Predicting red blood cell transfusion for primary hip and knee arthroplasty: Designing a hub and spoke high-volume arthroplasty model

Background

Waiting lists for elective surgery are at record levels. Consolidating elective care into superhubs where high-volume surgery can be undertaken efficiently is an attractive solution. These pathways should be safe but also efficient and convenient for patients.

We undertook this study to develop a model that would reliably predict healthy (American Society of Anaesthesiologists [ASA] class 1 and 2) patients who could be treated on a streamlined pathway and who would reliably not require red blood cell transfusion during their inpatient admission.

Methods

We retrospectively identified all patients undergoing primary total hip arthroplasty (THA) or total knee arthroplasty (TKA) at our centre over a five-year period. We used binary logistic regression to develop a predictive model based on these variables.

Results

We identified 13-preoperative candidate variables from our literature search and used these to construct our predictive model. The final validated model was highly effective in predicting those patients who would not need a red blood cell transfusion (area under curve = 0.945).

Conclusion

Our model reliably predicts those healthy (ASA 1 or 2) patients undergoing primary THA or TKA surgery who will not require a red blood cell transfusion during their admission. These patients are suitable for this streamlined pathway without the need for unnecessary preoperative patient tests and travel.

Level of evidence

III.