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Background: Perioperative acute kidney injury (AKI) is challenging to predict and a common complication of lower limb arthroplasties. Our aim was to create a machine learning model to predict AKI defined by both serum creatinine (sCr) levels and urine output (UOP) and to investigate which features are important for building the model. The features were divided into preoperative, intraoperative, and postoperative feature sets. Methods: This retrospective, register-based study assessed 648 patients who underwent primary knee or hip replacement at Oulu University Hospital, Finland, between January 2016 and February 2017. The RUSBoost algorithm was chosen to establish the models, and it was compared to Naïve/Kernel Bayes and support vector machine (SVM). Models of AKI classified by either sCr levels or UOP were established. All the models were trained and validated using a five-fold cross-validation approach. An external test set was not available at the time of this study. Results: The performance of both the sCr level- and UOP-based AKI models improved when pre-, intra-, and postoperative features were used together. The best sCr level-based AKI model performed as follows: area under receiving operating characteristic (AUROC) of 0.91, (95% CI ± 0.02), area under precision-recall (AUPR) of 0.35 (95% CI ± 0.04) sensitivity of 0.88 (95% CI ± 0.03), specificity of 0.87 (95% CI ± 0.03), and precision o (95% CI ± 0.03). This model correctly classified 22 out of 25 patients with AKI. The best UOP-based AKI model performed as follows: AUROC of 0.98 (95% CI ± 0.02), AUPR of 0.48 (95% CI ± 0.04), sensitivity of 0.88 (95% CI ± 0.02), specificity of 0.93 (95% CI ± 0.03), and precision of 0.34 (95% CI ± 0.04). This model correctly classified 23 out of 26 patients with AKI. In the sCr-AKI models, estimated glomerular filtration rate (eGFR)-related features were most important, and in the UOP-based AKI models, UOP-related features were most important. Other important and re

Background: This study aimed to evaluate the occurrence and perioperative risk factors of acute kidney injury (AKI) in primary elective hip and knee and emergency hip arthroplasty patients. We also aimed to assess the effect of urine output (UOP) as a diagnostic criterion in addition to serum creatinine (sCr) levels. We hypothesized that emergency arthroplasties are prone to AKI and that UOP is an underrated marker of AKI. Methods: This retrospective, register-based study assessed 731 patients who underwent primary elective knee or hip arthroplasty and 170 patients who underwent emergency hip arthroplasty at Oulu University Hospital, Finland, between January 2016 and February 2017. Results: Of the elective patients, 18 (2.5%) developed AKI. The 1-year mortality rate was 1.5% in elective patients without AKI and 11.1% in those with AKI (P = .038). Of the emergency patients, 24 (14.1%) developed AKI. The mortality rate was 16.4% and 37.5% in emergency patients without and with AKI, respectively (P = .024). In an AKI subgroup analysis of the combined elective and emergency patients, the mortality rate was 31.3% (n = 5) in the sCr group (n = 16), 23.5% (n = 4) in the UOP group (n = 17), and 22.2% (n = 2) in AKI patients who met both the sCr and UOP criteria (n = 9). Conclusion: Emergency hip arthroplasty is associated with an increased risk of AKI. Since AKI increases mortality in both elective and emergency arthroplasty, perioperative oliguria should also be considered as a diagnostic criterion for AKI. Focusing solely on sCr may overlook many cases of AKI.