Prediction of Safe Discharge From ICU

Risk Clinical Trial

— SAFEDI  

Official title:

Prediction of Safe Discharge From ICU

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05459350
• Other study ID #: SAFEDI
• Status: Completed
• Start date: June 1, 2022
• Est. completion date: July 31, 2022

Study information

• Verified date: August 2022
• Source: Kepler University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 24010
• Est. completion date: July 31, 2022
• Est. primary completion date: July 31, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Risk

Intervention

Diagnostic Test:
• Safe Discharge Classification
Safe Discharge Classification

Locations

Country	Name	City	State
Austria	Kepler University Hospital	Linz	Upper Austria

Sponsors (1)

Lead Sponsor	Collaborator
Kepler University Hospital

Country where clinical trial is conducted

Austria, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	AUROC for Classification of Safe Discharge	AUROC for Classification of Safe Discharge	2010-01-01 to 2019-10-31
Secondary	Confusion Matrix Value	Confusion Matrix Results: true positives, true negatives, false positive, false negatives and values calculated from these results.	2010-01-01 to 2019-10-31
Secondary	Descriptive Statistics	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