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Anaesth Intensive Care · Sep 2018
An audit of perioperative blood transfusions in a regional hospital to rationalise a maximum surgical blood ordering schedule.
- R M Blank, S P Blank, and H E Roberts.
- University of Melbourne, Rural Clinical School; Shepparton, Victoria.
- Anaesth Intensive Care. 2018 Sep 1; 46 (5): 498-503.
AbstractAppropriate preoperative blood typing and cross-matching is an important quality improvement target to minimise costs and rationalise the use of blood bank resources. This can be facilitated using a maximum surgical blood ordering schedule (MSBOS) for specific operations. It is recommended that individual hospitals develop a site-specific MSBOS based on institutional data, but this is challenging in non-tertiary centres without electronic databases. Our aim was to audit our perioperative blood transfusions to develop a site-specific MSBOS. A retrospective audit of blood transfusions in surgical patients in our regional referral hospital was conducted using five years' coded administrative data. Procedures with higher transfusion rates warranting preoperative testing (type and screen with or without subsequent cross-matching) were identified. There were about 15,000 eligible surgical procedures performed in our institution over the audit period. The need for preoperative testing was identified for only a few procedures, namely laparotomy, bowel resection, major amputation, joint arthroplasty, hip/femur fracture and humerus surgery, and procedures for obstetric complications. We observed a reduction in transfusion rates over time for total joint arthroplasty. The use of coding data represents an efficient method by which centres without electronic anaesthesia information management systems can conduct large-scale audits to develop a site-specific MSBOS. This would represent a significant improvement for hospitals that currently base preoperative testing recommendations on expert opinion alone. As many procedures in regional centres have very low transfusion rates, hospitals with a similar case mix to ours could consider selectively auditing higher-risk operations where local data is most likely to alter testing recommendations.
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