Risk prediction of kalaemia disturbance and acute kidney injury after total knee arthroplasty: use of a machine
Pierre Tran1, Siam Knecht2, Lyna Tamine1
1Institut Universitaire Locomoteur et du Sport (IULS), Hôpital Pasteur 2, CHU de Nice, 30 voie Romaine, 06000 Nice, France.
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.
