Prediction of transfusion risk after total knee arthroplasty: use of a machine learning algorithm - 26/10/24

Doi : 10.1016/j.otsr.2024.103985

Nicolas Faure ^a, Siam Knecht ^b, Pierre Tran ^a, Lyna Tamine ^a, Jean-Christophe Orban ^c, Nicolas Bronsard ^a, Jean-François Gonzalez ^a, Grégoire Micicoi ^a,⁎
^a University Institute of Locomotor and Sport (IULS), Pasteur 2 Hospital, Nice, 30 Voie Romaine, 06000 Nice, France
^b Aix-Marseille Univ, CNRS, EFS, ADES, 13007 Marseille, France
^c Département d’Anesthésie Réanimation et Médecine Péri-Opératoire, Hôpital Privé Cannes Oxford, 06400 Cannes, France

^⁎Corresponding author.

Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Saturday 26 October 2024

Abstract

Introduction

Total knee arthroplasty (TKA) carries a significant hemorrhagic risk, with a non-negligible rate of postoperative transfusions. The blood-sparing strategy has evolved to reduce blood loss after TKA by identifying the patient's risk factors preoperatively. In practice, a blood count is often performed postoperatively but rarely altering the patient's subsequent management. This study aimed to identify the preoperative variables associated with hemorrhagic risk, enabling the creation of a machine-learning model predictive of transfusion risk after total knee arthroplasty and the need for a complete blood count.

Hypothesis

Based on preoperative data, a powerful machine learning predictive model can be constructed to estimate the risk of transfusion after total knee arthroplasty.

Material and methods

This retrospective single-centre study included 774 total knee arthroplasties (TKA) operated between January 2020 and March 2023. Twenty-five preoperative variables were integrated into the machine learning model and filtered by a recursive feature elimination algorithm. The most predictive variables were selected and used to construct a gradient-boosting machine algorithm to define the overall postoperative transfusion risk model. Two groups were formed of patients transfused and not transfused after TKA. Odds ratios were determined, and the area under the curve evaluated the model's performance.

Results

Of the 774 TKA surgery patients, 100 were transfused postoperatively (12.9%). The machine learning predictive model included five variables: age, body mass index, tranexamic acid administration, preoperative hemoglobin level, and platelet count. The overall performance was good with an area under the curve of 0.97 [95% CI 0.921–1], sensitivity of 94.4% [95% CI 91.2–97.6], and specificity of 85.4% [95% CI 80.6–90.2]. The tool developed to assess the risk of blood transfusion after TKA is available at arthrorisk.com.

Conclusion

The risk of postoperative transfusion after total knee arthroplasty can be predicted by a model that identifies patients at low, moderate, or high risk based on five preoperative variables. This machine learning tool is available on a web platform that is accessible to all, easy to use, and has a high prediction performance. The model aims to limit the need for routine check-ups, depending on the risk presented by the patient.