Prediction of Transfusion-Associated Complications (PRETRACO)

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.

Study Overview

• Status: Completed
• Conditions: Transfusion-dependent Anemia
• Intervention / Treatment: Biological: Transfusion of Allogeneic Blood

• Study Type: Observational
• Enrollment (Actual): 3366

Contacts and Locations

Study Locations

• Austria
  • Upper Austria
    • Linz, Upper Austria, Austria, 4021
      • Kepler University Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

As described in the inclusion criteria.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
AKI; Acute Kidney Injury	Biological: Transfusion of Allogeneic Blood; Transfusion of Allogeneic Blood
ARF; Acute Respiratory Failure	Biological: Transfusion of Allogeneic Blood; Transfusion of Allogeneic Blood
AKI and ARF; Acute Kidney Injury and Acute Respiratory Failure	Biological: Transfusion of Allogeneic Blood; Transfusion of Allogeneic Blood
no complication	Biological: Transfusion of Allogeneic Blood; Transfusion of Allogeneic Blood

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
AUROC for Classification of AKI	AUROC for Classification of AKI	2016-10-31 to 2020-08-31
AUROC for Classification of ARF	AUROC for Classification of ARF	2016-10-31 to 2020-08-31
AUROC for Classification of AKI and ARF	AUROC for Classification of AKI and ARF	2016-10-31 to 2020-08-31

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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
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

Collaborators and Investigators

• Sponsor: Kepler University Hospital
• Investigators: Principal Investigator: Thomas Tschoellitsch, MD, Kepler University Hospital and Johannes Kepler University, Linz, Austria

Study record dates

Study Major Dates

• Study Start (Actual): July 15, 2022
• Primary Completion (Actual): July 31, 2022
• Study Completion (Actual): July 31, 2022

Study Registration Dates

• First Submitted: July 12, 2022
• First Submitted That Met QC Criteria: July 17, 2022
• First Posted (Actual): July 20, 2022

Study Record Updates

• Last Update Posted (Actual): April 26, 2023
• Last Update Submitted That Met QC Criteria: April 24, 2023
• Last Verified: April 1, 2023

More Information

Terms related to this study

• Keywords: Complications; Artificial Intelligence; Transfusion; Machine Learning
• Other Study ID Numbers: PRETRACO

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No