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Simplification of the HOSPITAL score for predicting 30-day readmissions
  1. Carole E Aubert1,
  2. Jeffrey L Schnipper2,3,
  3. Mark V Williams4,
  4. Edmondo J Robinson5,
  5. Eyal Zimlichman6,
  6. Eduard E Vasilevskis7,8,9,
  7. Sunil Kripalani7,8,
  8. Joshua P Metlay10,
  9. Tamara Wallington11,
  10. Grant S Fletcher12,
  11. Andrew D Auerbach13,
  12. Drahomir Aujesky1,
  13. Jacques D Donzé1,2,3
  1. 1 Department of General Internal Medicine, Bern University Hospital, University of Bern, Bern, Switzerland
  2. 2 BWH Hospitalist Service, Division of General Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
  3. 3 Harvard Medical School, Boston, Massachusetts, USA
  4. 4 Center for Health Services Research, University of Kentucky, Kentucky, USA
  5. 5 Value Institute, Christiana Care Health System, Wilmington, Delaware, USA
  6. 6 Sheba Medical Center, Tel Hashomer, Israel
  7. 7 Section of Hospital Medicine, Division of General Internal Medicine and Public Health Vanderbilt University, Nashville, TN, USA
  8. 8 Center for Clinical Quality and Implementation Research, Vanderbilt University, Nashville, TN, USA
  9. 9 VA Tennessee Valley – Geriatric Research, Education and Clinical Center (GRECC), Nashville, TN, USA
  10. 10 Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
  11. 11 William Osler Health System, Ontario, Canada
  12. 12 Department of Medicine, Harborview Medical Center, University of Washington, Seattle, Washington, USA
  13. 13 Division of Hospital Medicine, University of California, San Francisco, USA
  1. Correspondence to Dr Carole E Aubert, General Internal Medicine, Bern University Hospital, University of Bern, Freiburgstrasse, Bern 3010, Switzerland; caroleelodie.aubert{at}


Objective The HOSPITAL score has been widely validated and accurately identifies high-risk patients who may mostly benefit from transition care interventions. Although this score is easy to use, it has the potential to be simplified without impacting its performance. We aimed to validate a simplified version of the HOSPITAL score for predicting patients likely to be readmitted.

Design and setting Retrospective study in 9 large hospitals across 4 countries, from January through December 2011.

Participants We included all consecutively discharged medical patients. We excluded patients who died before discharge or were transferred to another acute care facility.

Measurements The primary outcome was any 30-day potentially avoidable readmission. We simplified the score as follows: (1) ‘discharge from an oncology division’ was replaced by ‘cancer diagnosis or discharge from an oncology division’; (2) ‘any procedure’ was left out; (3) patients were categorised into two risk groups (unlikely and likely to be readmitted). The performance of the simplified HOSPITAL score was evaluated according to its overall accuracy, its discriminatory power and its calibration.

Results Thirty-day potentially avoidable readmission rate was 9.7% (n=11 307/117 065 patients discharged). Median of the simplified HOSPITAL score was 3 points (IQR 2–5). Overall accuracy was very good with a Brier score of 0.08 and discriminatory power remained good with a C-statistic of 0.69 (95% CI 0.68 to 0.69). The calibration was excellent when comparing the expected with the observed risk in the two risk categories.

Conclusions The simplified HOSPITAL score has good performance for predicting 30-day readmission. Prognostic accuracy was similar to the original version, while its use is even easier. This simplified score may provide a good alternative to the original score depending on the setting.

  • Hospital medicine
  • Risk management
  • Transitions in care
  • Adverse events, epidemiology and detection

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  • Contributors JDD and JLS had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: CEA, JDD. Acquisition, analysis or interpretation of data: JDD. Drafting of the manuscript: CEA. Critical revision of the manuscript for important intellectual content: JDD, JLS, MVW, EJR, EZ, EEV, SK, JPM, TW, GSF, ADA, DA. Statistical analysis: JDD.

  • Funding Swiss National Science Foundation and the Swiss Foundation for Medical-Biological Scholarships (PASMP3-142734), Veterans Affairs Clinical Research Center of Excellence, Geriatric Research, Education, and Clinical Center (GRECC), National Institute on Aging of the National Institutes of Health (K23AG040157).

  • Competing interests JDD worked as consultant at Profility, and Homeward Health, and was supported by the Swiss National Science Foundation and the Swiss Foundation for Medical-Biological Scholarships (grant no. PASMP3-142734). EZ is a consultant and advisory board member at Earlysense, which makes monitors for patients on hospital wards, a consultant and advisory board member at Hello Doctor, which develops patient record applications, a consultant to Profility, which develops big data applications for improvement of the management of elderly populations, a founder and advisory board member at ValueScope Health, which creates financial management systems for healthcare organisations, and a founder and advisory board member at Ethos, which develops mobile health applications for patient engagement. TW has been a consultant for Novartis to provide advice on screening for cardiovascular disease. JLS has received grant funding from Sanofi-Aventis for an investigator-initiated study to design and evaluate an intensive discharge and follow-up intervention in patients with diabetes.

  • Ethics approval The managing site (Brigham and Women's Hospital/Partners Healthcare, Boston, Massachusetts, USA) and the institutional review board of each local hospital approved the trial protocol.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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