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

NCT ID: NCT04306172

Last Updated: 2020-10-20

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 23116 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2020-03-10
• Study Completion Date: 2020-10-01

Brief 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.

Conditions

Hospital Readmission

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Readmitted inpatients/Cases

Outcome 1: Patients who were readmitted within 18 days of index hospitalization discharge date to the same hospital, with a diagnosis leading to the same Major Diagnostic Group as the index stay (definition according to Swiss Diagnosis Related Groups system, case merger)

Outcome 2: Patients with an unplanned readmission within 30 days of index hospitalization discharge date to the same hospital. An unplanned readmission was defined as a readmission through the emergency department.

An US Readmission Risk Prediction Model

Intervention Type: OTHER

Logistic regression model that predicts the risk of all-cause unplanned readmissions developed by the privately held healthcare software company EPIC.

LACE+ score

Intervention Type: OTHER

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.

SQLAPE model

Intervention Type: OTHER

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.

Non-Readmitted inpatients/Controls

Outcome 1 \& 2: Patients who were not readmitted within 30 days of index hospitalization discharge date.

An US Readmission Risk Prediction Model

Intervention Type: OTHER

Logistic regression model that predicts the risk of all-cause unplanned readmissions developed by the privately held healthcare software company EPIC.

LACE+ score

Intervention Type: OTHER

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.

SQLAPE model

Intervention Type: OTHER

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.

Interventions

An US Readmission Risk Prediction Model

Logistic regression model that predicts the risk of all-cause unplanned readmissions developed by the privately held healthcare software company EPIC.

Intervention Type: OTHER

LACE+ score

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.

Intervention Type: OTHER

SQLAPE model

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.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• All inpatients, aged one year or older (max. 100 years), who were hospitalized either between the 1st of January 2018 and the 31st of December 2018, or between the 23rd of September and the 31st of December 2019 will be included.

Exclusion Criteria

• admission/transfer from another psychiatric, rehabilitative or acute care ward from the same institution,
• discharge destination other than the patient's home or
• transfer to another acute care hospital, both being considered as treatment continuation;
• foreign residence,
• deceased before discharge,
• discharged on admission day,
• refusal of general consent, and
• unknown patient residence or discharge destination.

• Minimum Eligible Age: 1 Year
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Universität Luzern

OTHER

Sponsor Role: collaborator

Luzerner Kantonsspital

OTHER

Sponsor Role: lead

Responsible Party

Aljoscha Hwang

Research Project Manager & Advanced Analytics Analyst

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Aljoscha B. Hwang

Role: PRINCIPAL_INVESTIGATOR

University Lucerne (Switzerland)

Stefan Boes

Role: PRINCIPAL_INVESTIGATOR

University Lucerne (Switzerland)

Locations

Cantonal Hospital of Lucerne

Lucerne, Canton Lucerne, Switzerland

Countries

Switzerland

References

van Walraven C, Wong J, Forster AJ. LACE+ index: extension of a validated index to predict early death or urgent readmission after hospital discharge using administrative data. Open Med. 2012 Jul 19;6(3):e80-90. Print 2012.

Reference Type: BACKGROUND

PMID: 23696773 (View on PubMed)

Halfon P, Eggli Y, Pretre-Rohrbach I, Meylan D, Marazzi A, Burnand B. Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006 Nov;44(11):972-81. doi: 10.1097/01.mlr.0000228002.43688.c2.

Reference Type: BACKGROUND

PMID: 17063128 (View on PubMed)

Provided Documents

Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

LUKS_RRM_2019

Identifier Type: -

Identifier Source: org_study_id