External Validation of the TRACK Allogeneic Transfusion Model in a Dutch Adult Cardiac Surgery Population, and the Effect on Discriminative Ability When Adding Anti-platelet Therapy

Study Overview
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Brief Summary
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The TRACK allogeneic blood transfusion prediction model was developed in 2008 and demonstrated good discriminative ability in patients with increased risk for allogeneic blood transfusion in an all Italian population. At the time of derivation, dual anti-platelet medication was suggested in the treatment of acute coronary syndrome, but not yet fully implemented. The aim of this study is to externally validate the TRACK blood transfusion prediction model in the cardiac surgery population of MST Thoraxcentrum Twente. Additionally, we will study the impact of adding the preoperative use of dual anti-platelet medication, as additional predictive factor, to the TRACK blood transfusion prediction model and hypothesize a positive effect on predictive capacity.

Detailed Description
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Cardiothoracic surgery is associated with increased perioperative blood loss and the need for allogeneic blood and blood product transfusions. This is due to 3 distinct factors, the invasive nature of the surgical procedures, the need for high dose anticoagulation during extracorporeal circulation, and lastly the exposure of blood to the internal surface area of the heart-lung machine. Most cardiac patients use anti-platelet therapy before and after surgery, making hemostasis management in this specific patient population even more complex. European guidelines recommend administering anti-platelet therapy using Aspirin in combination with P2Y12 receptor inhibitors such as Clopidogrel, Prasugrel or Ticagrelor, also known as dual anti-platelet therapy (DAPT) in patients with acute coronary syndrome. Making distinct decisions regarding individual patient hemostasis management remains challenging. Decision making supported by prediction models, i.e., EuroSCORE is well established in the cardiac surgery population. A few models pertaining specifically to allogeneic blood transfusion have been created and externally validated. Most of these prediction models perform reasonably well, predicting red blood cell transfusions with 70-80% accuracy, depending on the model and number of prediction factors used. Some are even excellent for predicting the chance of severe post-operative bleeding. As the transfusion of even one unit of allogeneic blood transfusion impacts mortality, the choice for the best feasible prediction model for routine clinical practice that reflects daily practice, uses a limited number of predictive factors, has a predictive capacity (>70%), and discriminates between risk groups for allogeneic blood transfusion is desirable. Transfusion Risk and Clinical Knowledge (TRACK) model validation and optimisation The Transfusion Risk and Clinical Knowledge (TRACK) model was developed in 2008 in an Italian adult cardiac surgery population of 8989 cardiac surgery patients and consists of the following 5 predictive factors: sex, age, weight, pre-operative haematocrit and complexity of surgery (6). The decision to validate the TRACK model was based on its simplicity and relatively high predictive capacity, in comparison to other models with higher numbers of complex factors. This model has an allogeneic blood transfusion predictive capacity of 72% and uses a point system to divide patients into different risk groups, according to the total number of points allocated. During the derivation of this model in 2008, dual anti-platelet medication was included, but no significant association was found. In the 12 years since development, the popularity of dual anti-platelet medication used in acute coronary syndrome patients has significantly increased and its association with post-operative bleeding and allogeneic blood transfusion has been suggested. Leunissen et al. suggest that platelet activity may play a significant role in the prediction of post-operative bleeding, and Li et al. found that adding platelet activity to the CRUSADE score showed a significant increase in predicting risk of major bleeding in ACS patients. A re-evaluation of the association between DAPT and allogeneic blood transfusion is necessary. This will be done by the addition of DAPT as an extra predictive factor to the TRACK model, during external validation in our population. The negative association between mortality and transfusion products is well known. In addition, the related significant increase in hospital costs makes better perioperative hemostasis management crucial. Identifying cardiac surgery patients at risk for blood transfusion pre-operatively would aid clinicians in modifying the perioperative approach with goal the prevention of unnecessary allogeneic blood transfusion and the associated complications thereof. Validating this model might aid clinicians in reducing allogeneic blood transfusions, transfusion complications and associated costs. Ultimately this might aid for development of patient specific transfusion strategies and new blood management protocols. The aim of this study is to externally validate the TRACK blood transfusion prediction model in the cardiac surgery population of MST Thoraxcentrum Twente. Additionally, we will study the impact of adding the preoperative use of dual anti-platelet medication, as additional predictive factor, to the TRACK blood transfusion prediction model and hypothesize a positive effect on predictive capacity.

Official Title
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External Validation of the TRACK Allogeneic Transfusion Model in a Dutch Adult Cardiac Surgery Population, and the Effect on Discriminative Ability When Adding Anti-platelet Therapy

Conditions
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P2Y12 Receptor Inhibitor, Blood Transfusion, Prediction Model Validation

Intervention / Treatment
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* Other: TRACK-TCT


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients receiving on-pump cardiac surgery Exclusion Criteria: Patients who opted out for reuse of their data for scientific purposes

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| TRACK<br>External validation of TRACK prediction model with 5 variables: age, weight, sex, pre-op HCT, Type of surgery. | |
| TRACK-TCT<br>New model development with 6 variables. 5 From the TRACK model: age, weight, sex, pre-op HCT, Type of surgery. A sixth variable will be added i.e.: pre-operative P2Y12 drug use | Other: TRACK-TCT<br>* An extra variable will be added to an existing prediction model. It is hypothesized that the predictive ability will improve and that better distinction could be made between patients with an increased risk for receiving blood transfusions.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| External validation TRACK blood prediction Model (TRACK) | Does adding P2Y12 inhibitors as extra variable to the TRACK model after validation improve the predictive capacity? This will be done by validation of the TRACK model in the cardiac surgery population of TCT by calculating the discriminative ability. The change in discriminative ability, after correction for optimism, when the pre-operative use of P2Y12 inhibitors is added as extra variable will be assessed and the net improvement in reclassification will be calculated. | 1 year | 
| Evaluation of the change in predictive capacity when adding P2Y12 as extra variable (TRACK-TCT) | Does adding P2Y12 inhibitors as extra variable to the TRACK model after validation improve the predictive capacity? This will be done by validation of the TRACK model in the cardiac surgery population of TCT by calculating the discriminative ability. The change in discriminative ability, after correction for optimism, when the pre-operative use of P2Y12 inhibitors is added as extra variable will be assessed and the net improvement in reclassification will be calculated. | 1 year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Male vs Female | How does the performance of the model change in an all-male and all-female population. | 1 year | 
| Post operative complication in patients that received a blood transfusion | Does allogeneic blood transfusion increase the risk of post-operative complications or thromboembolic events? | 1 year | 
| Mortality difference in patients that received a blood transfusion | What is the difference in 30-day mortality between patients receiving allogeneic blood transfusions and patients that does not? | 1 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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External validation, Blood transfusion, P2Y12 Receptor Inhibitor, Prediction Model