- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05459350
Prediction of Safe Discharge From ICU (SAFEDI)
Patients who have an increased need for monitoring or therapy during their stay in hospital are typically admitted to an intensive care unit. This is characterized by a large number of diagnostic and therapeutic options. If this additional effort is no longer necessary, then typically in most hospitals patients are transferred to wards with a lower presence of nurses and physicians and reduced provision of extensive monitoring and therapeutic procedures such as organ replacement procedures.
However, deintensification of medical and nursing care requires that previously monitored and partially supported bodily functions are restored to the point where further monitoring is no longer necessary. For this reason, transfer from an intensive care unit to the normal inpatient area is only possible if the patient in question has neither an increased need for monitoring nor an increased need for therapy. If this is not the case, then there is a risk of life-threatening conditions in the normal ward, which can sometimes occur very quickly. However, the need for further monitoring, or for continued intensive medical therapy, cannot be easily assessed. There is no laboratory value or clinical examination method that can be used to estimate beyond doubt whether a patient's condition could worsen if he or she is transferred to the normal ward. For this reason, the decision to transfer is made on the basis of the individual assessment by the attending physician. Although this is based on the synopsis of a wide variety of examinations and laboratory findings, it is therefore subject to large interindividual variations. Thus, the personal experience of the evaluating physician has a considerable influence on the decision for or against a transfer to the normal inpatient area.
In this respect, the decision to deintensify therapy, i.e. to transfer patients from intensive care units to the normal care area, is challenging:
The assessing physician has to make a prediction from the combination of the available findings under time pressure whether a transfer to the normal inpatient area is possible without endangering the patient. In this situation, it would be desirable to have an automated warning system that could describe the success of the transfer with sufficient accuracy in the presence of specific laboratory constellations. In the best case, such an approach would prevent dangerous transfers, but at the same time reduce unnecessary lengths of stay in the ICU. Machine learning methods seem particularly suited to support such a decision.
Study Overview
Status
Conditions
Intervention / Treatment
Study Type
Enrollment (Actual)
Contacts and Locations
Study Locations
-
-
Upper Austria
-
Linz, Upper Austria, Austria, 4021
- Kepler University Hospital
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- All adult patients that were treated in intensive care units at the Kepler University Hospital in Linz, Austria in the period 2010-01-01 to 2019-10-31.
Exclusion Criteria:
- None.
Study Plan
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Retrospective
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Safe Discharge Positive
|
Safe Discharge Classification
|
Safe Discharge Negative
|
Safe Discharge Classification
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
AUROC for Classification of Safe Discharge
Time Frame: 2010-01-01 to 2019-10-31
|
AUROC for Classification of Safe Discharge
|
2010-01-01 to 2019-10-31
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Confusion Matrix Value
Time Frame: 2010-01-01 to 2019-10-31
|
Confusion Matrix Results: true positives, true negatives, false positive, false negatives and values calculated from these results.
|
2010-01-01 to 2019-10-31
|
Descriptive Statistics
Time Frame: 2010-01-01 to 2019-10-31
|
Descriptive Statistics (age in years, height in cm, weight in kg, gender as male/female, date of death, standard laboratory measurements (e. g. blood gas analysis, full blood count, liver function tests, kidney function tests), ICD 10-codes associated with the patient's admission, Glasgow Coma Scale) This outcome measure will compare the individual feature (e. g. height in cm) in one group vs. the other. Significant difference will be described by p-value. |
2010-01-01 to 2019-10-31
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Thomas Tschoellitsch, MD, Kepler University Hospital and Johannes Kepler University, Linz, Austria
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
- SAFEDI
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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