Prediction of Safe Discharge From ICU

NCT ID: NCT05459350

Last Updated: 2022-08-17

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 24010 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2022-06-01
• Study Completion Date: 2022-07-31

Brief 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.

Conditions

Risk

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Safe Discharge Positive

Safe Discharge Positive

Safe Discharge Classification

Intervention Type: DIAGNOSTIC_TEST

Safe Discharge Classification

Safe Discharge Negative

Safe Discharge Negative

Safe Discharge Classification

Intervention Type: DIAGNOSTIC_TEST

Safe Discharge Classification

Interventions

Safe Discharge Classification

Safe Discharge Classification

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Kepler University Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Thomas Tschoellitsch, MD

Role: PRINCIPAL_INVESTIGATOR

Kepler University Hospital and Johannes Kepler University, Linz, Austria

Locations

Kepler University Hospital

Linz, Upper Austria, Austria

Countries

Austria

Other Identifiers

SAFEDI

Identifier Type: -

Identifier Source: org_study_id