Machine Learning-Based Prediction of Major Perioperative Allogeneic Blood Requirements in Cardiac Surgery

NCT04856618 | Completed

• 1 other identifier: PREMATRICS
• Study Type: observational
• Target: 3,782
• Locations: 1 country
• Sites: 1

Brief Summary

Cardiac surgery is one of the clinical surgical specialties that carries a particularly high risk for patients to suffer from severe bleeding perioperatively and consequent anemia, which subsequently requires transfusion of allogeneic blood. Although a surprisingly high number of patients in cardiac surgery do not require perioperative transfusions, it is primarily those patients who do require transfusion who are subsequently at risk for a worse outcome. In recent years many studies have been published discussing measures that can assist physicians in avoiding the triad of anemia, bleeding, and transfusion in cardiac surgery. Within these publications, the implementation of Patient Blood Management (PBM) is advised. PBM is a set of measures aimed at improving patient outcome by reducing perioperative bleeding and thus preventing both anemia and bleeding. The three pillars of this bundle are the preoperative preparation of anemic patients with iron, erythropoietin, folic acid and vitamin B12, the prevention of intraoperative blood loss and the reasonable indication for allogeneic transfusions. Nevertheless, it must be mentioned that the implementation of at least part of these measures is laborious, and full implementation of the recommended bundle is therefore rarely achieved. As a consequence, the full potential of Patient Blood Management is not always realized. Unfortunately this means that transfusion of allogeneic blood cannot be prevented in many patients. A small proportion of patients undergoing cardiac surgery requires a very large amount of allogeneic blood perioperatively. These patients are typically those with a particularly poor outcome. Massive transfusion of allogeneic blood in this situation is an indicator of complications and a cause of increased mortality. Although cardiac surgeons and anesthesiologists believe they can assess which patients are at high risk for hemorrhage, recent publications indicate that there is an urgent need for adequate predictive methods. A variety of studies exist that attempt to predict perioperative transfusion requirements, but to date have been plagued by several limitations. Either the previous publications do not focus on the prediction of massive transfusion of allogeneic blood, i.e. administration of ten or more packed red blood cell units perioperatively, but on much lower transfusion volumes, have only low predictive strength to predict massive transfusion in daily clinical practice, or are hardly usable for true prediction because they use factors (features) that are not strictly present only in the preoperative phase. If an accurate prediction model based on a few features could be created and those patients particularly at risk of massive transfusion of allogeneic blood could be identified, it would subsequently be possible to develop an adapted clinical pathway that would allow patient care to be improved and individualized interventions adapted to the situation to be implemented. In the best case, an adapted care of patients would be possible, which is able to increase the acceptance for the use of even complex measures of patient blood management. This is especially true for measures such as preoperative preparation with iron and/or erythropoietin, the use of a cell saver, and a particularly careful surgical approach. Even if it is difficult to apply all measures of patient blood management in all patients, it would be possible with an approach as described to identify those patients who would benefit most from individualized approaches.

Conditions

Transfusion-dependent Anemia

Keywords

Artificial Intelligence; Machine Learning; Transfusion

Outcome Measures

Primary Outcomes (1)

• AUROC for Classification of Necessity for Massive Transfusion

  AUROC for Classification of Necessity for Massive Transfusion

  2010-01-01 to 2019-12-31

Secondary Outcomes (2)

• Confusion Matrix Values

  2010-01-01 to 2019-12-31

• Descriptive Statistics

  2010-01-01 to 2019-12-31

Study Arms (2)

Massive Transfusion Positive

Massive Transfusion Positive

Biological: Massive Transfusion of Allogeneic Blood

Massive Transfusion Negative

Massive Transfusion Negative

Interventions

Massive Transfusion of Allogeneic Blood BIOLOGICAL

Massive Transfusion of Allogeneic Blood, \> pRBCs

Massive Transfusion Positive

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

As described in the inclusion criteria.

You may qualify if:

• All patients that underwent cardiac surgery at the Kepler University Hospital in the period between 2010-01-01 and 2019-12-31.

You may not qualify if:

• Patients below 18 years of age.
• Presence of congenital heart disease.
• Revision surgery of the same patient.

Sponsors & Collaborators

• Kepler University Hospital: lead

Study Sites (1)

Kepler University Hospital

Linz, Upper Austria, 4021, Austria

Location

Related Publications (1)

• Tschoellitsch T, Bock C, Mahecic TT, Hofmann A, Meier J. Machine learning-based prediction of massive perioperative allogeneic blood transfusion in cardiac surgery. Eur J Anaesthesiol. 2022 Sep 1;39(9):766-773. doi: 10.1097/EJA.0000000000001721. Epub 2022 Jul 20.

  PMID: 35852544 DERIVED

Study Design

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

Study Record Dates

• First Submitted: April 16, 2021
• First Posted: April 23, 2021
• Study Start: June 16, 2021
• Primary Completion: June 30, 2021
• Study Completion: July 20, 2022
• Last Updated: November 29, 2022

Record last verified: 2022-11

Data Sharing

• IPD Sharing: Will not share

Locations

AU (1)