Clinical trial NCT05466370: Prediction of Transfusion-Associated Complications
Official title: Prediction of Transfusion-Associated Complications
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Transfusion-dependent Anemia
Keywords: Artificial Intelligence
Machine Learning
Transfusion
Complications
Lead sponsor: Kepler University Hospital
Sponsor class: OTHER
First posted: 2022-07-20
Enrollment: 3366
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: As described in the inclusion criteria.

Brief summary: 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.


Eligibility criteria: 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.

Primary outcomes:
measure: AUROC for Classification of AKI
timeFrame: 2016-10-31 to 2020-08-31
measure: AUROC for Classification of ARF
timeFrame: 2016-10-31 to 2020-08-31
measure: AUROC for Classification of AKI and ARF
timeFrame: 2016-10-31 to 2020-08-31
Secondary outcomes:
measure: Confusion Matrix
timeFrame: 2016-10-31 to 2020-08-31
measure: Descriptive Statistics
timeFrame: 2016-10-31 to 2020-08-31
Other outcomes:
Locations:
city: Linz
country: Austria
facility: Kepler University Hospital
state: Upper Austria
Study officials:
affiliation: Kepler University Hospital and Johannes Kepler University, Linz, Austria
name: Thomas Tschoellitsch, MD
role: PRINCIPAL_INVESTIGATOR
References: