Prediction of Transfusion-Associated Complications

Transfusion-dependent Anemia Clinical Trial

— PRETRACO  

Official title:

Prediction of Transfusion-Associated Complications

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05466370
• Other study ID #: PRETRACO
• Status: Completed
• Start date: July 15, 2022
• Est. completion date: July 31, 2022

Study information

• Verified date: April 2023
• Source: Kepler University Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 3366
• Est. completion date: July 31, 2022
• Est. primary completion date: July 31, 2022
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Transfusion-dependent Anemia

Intervention

Biological:
• Transfusion of Allogeneic Blood
Transfusion of Allogeneic Blood

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 AKI	AUROC for Classification of AKI	2016-10-31 to 2020-08-31
Primary	AUROC for Classification of ARF	AUROC for Classification of ARF	2016-10-31 to 2020-08-31
Primary	AUROC for Classification of AKI and ARF	AUROC for Classification of AKI and ARF	2016-10-31 to 2020-08-31
Secondary	Confusion Matrix	Confusion Matrix Results: true positives, true negatives, false positive, false negatives and values calculated from these results.	2016-10-31 to 2020-08-31
Secondary	Descriptive Statistics	Descriptive Statistics (age in years, gender as male/female, blood group as A, B, 0, AB, hemoglobin in g/dl, platelets in count/microliter, leukocytes in cells per liter, CRP in mg/dl, creatinine in mg/dl, GFR ml/minute, glucose in mg/dl, potassium in mmol/l, TT, aPTT in seconds, ALT U/liter, paO2 mmHg, Rhesus type as positive or negative, height in cm, weight in kg, body temperature in kg, age of blood in days, cholesterine in mg/dl, systolic blood pressure in mmHg, diastolic blood pressure in mmHg); 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.	2016-10-31 to 2020-08-31