Prediction of Transfusion-Associated Complications

NCT ID: NCT05466370

Last Updated: 2023-04-26

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

3366 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-07-15

Study Completion Date

2022-07-31

Brief Summary

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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.

Detailed Description

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Conditions

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Transfusion-dependent Anemia

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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AKI

Acute Kidney Injury

Transfusion of Allogeneic Blood

Intervention Type BIOLOGICAL

Transfusion of Allogeneic Blood

ARF

Acute Respiratory Failure

Transfusion of Allogeneic Blood

Intervention Type BIOLOGICAL

Transfusion of Allogeneic Blood

AKI and ARF

Acute Kidney Injury and Acute Respiratory Failure

Transfusion of Allogeneic Blood

Intervention Type BIOLOGICAL

Transfusion of Allogeneic Blood

no complication

no complication

Transfusion of Allogeneic Blood

Intervention Type BIOLOGICAL

Transfusion of Allogeneic Blood

Interventions

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Transfusion of Allogeneic Blood

Transfusion of Allogeneic Blood

Intervention Type BIOLOGICAL

Eligibility Criteria

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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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Kepler University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Thomas Tschoellitsch, MD

Role: PRINCIPAL_INVESTIGATOR

Kepler University Hospital and Johannes Kepler University, Linz, Austria

Locations

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Kepler University Hospital

Linz, Upper Austria, Austria

Site Status

Countries

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Austria

References

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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.

Reference Type DERIVED
PMID: 38364244 (View on PubMed)

Other Identifiers

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PRETRACO

Identifier Type: -

Identifier Source: org_study_id

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