Prediction of transfusion risk after total knee arthroplasty: use of a machine learning algorithm.

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 https://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. LEVEL OF EVIDENCE: II; diagnostic study.

Risk prediction of kalaemia disturbance and acute kidney injury after total knee arthroplasty: use of a machine learning algorithm.

INTRODUCTION: Total knee arthroplasty (TKA) is a procedure associated with risks of electrolyte and kidney function disorders, which are rare but can lead to serious complications if not correctly identified. A routine check-up is very often carried out to assess the seric ionogram and kidney function after TKA, that rarely requires clinical intervention in the event of a disturbance. The aim of this study was to identify perioperative variables that would lead to the creation of a machine learning model predicting the risk of kalaemia disorders and/or acute kidney injury after total knee arthroplasty. HYPOTHESIS: A predictive model could be constructed to estimate the risk of kalaemia disorders and/or acute kidney injury after total knee arthroplasty. MATERIAL AND METHODS: This single-centre retrospective study included 774 total knee arthroplasties (TKA) operated on between January 2020 and March 2023. Twenty-five preoperative variables were incorporated into the machine learning model and filtered by a first algorithm. The most predictive variables selected were used to construct a second algorithm to define the overall risk model for postoperative kalaemia and/or acute kidney injury (K+ A). Two groups were formed of K+ A and non-K+ A patients after TKA. A univariate analysis was performed and the performance of the machine learning model was assessed by the area under the curve representing the sensitivity of the model as a function of 1 - specificity. RESULTS: Of the 774 patients included who had undergone TKA surgery, 46 patients (5.9%) had a postoperative kalaemia disorder requiring correction and 13 patients (1.7%) had acute kidney injury, of whom 5 patients (0.6%) received vascular filling. Eight variables were included in the machine learning predictive model, including body mass index, age, presence of diabetes, operative time, lowest mean arterial pressure, Charlson score, smoking and preoperative glomerular filtration rate. Overall performance was good with an area under the curve of 0.979 [CI95% 0.938-1.02], sensitivity was 90.3% [CI95% 86.2-94.4] and specificity 89.7% [CI95% 85.5-93.8]. The tool developed to assess the risk of impaired kalaemia and/or acute kidney injury after TKA is available on https://arthrorisk.com. CONCLUSION: The risk of kalaemia disturbance and postoperative acute kidney injury after total knee arthroplasty could be predicted by a model that identifies low-risk and high-risk patients based on eight pre- and intraoperative variables. This machine learning tool is available on a web platform accessible for everyone, easy to use and has a high predictive performance. The aim of the model was to better identify and anticipate the complications of dyskalaemia and postoperative acute kidney injury in high-risk patients. Further prospective multicentre series are needed to assess the value of a systematic postoperative biochemical work-up in the absence of risk predicted by the model. LEVEL OF EVIDENCE: IV; retrospective study of case series.