A Novel Scoring System Predicting Red Blood Cell Transfusion Requirements in Patients Undergoing Invasive Spine Surgery

Author:

Schenk Alina1,Ende Jonas2,Hoch Jochen3,Güresir Erdem45,Grabert Josefin2ORCID,Coburn Mark2,Schmid Matthias1,Velten Markus26ORCID

Affiliation:

1. Institute for Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, 53127 Bonn, Germany

2. Department of Anesthesiology and Intensive Care Medicine, University Hospital Bonn, 53127 Bonn, Germany

3. Institute for Experimental Hematology and Transfusion Medicine, University Hospital Bonn, 53127 Bonn, Germany

4. Department of Neurosurgery, University Hospital Bonn, 53127 Bonn, Germany

5. Department of Neurosurgery, University Hospital Leipzig, 04103 Leipzig, Germany

6. Department of Anesthesiology and Pain Management, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

Abstract

Background: Access to blood products is crucial for patient safety during the perioperative course. However, reduced donations and seasonally occurring blood shortages pose a significant challenge to the healthcare system, with surgeries being postponed. The German Blood Transfusion act requires that RBC packages become assigned to an individual patient, resulting in a significant reduction in the available blood products, further aggravating shortages. We aimed to develop a scoring system predicting transfusion probability in patients undergoing spine surgery to reduce assignment and, thus, increase the availability of blood products. Methods: The medical records of 252 patients who underwent spine surgery were evaluated and 18 potential predictors for RBC transfusion were tested to construct a logistic-regression-based predictive scoring system for blood transfusion in patients undergoing spine surgery. Results: The variables found to be the most important included the type of surgery, vertebral body replacement, number of stages, and pre-operative Hb concentration, indicating that surgical specification and the extent of the surgical procedure were more influential than the pre-existing patient condition and medication. Conclusions: Our model showed a good discrimination ability with an average AUC [min, max] of 0.87 [0.6, 0.97] and internal validation with a similar AUC of 0.84 [0.66, 0.97]. In summary, we developed a scoring system to forecast patients’ perioperative transfusion needs when undergoing spine surgery using pre-operative predictors, potentially reducing the need for RBC allocation and, thus, resulting in an increased availability of this valuable resource.

Publisher

MDPI AG

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