BACKGROUND: Data can be collected for various purposes with anesthesia information management systems. The authors describe methods for using data acquired from an anesthesia information management system to assess intraoperative utilization of blood and blood components. METHODS: Over an 18-month period, data were collected on 48,086 surgical patients at a tertiary care academic medical center. All data were acquired with an automated anesthesia recordkeeping system. Detailed reports were generated for blood and blood component utilization according to surgical service and surgical procedure, and for individual surgeons and anesthesiologists. Transfusion hemoglobin trigger and target concentrations were compared among surgical services and procedures, and between individual medical providers. RESULTS: For all patients given erythrocytes, the mean transfusion hemoglobin trigger was 8.4 ± 1.5, and the target was 10.2 ± 1.5 g/dl. Variation was significant among surgical services (trigger range: 7.5 ± 1.2-9.5 ± 1.1, P = 0.0001; target range: 9.1 ± 1.2-11.3 ± 1.4 g/dl, P = 0.002), surgeons (trigger range: 7.2 ± 0.7-9.8 ± 1.0, P = 0.001; target range: 8.8 ± 0.9-11.8 ± 1.3 g/dl, P = 0.001), and anesthesiologists (trigger range: 7.2 ± 0.8-9.6 ± 1.2, P = 0.001; target range: 9.0 ± 0.9-11.7 ± 1.3 g/dl, P = 0.0004). The use of erythrocyte salvage, fresh frozen plasma, and platelets varied threefold to fourfold among individual surgeons compared with their peers performing the same surgical procedure. CONCLUSIONS: The use of data acquired from an anesthesia information management system allowed a detailed analysis of blood component utilization, which revealed significant variation among surgical services and surgical procedures, and among individual anesthesiologists and surgeons compared with their peers. Incorporating these methods of data acquisition and analysis into a blood management program could reduce unnecessary transfusions, an outcome that may increase patient safety and reduce costs.
BACKGROUND: Data can be collected for various purposes with anesthesia information management systems. The authors describe methods for using data acquired from an anesthesia information management system to assess intraoperative utilization of blood and blood components. METHODS: Over an 18-month period, data were collected on 48,086 surgical patients at a tertiary care academic medical center. All data were acquired with an automated anesthesia recordkeeping system. Detailed reports were generated for blood and blood component utilization according to surgical service and surgical procedure, and for individual surgeons and anesthesiologists. Transfusion hemoglobin trigger and target concentrations were compared among surgical services and procedures, and between individual medical providers. RESULTS: For all patients given erythrocytes, the mean transfusion hemoglobin trigger was 8.4 ± 1.5, and the target was 10.2 ± 1.5 g/dl. Variation was significant among surgical services (trigger range: 7.5 ± 1.2-9.5 ± 1.1, P = 0.0001; target range: 9.1 ± 1.2-11.3 ± 1.4 g/dl, P = 0.002), surgeons (trigger range: 7.2 ± 0.7-9.8 ± 1.0, P = 0.001; target range: 8.8 ± 0.9-11.8 ± 1.3 g/dl, P = 0.001), and anesthesiologists (trigger range: 7.2 ± 0.8-9.6 ± 1.2, P = 0.001; target range: 9.0 ± 0.9-11.7 ± 1.3 g/dl, P = 0.0004). The use of erythrocyte salvage, fresh frozen plasma, and platelets varied threefold to fourfold among individual surgeons compared with their peers performing the same surgical procedure. CONCLUSIONS: The use of data acquired from an anesthesia information management system allowed a detailed analysis of blood component utilization, which revealed significant variation among surgical services and surgical procedures, and among individual anesthesiologists and surgeons compared with their peers. Incorporating these methods of data acquisition and analysis into a blood management program could reduce unnecessary transfusions, an outcome that may increase patient safety and reduce costs.
Authors: Charles H Brown; William J Savage; Courtney G Masear; Jeremy D Walston; Jing Tian; Elizabeth Colantuoni; Charles W Hogue; Steven M Frank Journal: Anesth Analg Date: 2014-06 Impact factor: 5.108
Authors: Osman N Salaria; Viachaslau M Barodka; Charles W Hogue; Dan E Berkowitz; Paul M Ness; Jack O Wasey; Steven M Frank Journal: Anesth Analg Date: 2014-06 Impact factor: 5.108