Hospital ReadmissionNCT04306172
Validation of EPIC's Readmission Risk Model, the LACE+ Index and SQLape as Predictors of Unplanned Hospital Readmissions
An observational study of Hospital Readmission, run by Luzerner Kantonsspital at 1 site in Switzerland.
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… Read more about this study
- Who is it for?
- Ages 1 year to 100 years
- Conditions the study is for
- Hospital Readmission
- What is being tested?
- An US Readmission Risk Prediction Model, LACE+ score and SQLAPE model
- How many people
- 23,116 participants
- Where
- 1 siteSwitzerland
- Study dates
- 10 Mar 2020to 1 Oct 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 1 year to 100 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Inpatients from an acute care hospital in Central-Switzerland
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Switzerland1 site
Cantonal Hospital of Lucerne
Lucerne, Canton Lucerne, 6000
About this study
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
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.
Read the 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
- Being tested
- An US Readmission Risk Prediction Model, LACE+ score, SQLAPE model
- Keywords
- Prediction model, hospital readmission, external validation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
An US Readmission Risk Prediction ModelOther
Logistic regression model that predicts the risk of all-cause unplanned readmissions developed by the privately held healthcare software company EPIC.
LACE+ scoreOther
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 modelOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: An US Readmission Risk Prediction Model; Other: LACE+ score; Other: SQLAPE model | |
| Non-Readmitted inpatients/Controls | Outcome 1 \& 2: Patients who were not readmitted within 30 days of index hospitalization discharge date. Receives: Other: An US Readmission Risk Prediction Model; Other: LACE+ score; Other: SQLAPE model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Discrimination at 18 days
For discrimination of the scores under investigation, the area under the receiver operating characteristics curves (AUC) will be calculated.
Measured: 18 days after index discharge date
Discrimination at 30 days
For discrimination of the scores under investigation, the area under the receiver operating characteristics curves (AUC) will be calculated.
Measured: 30 days after index discharge date
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.
Measured: 18 days after index discharge date
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.
Measured: 30 days after index discharge date
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.)
Measured: 18 days after index discharge date
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.)
Measured: 30 days after index discharge date
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.
Measured: 18 days after index discharge date
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.
Measured: 30 days after index discharge date
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
Measured: 18 days after index discharge date
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
Measured: 30 days after index discharge date
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Mar 2020
- Study start10 Mar 2020
- Primary completion10 Apr 2020
- Study completion1 Oct 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Hospital ReadmissionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 23,116 participants (actual)
Sponsor and investigators
- Lead sponsor
- Luzerner Kantonsspital
- Sponsor type
- University, hospital or other
- Collaborators
- Universität Luzern
- Responsible party
- Principal investigator, Aljoscha Hwang, Luzerner Kantonsspital
- Investigators
- Aljoscha B. Hwang, Principal investigator, University Lucerne (Switzerland)Stefan Boes, Principal investigator, University Lucerne (Switzerland)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04306172
- Sponsor's protocol ID
- LUKS_RRM_2019
- Status last verified
- Oct 2020
- First submitted
- 6 Mar 2020
- Last updated
- 20 Oct 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 23696773Halfon 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.
PubMed 17063128
Study at a glance
From the registry record, last updated by the sponsor on 20 Oct 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT04306172
- Acronym / sponsor ID
- LUKS_RRM_2019
- Conditions
- Hospital Readmission
- Keywords
- Prediction model, hospital readmission, external validation
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 23,116 participants (actual)
- Lead sponsor
- Luzerner KantonsspitalUniversity, hospital or other
- Collaborators
- Universität Luzern
- Study start
- 10 Mar 2020 (actual)
- Primary completion
- 10 Apr 2020 (actual)
- Study completion
- 1 Oct 2020 (actual)
- First posted
- 12 Mar 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Readmitted inpatients/Cases | – | Other: An US Readmission Risk Prediction Model, Other: LACE+ score, Other: SQLAPE modelOutcome 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. |
| Non-Readmitted inpatients/Controls | – | Other: An US Readmission Risk Prediction Model, Other: LACE+ score, Other: SQLAPE modelOutcome 1 \& 2: Patients who were not readmitted within 30 days of index hospitalization discharge date. |
Who may qualify
- Ages
- 1 year to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Inpatients from an acute care hospital in Central-Switzerland
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (8 listed)
- 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,
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| An US Readmission Risk Prediction ModelLogistic regression model that predicts the risk of all-cause unplanned readmissions developed by the privately held healthcare software company EPIC. | Other | Non-Readmitted inpatients/Controls, Readmitted inpatients/Cases |
| LACE+ scoreThe 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 | Non-Readmitted inpatients/Controls, Readmitted inpatients/Cases |
| SQLAPE modelThe 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. | Other | Non-Readmitted inpatients/Controls, Readmitted inpatients/Cases |
Primary outcome
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.
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.
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.
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.
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.)
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.)
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.
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.
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
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Oct 2020.
- Background: PubMed 23696773van 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.
- Background: PubMed 17063128Halfon 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Aljoscha B. Hwang principal investigator
University Lucerne (Switzerland)
Stefan Boes principal investigator
University Lucerne (Switzerland)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Hospital Readmission, run by Luzerner Kantonsspital at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 8 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Oct 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Discrimination at 18 days (time frame: 18 days after index discharge date). The study lists 10 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04306172, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Oct 2020.