Prediction of Transfusion-Associated Complications

NCT05466370 | Completed DMC

• 1 other identifier: PRETRACO
• Study Type: observational
• Target: 3,366
• Locations: 1 country
• Sites: 1

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.

Conditions

Transfusion-dependent Anemia

Keywords

Artificial Intelligence; Machine Learning; Transfusion; Complications

Outcome Measures

Primary Outcomes (3)

• 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 Outcomes (2)

• Confusion Matrix

  2016-10-31 to 2020-08-31

• Descriptive Statistics

  2016-10-31 to 2020-08-31

Study Arms (4)

AKI

Acute Kidney Injury

Biological: Transfusion of Allogeneic Blood

ARF

Acute Respiratory Failure

Biological: Transfusion of Allogeneic Blood

AKI and ARF

Acute Kidney Injury and Acute Respiratory Failure

Biological: Transfusion of Allogeneic Blood

no complication

no complication

Biological: Transfusion of Allogeneic Blood

Interventions

Transfusion of Allogeneic Blood BIOLOGICAL

Transfusion of Allogeneic Blood

AKI; AKI and ARF; ARF; no complication

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

As described in the inclusion criteria.

You may qualify if:

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

You may not qualify if:

• None.

Sponsors & Collaborators

• Kepler University Hospital: lead

Study Sites (1)

Kepler University Hospital

Linz, Upper Austria, 4021, Austria

Location

Related Publications (1)

• Tschoellitsch T, Moser P, Maletzky A, Seidl P, Bock C, Roland T, Ludwig H, Sussner S, Hochreiter S, Meier J. Potential Predictors for Deterioration of Renal Function After Transfusion. Anesth Analg. 2024 Mar 1;138(3):645-654. doi: 10.1213/ANE.0000000000006720. Epub 2024 Feb 16.

  PMID: 38364244 DERIVED

Study Officials

• Thomas Tschoellitsch, MD

  Kepler University Hospital and Johannes Kepler University, Linz, Austria

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 12, 2022
• First Posted: July 20, 2022
• Study Start: July 15, 2022
• Primary Completion: July 31, 2022
• Study Completion: July 31, 2022
• Last Updated: April 26, 2023

Record last verified: 2023-04

Data Sharing

• IPD Sharing: Will not share

Locations

AU (1)