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Trial record 4 of 123 for:    lucerne

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

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ClinicalTrials.gov Identifier: NCT04306172
Recruitment Status : Completed
First Posted : March 12, 2020
Last Update Posted : October 20, 2020
Sponsor:
Collaborator:
University of Lucerne
Information provided by (Responsible Party):
Aljoscha Hwang, Luzerner Kantonsspital

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.


Condition or disease Intervention/treatment
Hospital Readmission Other: An US Readmission Risk Prediction Model Other: LACE+ score Other: SQLAPE model

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.

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Study Type : Observational
Actual Enrollment : 23116 participants
Observational Model: Cohort
Time Perspective: Retrospective
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
Actual Study Start Date : March 10, 2020
Actual Primary Completion Date : April 10, 2020
Actual Study Completion Date : October 1, 2020

Resource links provided by the National Library of Medicine


Group/Cohort Intervention/treatment
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.

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.

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.

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.

Non-Readmitted inpatients/Controls
Outcome 1 & 2: Patients who were not readmitted within 30 days of index hospitalization discharge date.
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.

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.

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.




Primary Outcome Measures :
  1. Discrimination at 18 days [ Time Frame: 18 days after index discharge date ]
    For discrimination of the scores under investigation, the area under the receiver operating characteristics curves (AUC) will be calculated.

  2. Discrimination at 30 days [ Time Frame: 30 days after index discharge date ]
    For discrimination of the scores under investigation, the area under the receiver operating characteristics curves (AUC) will be calculated.

  3. Calibration at 18 days [ Time Frame: 18 days after index discharge date ]
    For calibration, the Hosmer-Lemeshow goodness-of-fit test will be graphically illustrated by plotting the predicted outcomes by decile against the observations.

  4. Calibration at 30 days [ Time Frame: 30 days after index discharge date ]
    For calibration, the Hosmer-Lemeshow goodness-of-fit test will be graphically illustrated by plotting the predicted outcomes by decile against the observations.

  5. Overall Performance at 18 days [ Time Frame: 18 days after index discharge date ]
    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.)

  6. Overall Performance at 30 days [ Time Frame: 30 days after index discharge date ]
    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.)

  7. Clinical usefulness (NRI) at 18 days [ Time Frame: 18 days after index discharge date ]
    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.

  8. Clinical usefulness (NRI) at 30 days [ Time Frame: 30 days after index discharge date ]
    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.

  9. Clinical usefulness (NB) at 18 days [ Time Frame: 18 days after index discharge date ]
    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

  10. Clinical usefulness (NB) at 30 days [ Time Frame: 30 days after index discharge date ]
    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



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Ages Eligible for Study:   1 Year to 100 Years   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Inpatients from an acute care hospital in Central-Switzerland
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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04306172


Locations
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Switzerland
Cantonal Hospital of Lucerne
Lucerne, Canton Lucerne, Switzerland, 6000
Sponsors and Collaborators
Luzerner Kantonsspital
University of Lucerne
Investigators
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Principal Investigator: Aljoscha B. Hwang University Lucerne (Switzerland)
Principal Investigator: Stefan Boes University Lucerne (Switzerland)
  Study Documents (Full-Text)

Documents provided by Aljoscha Hwang, Luzerner Kantonsspital:
Publications:
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Responsible Party: Aljoscha Hwang, Research Project Manager & Advanced Analytics Analyst, Luzerner Kantonsspital
ClinicalTrials.gov Identifier: NCT04306172    
Other Study ID Numbers: LUKS_RRM_2019
First Posted: March 12, 2020    Key Record Dates
Last Update Posted: October 20, 2020
Last Verified: October 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Aljoscha Hwang, Luzerner Kantonsspital:
Prediction model
hospital readmission
external validation