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Updated: Jun 24, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Risk factor analysis and establishment of a nomogram model to predict blood loss during total knee arthroplasty
Yikai Liu1,2, Jiangshan Ai3, Xue Teng4
1Department of Joint Surgery, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Purpose:
The risk factors for excessive blood loss and transfusion during total knee arthroplasty (TKA) remain unclear. The present study aimed to determine the risk factors for excessive blood loss and establish a predictive model for postoperative blood transfusion.
Methods:
This retrospective study included 329 patients received TKA, who were randomly assigned to a training set (n = 229) or a test set (n = 100). Univariate and multivariate linear regression analyses were used to determine risk factors for excessive blood loss. Univariate and multivariate logistic regression analyses were used to determine risk factors for blood transfusion. R software was used to establish the prediction model. The accuracy and stability of the models were evaluated using calibration curves, consistency indices, and receiver operating characteristic (ROC) curve analysis.
Results:
Risk factors for excessive blood loss included timing of using a tourniquet, the use of drainage, preoperative ESR, fibrinogen, HCT, ALB, and free fatty acid levels. Predictors in the nomogram included timing of using a tourniquet, the use of drainage, the use of TXA, preoperative ESR, HCT, and albumin levels. The area under the ROC curve was 0.855 (95% CI, 0.800 to 0.910) for the training set and 0.824 (95% CI, 0.740 to 0.909) for the test set. The consistency index values for the training and test sets were 0.855 and 0.824, respectively.
Conclusions:
Risk factors for excessive blood loss during and after TKA were determined, and a satisfactory and reliable nomogram model was designed to predict the risk for postoperative blood transfusion.
