Clinical prediction models for postoperative blood transfusion after total knee arthroplasty: a systematic review and meta-analysis.

Abstract

Background: Postoperative blood transfusion remains a significant concern following total knee arthroplasty. Clinical prediction models can facilitate early identification of patients at risk, enabling targeted blood management to reduce unnecessary transfusions and related complications. However, the predictive performance, methodological quality, and clinical applicability of these models remain uncertain. Therefore, we systematically reviewed existing models for predicting postoperative transfusion in total knee arthroplasty.

Methods: Ten English and Chinese databases were comprehensively searched from database inception to February 2025 to identify relevant studies. Two reviewers independently extracted data based on the checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). The risk of bias and the applicability of each study was evaluated applying the Prediction model Risk Of Bias Assessment Tool (PROBAST). Extracted AUC of included models were pooled and analyzed utilizing a random-effects meta-analysis. A leave-one-out sensitivity analysis and an exploratory subgroup meta-analysis by modelling approach were also conducted to explore the sources of heterogeneity. All statistical analyses were performed in Stata 17.0 software.

Results: Twelve studies involving eighteen models were incorporated in this review. All studies established the prediction models employing logistic regression or machine learning methods. The most commonly used predictors were preoperative hemoglobin, age, body mass index, surgery duration, and the use of tranexamic acid. The pooled AUC for the six internally validated models was 0.83 (95% CI: 0.74-0.92), demonstrating a relatively high predictive discrimination. Sensitivity analysis did not materially change the estimates, and the subgroup meta-analyses showed that the modelling approach alone could not explain the heterogeneity (p = 0.406). However, all model were considered as having a high risk of bias, mainly owing to the unsuitable study design and poor reporting within the analysis domain.

Conclusions: Despite the included studies demonstrating moderate to excellent discrimination for predicting postoperative transfusion after total knee arthroplasty, all studies were considered as having a high risk of bias following the PROBAST due to some methodological shortcomings and inadequate external validation. Future research should focus on improving methodological quality and performing multicenter external validation to ensure clinical applicability.

Clinical trial number: Not applicable.