• Josephine To, Romi Sinha, Susan W Kim, Kathryn Robinson, Brendon Kearney, Donald Howie, and Luen Bik To.
• From the Division of Aged Care, Rehabilitation and Palliative Care, Modbury Hospital, Modbury, South Australia, Australia (J.T.); School of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia (J.T.); Blood, Organ and Tissue Programs, Public Health and Clinical Systems, Department of Health, Adelaide, South Australia, Australia (R.S.); Faculty of Health Sciences (R.S.) and Flinders Centre for Epidemiology and Biostatistics, School of Medicine (S.W.K.), Flinders University, Bedford Park, South Australia, Australia; South Australia Bloodsafe Program, Adelaide, Australia (K.R.); Queen Elizabeth Hospital, Woodville, South Australia, Australia (K.R.); Departments of Haematology (B.K.) and Orthopaedics and Trauma (D.H.), Clinical Haematology Service (L.B.T.), and Clinical Section (L.B.T.), Royal Adelaide Hospital, Adelaide, South Australia, Australia; and Discipline of Orthopaedics and Trauma (D.H.) and Clinical Pathology (L.B.T.), University of Adelaide, Adelaide, South Australia, Australia.
• Anesthesiology. 2017 Aug 1; 127 (2): 317-325.

BackgroundPreoperative anemia is a significant predictor of perioperative erythrocyte transfusion in elective arthroplasty patients. However, interactions with other patient and procedure characteristics predicting transfusion requirements have not been well studied.MethodsPatients undergoing elective primary total hip arthroplasty or total knee arthroplasty at a tertiary hospital in Adelaide, South Australia, Australia, from January 2010 to June 2014 were used to identify preoperative predictors of perioperative transfusion. A logistic regression model was developed and externally validated with an independent data set from three other hospitals in Adelaide.ResultsAltogether, 737 adult patients in the derivation group and 653 patients in the validation group were included. Binary logistic regression modeling identified preoperative hemoglobin (odds ratio, 0.51; 95% CI, 0.43 to 0.59; P < 0.001 for each 1 g/dl increase), total hip arthroplasty (odds ratio, 3.56; 95% CI, 2.39 to 5.30; P < 0.001), and females 65 yr of age and older (odds ratio, 3.37; 95% CI, 1.88 to 6.04; P = 0.01) as predictors of transfusion in the derivation cohort.ConclusionsUsing a combination of patient-specific preoperative variables, this validated model can predict transfusion in patients undergoing elective hip and knee arthroplasty. The model may also help to identify patients whose need for transfusion may be decreased through preoperative hemoglobin optimization.

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