforts to reduce potentially avoidable rehospitalizations have received widespread attention and readmissions are a focus of reimbursement reform.^3-5

The LACE index is a widely used model for readmission risk prediction.⁶ Derived from a study of hospitalized patients in Ontario, Canada, LACE uses information about length of stay, acuity, comorbidities, and emergency department utilization to estimate readmission risk. Models such as LACE+, LACE-rt, and HOSPITAL have tried to improve on LACE’s performance, but models with strong discriminative ability are lacking.^7-9 Institution-specific models exist,¹⁰ as do well-organized multicenter studies,⁷ but their generalizability is limited due to population differences or inclusion of data difficult to extract from patients in real-time, such as data gathered through a socioeconomic questionnaire.

Given hospital-specific differences in operational performance and patient population, we sought to develop a statistical model for 30-day readmission prediction and demonstrate the process that could be utilized by other institutions to identify high-risk patients for intensive case management and discharge planning.

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Developing a Real-Time Prediction Model for Medicine Service 30-Day Readmissions

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Abstract

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