Arthroplasty surgeons often face a dilemma regarding need for a transfusion which one of the common adverse events after total knee arthroplasty (TKA). As described in this study, the personalized patient counseling and decision-making concerning perioperative blood management for total knee arthroplasty (TKA) patients can be improved by using a nomogram based on 5 preoperative predictors.

The preoperative predictors of transfusion risk in patients after TKA has been explored and preoperative nomogram for predicting transfusion risk has been developed in this study.

The training set of 5402 patients who underwent TKA from September 2013 till November 2018 formed the basis to make the nomogram. Further, the univariate, LASSO, and binary logistic regression analyses recognized the independent predictors of transfusion risk creating a nomogram. It was assessed via area under the curve (AUC), calibration curve, and decision curve analysis (DCA). The study outcomes were authenticated by an autonomous set of 1116 patients who undertook TKA from December 2018 till September 2019. Also, subgroup analyses was done in the training and testing sets as per these predictors 

All in all, 5 predictors were recognized to establish the nomogram. The AUCs of the nomogram were found to be 0.884 (95% CI: 0.865–0.903) and 0.839 (95% CI, 0.773–0.905) in the training and testing sets. The calibration curve indicated that the nomogram predictions were reliable with the actual observation in both the training and testing sets. The DCA favored the nomogram in terms of clinical effectiveness. The AUC of the nomogram was appreciably higher as compared to the AUC of any independent predictor for predicting transfusion risk, and the subgroup analysis performed well in 20 subgroups.

The independent predictors of postoperative transfusion in patients following TKA were: lesser preoperative Hb levels, simultaneous bilateral TKA, lesser BMI, older age, and coronary heart illness. Hence, a nomogram considering these 5 predictors could precisely be useful for predicting the transfusion risk.