Prediction of Transfusion-Associated Complications (PRETRACO)

Kepler University Hospital

Status: Completed

Conditions: Transfusion-dependent Anemia

Treatments: Biological: Transfusion of Allogeneic Blood

Study type: Observational

Funder types: Other

Identifiers

NCT05466370

PRETRACO

Details and patient eligibility

About

Currently, about 350000 red blood cell concentrates are produced from blood donations in Austria every year.

In addition to the main effect of replacing lost blood, red blood cell concentrates also have many undesirable effects - from blood group compatibilities, which are easily avoidable due to care, to storage-related side effects, to mostly intensive care problems as a result of massive transfusions, to system-wide effects such as TRALI, TACO and TRIM.

Before being administered to patients, red blood cell concentrates undergo an extensive quality assurance process in which a large number of parameters are collected. Prior to use on patients, for example, bedside tests and tests for further incompatibilities with a blood sample from the intended patient are performed. With the implementation of Patient Blood Management (PBM) in recent years, the use of red cell concentrates has become more targeted - the number of transfusions is decreasing in most developed countries. However, it is still possible to suffer transfusion-related adverse events (TRAE). Thus, active research activity to reduce these TRAEs continues to be called for.

To date, however, it is not known which patients experience transfusion-related adverse events. Despite the broad measures of hemovigilance and pre-transfusion testing, it is still not possible to predict which individual patient will respond to a transfusion with a typical adverse event such as hypotension, hemolysis, renal failure, or TRALI. It seems understandable that characteristics of the patient as well as characteristics of the administered unit could play a role for this. In particular, it is conceivable that a combination of characteristics of the blood unit and characteristics of the patient could determine a complication in the course of administration. For this reason, it seems attractive to use artificial intelligence and machine learning methods to predict any complications.

Enrollment: 3,366 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• All adult patients that received at a blood transfusion the Kepler University Hospital in the period between 2016-10-31 to 2020-08-31.

Exclusion criteria

None.

Trial design

3,366 participants in 4 patient groups

AKI

Description:

Acute Kidney Injury

Treatment:

Biological: Transfusion of Allogeneic Blood

ARF

Description:

Acute Respiratory Failure

Treatment:

Biological: Transfusion of Allogeneic Blood

AKI and ARF

Description:

Acute Kidney Injury and Acute Respiratory Failure

Treatment:

Biological: Transfusion of Allogeneic Blood

no complication

Description:

no complication

Treatment:

Biological: Transfusion of Allogeneic Blood