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

Hospital Readmission Clinical Trial

Official title:

External Validation of EPIC's Readmission Risk Model, the LACE+ Index and SQLape as Predictors of Unplanned Hospital Readmissions: A Monocentric, Retrospective, Diagnostic Cohort Study in Switzerland

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04306172
• Other study ID #: LUKS_RRM_2019
• Status: Completed
• Start date: March 10, 2020
• Est. completion date: October 1, 2020

Study information

• Verified date: October 2020
• Source: Luzerner Kantonsspital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 23116
• Est. completion date: October 1, 2020
• Est. primary completion date: April 10, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 1 Year to 100 Years

Eligibility

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.

Related Conditions & MeSH terms

• Hospital Readmission

Intervention

Other:
• 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.
• 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.
• 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.

Locations

Country	Name	City	State
Switzerland	Cantonal Hospital of Lucerne	Lucerne	Canton Lucerne

Sponsors (2)

Lead Sponsor	Collaborator
Luzerner Kantonsspital	University of Lucerne

Country where clinical trial is conducted

Switzerland, 

References & Publications (2)

Halfon P, Eggli Y, Prêtre-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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Discrimination at 18 days	For discrimination of the scores under investigation, the area under the receiver operating characteristics curves (AUC) will be calculated.	18 days after index discharge date
Primary	Discrimination at 30 days	For discrimination of the scores under investigation, the area under the receiver operating characteristics curves (AUC) will be calculated.	30 days after index discharge date
Primary	Calibration at 18 days	For calibration, the Hosmer-Lemeshow goodness-of-fit test will be graphically illustrated by plotting the predicted outcomes by decile against the observations.	18 days after index discharge date
Primary	Calibration at 30 days	For calibration, the Hosmer-Lemeshow goodness-of-fit test will be graphically illustrated by plotting the predicted outcomes by decile against the observations.	30 days after index discharge date
Primary	Overall Performance at 18 days	Brier Score (The Brier score is a quadratic scoring rule, where the squared difference between actual binary outcomes Y and predictions p are calculated. The Brier score can range from 0 for a perfect model to 0.25 for a non-informative model with a 50% incidence of the outcome.)	18 days after index discharge date
Primary	Overall Performance at 30 days	Brier Score (The Brier score is a quadratic scoring rule, where the squared difference between actual binary outcomes Y and predictions p are calculated. The Brier score can range from 0 for a perfect model to 0.25 for a non-informative model with a 50% incidence of the outcome.)	30 days after index discharge date
Primary	Clinical usefulness (NRI) at 18 days	Net Reclassification Improvement (NRI): In the calculation of the NRI, the improvement in sensitivity and the improvement in specificity are summed. The NRI ranges from 0 for no improvement and 1 for perfect improvement.	18 days after index discharge date
Primary	Clinical usefulness (NRI) at 30 days	Net Reclassification Improvement (NRI): In the calculation of the NRI, the improvement in sensitivity and the improvement in specificity are summed. The NRI ranges from 0 for no improvement and 1 for perfect improvement.	30 days after index discharge date
Primary	Clinical usefulness (NB) at 18 days	Net Benefit (NB): NB = (TP - w FP) / N, where TP is the number of true positive decisions, FP the number of false positive decisions, N is the total number of patients and w is a weight equal to the odds of the cut-off (pt/(1-pt), or the ratio of harm to benefit	18 days after index discharge date
Primary	Clinical usefulness (NB) at 30 days	Net Benefit (NB): NB = (TP - w FP) / N, where TP is the number of true positive decisions, FP the number of false positive decisions, N is the total number of patients and w is a weight equal to the odds of the cut-off (pt/(1-pt), or the ratio of harm to benefit	30 days after index discharge date