Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?

BMC Med Inform Decis Mak. 2020 Sep 15;20(1):227. doi: 10.1186/s12911-020-01248-1.


Background: Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). We assessed how well a previously validated 30-day EHR-based readmission prediction model predicts 7-day readmissions and compared differences in strength of predictors.

Methods: We conducted an observational study on adult hospitalizations from 6 diverse hospitals in North Texas using a 50-50 split-sample derivation and validation approach. We re-derived model coefficients for the same predictors as in the original 30-day model to optimize prediction of 7-day readmissions. We then compared the discrimination and calibration of the 7-day model to the 30-day model to assess model performance. To examine the changes in the point estimates between the two models, we evaluated the percent changes in coefficients.

Results: Of 32,922 index hospitalizations among unique patients, 4.4% had a 7-day admission and 12.7% had a 30-day readmission. Our original 30-day model had modestly lower discrimination for predicting 7-day vs. any 30-day readmission (C-statistic of 0.66 vs. 0.69, p ≤ 0.001). Our re-derived 7-day model had similar discrimination (C-statistic of 0.66, p = 0.38), but improved calibration. For the re-derived 7-day model, discharge day factors were more predictive of early readmissions, while baseline characteristics were less predictive.

Conclusion: A previously validated 30-day readmission model can also be used as a stopgap to predict 7-day readmissions as model performance did not substantially change. However, strength of predictors differed between the 7-day and 30-day model; characteristics at discharge were more predictive of 7-day readmissions, while baseline characteristics were less predictive. Improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.

Keywords: Care transitions; Clinical decision support; Early readmissions; Healthcare quality improvement; Hospital medicine; Hospital utilization; Predictive model.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Aged
  • Female
  • Forecasting
  • Hospitalization*
  • Hospitals
  • Humans
  • Male
  • Middle Aged
  • Patient Readmission*
  • Reference Standards
  • Retrospective Studies
  • Risk Factors
  • Texas
  • United States