A simplified scoring tool for prediction of readmission in elderly patients hospitalized in internal medicine departments

Isr Med Assoc J. 2012 Dec;14(12):752-6.


Background: Frequent readmissions significantly contribute to health care costs as well as work load in internal medicine wards.

Objective: To develop a simple scoring method that includes basic demographic and medical characteristics of elderly patients in internal medicine wards that would allow prediction of readmission within 3 months of discharge.

Methods: We conducted a retrospective observational study of 496 hospitalized patients using data collected from discharge letters in the computerized archives. Univariate and multivariate logistic regression analyses were performed and factors that were significantly associated with readmission were selected to construct a scoring tool. Validity was assessed in a cohort of 200 patients.

Results: During a 2 year follow-up 292 patients were readmitted at least once within 3 months of discharge. Age 80 or older, any degree of impaired cognition, nursing home residence, congestive heart failure, and creatinine level > 1.5 mg/dl were found to be strong predictors of readmission. The presence of each variable was scored as 1. A score of 3 or higher in the derivation and validation cohorts corresponded with a positive predictive value of 80% and 67%, respectively, when evaluating the risk of rehospitalization.

Conclusions: We propose a practical, readily available five-item scoring tool that allows prediction of most unplanned readmissions within 3 months. The strength of this scoring tool, as compared with previously published scores, is its simplicity and straightforwardness.

Publication types

  • Comparative Study

MeSH terms

  • Aged
  • Aged, 80 and over
  • Female
  • Follow-Up Studies
  • Forecasting / methods*
  • Hospital Departments / statistics & numerical data*
  • Hospitals, Teaching / statistics & numerical data*
  • Humans
  • Internal Medicine*
  • Male
  • Patient Readmission / statistics & numerical data*
  • Reproducibility of Results
  • Retrospective Studies
  • Risk Factors