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Updated: Jul 18, 2025
A Precision Medicine Tool for Measurement and Monitoring of Hemoglobin S in Sickle Cell Disease Patients Receiving Transfusion Therapy
Development of a Novel Prediction Model for Red Blood Cell Transfusion Risk in Cardiac Surgery
Ordoño Alonso-Tuñón1, Manuel Bertomeu-Cornejo1, Isabel Castillo-Cantero2
1Department of Anesthesia and Reanimation, Virgen del Rocio University Hospital, 41013 Seville, Spain.
Insights
A new model predicts red blood cell transfusion risk in cardiac surgery patients. This tool helps optimize blood product use, crucial for scarce resources.
Area of Science:
- Cardiology
- Transfusion Medicine
- Surgical Outcomes
Background:
- Cardiac surgery necessitates blood transfusions due to blood loss.
- Blood products are scarce and costly resources.
- Standardized methods are needed to determine transfusion requirements in cardiac surgery.
Purpose of the Study:
- To develop a simple prediction model for red blood cell transfusion risk in cardiac surgery.
- To identify key predictors of transfusion need in this patient population.
Main Methods:
- Retrospective analysis of adult cardiac surgery patients (2017-2019).
- Multivariable logistic regression model developed to predict transfusion risk.
- Model performance evaluated using AUC and Hosmer-Lemeshow test.
Main Results:
- 1234 patients included; 20.9% required transfusion.
- Predictors of transfusion: age > 60, female sex, BMI > 30, perioperative hemoglobin < 14 g/dL, and combined surgery.
- The model achieved an AUC of 80.9% for transfusion risk prediction.
Conclusions:
- A parsimonious and efficient model for predicting cardiac surgery transfusion risk was developed.
- The model demonstrates good discriminatory ability.
- This tool can aid in optimizing blood product utilization.
Background:
Cardiac surgery is a complex and invasive procedure that often requires blood transfusions to replace the blood lost during surgery. Blood products are a scarce and expensive resource. Therefore, it is essential to develop a standardized approach to determine the need for blood transfusions in cardiac surgery. The main objective of our study is to develop a simple prediction model for determining the risk of red blood cell transfusion in cardiac surgery.
Methods:
Retrospective cohorts of adult patients who underwent cardiac surgery between 2017 and 2019 were studied to identify hypothetical predictors of blood transfusion. Finally, a multivariable logistic regression model was developed to predict the risk of transfusion in cardiac surgery using the AUC and the Hosmer-Lemeshow goodness-of-fit test.
Results:
We included 1234 patients who underwent cardiac surgery. Of the entire cohort, 875 patients underwent a cardiac procedure 69.4% [CI 95% (66.8%; 72.0%)]; 119 patients 9.6% [CI 95% (8.1%; 11.4%)] underwent a combined procedure, and 258 patients 20.9% [CI 95% (18.7; 23.2)] underwent other cardiac procedures. The median perioperative hemoglobin was 13.0 mg/dL IQR (11.7; 14.2). The factors associated with the risk of transfusion were age > 60 years OR 1.37 CI 95% (1.02; 1.83); sex female OR 1.67 CI 95% (1.24; 2.24); BMI > 30 OR 1.46 (1.10; 1.93); perioperative hemoglobin < 14 OR 2.11 to 51.41 and combined surgery OR 3.97 CI 95% (2.19; 7.17). The final model shows an AUC of 80.9% for the transfusion risk prediction [IC 95% (78.5-83.3%)]; p < 0.001].
Conclusions:
We have developed a model with good discriminatory ability, which is more parsimonious and efficient than other models.
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