Validation of EPIC's Readmission Risk Model, the LACE+ Index and SQLape as Predictors of Unplanned Hospital Readmissions
Condition: Hospital Readmission
Study type: Observational

Summary: 
      The primary objective of this study is to externally validate the EPIC's Readmission Risk
      model and to compare it with the LACE+ index and the SQLape Readmission model.

      As secondary objective, the EPIC's Readmission Risk model will be adjusted based on the
      validation sample, and finally, it´s performance will be compared with machine learning
      algorithms.
    

Detailed description: 
      Introduction: Readmissions after an acute care hospitalization are relatively common, costly
      to the health care system and are associated with significant burden for patients. As one way
      to reduce costs and simultaneously improve quality of care, hospital readmissions receive
      increasing interest from policy makers. It is only relatively recently that strategies were
      developed with the specific aim of reducing unplanned readmissions by applying prediction
      models. EPIC's Readmission Risk model, developed in 2015 for the U.S. acute care hospital
      setting, promises superior calibration and discriminatory abilities. However, its routine
      application in the Swiss hospital setting requires external validation first. Therefore, the
      primary objective of this study is to externally validate the EPIC's Readmission Risk model
      and to compare it with the LACE+ index (Length of stay, Acuity, Comorbidities, Emergency Room
      visits index) and the SQLape (Striving for Quality Level and analysing of patient
      expenditures) Readmission model.

      Methods: For this reason, a monocentric, retrospective, diagnostic cohort study will be
      conducted. The study will include all inpatients, who were hospitalized between the 1st
      January 2018 and the 31st of January 2019 in the Lucerne Cantonal hospital in Switzerland.
      Cases will be inpatients that experienced an unplanned (all-cause) readmission within 18 or
      30 days after the index discharge. The control group will consist of individuals who had no
      unscheduled readmission.

      For external validation, discrimination of the scores under investigation will be assessed by
      calculating the area under the receiver operating characteristics curves (AUC). For
      calibration, the Hosmer-Lemeshow goodness-of-fit test will be graphically illustrated by
      plotting the predicted outcomes by decile against the observations. Other performance
      measures to be estimated will include the Brier Score, Net Reclassification Improvement (NRI)
      and the Net Benefit (NB).

      All patient data will be retrieved from clinical data warehouses.
    

Intervention name(s): An US Readmission Risk Prediction Model; LACE+ score; SQLAPE model
Intervention type(s): Other; Other; Other
Intervention description(s): Logistic regression model that predicts the risk of all-cause unplanned readmissions developed by the privately held healthcare software company EPIC.; The LACE+ score is a point score that can be used to predict the risk of post-discharge death or urgent readmission. It was developed based on administrative data in Ontario, Canada.; The readmission risk model (Striving for Quality Level and analyzing of patient expenditures), is a computerized validated algorithm and was developed in 2002 to identify potentially avoidable readmissions.
Observational model: Cohort
Time perspective: Retrospective
Sampling method: Non-Probability Sample
Study population: 
        Inpatients from an acute care hospital in Central-Switzerland
      
Keywords: Prediction model; hospital readmission; external validation