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Purpose A blood transfusion after total knee arthroplasty (TKA) is associated with an increase in complication and infection rates. However, no studies have been conducted to predict transfusion after TKA using a machine learning algorithm. The purpose of this study was to identify informative preoperative variables to create a machine learning model, and to provide a web‐based transfusion risk‐assessment system for clinical use. Methods This study retrospectively reviewed 1686 patients who underwent TKA at our institution. Data for 43 preoperative variables, including medication history, laboratory values, and demographic characteristics, were collected. Variable selection was conducted using the recursive feature elimination algorithm. The transfusion group was defined as patients with haemoglobin (Hb) < 7 g/dL after TKA. A predictive model was developed using the gradient boosting machine, and the performance of the model was assessed by the area under the receiver operating characteristic curve (AUC). Data sets from an independent institution were tested with the model for external validation. Results Of the 1686 patients who underwent TKA, 108 (6.4%) were categorized into the transfusion group. Six preoperative variables were selected, including preoperative Hb, platelet count, type of surgery, tranexamic acid, age, and body weight. The predictive model demonstrated good predictive performance using the six variables [AUC 0.842; 95% confidence interval (CI) 0.820–0.856]. Performance was also good according to the external validation using 400 data from an independent institution (AUC 0.880; 95% CI 0.844–0.910). This web‐based blood transfusion risk‐assessment system can be accessed at http://safetka.net. Conclusions A web‐based predictive model for transfusion after TKA using a machine learning algorithm was developed using six preoperative variables. The model is simple, has been validated, showed good performance, and can be used before TKA to predict the risk of transfusion and guide appropriate precautions for high‐risk patients. Level of evidence Diagnostic level II.

The ABI showed significantly decreased sensitivity especially in stenosis below the trifurcation level. Both PPG and CWD were complementary to ABI in these groups of patients.

PurposeAcute kidney injury (AKI) is a deleterious complication after total knee arthroplasty (TKA). The purposes of this study were to identify preoperative risk factors and develop a web‐based prediction model for postoperative AKI, and assess how AKI affected the progression to ESRD. MethodThe study included 5757 patients treated in three tertiary teaching hospitals. The model was developed using data on 5302 patients from two hospitals and externally validated in 455 patients from the third hospital. Eighteen preoperative variables were collected and feature selection was performed. A gradient boosting machine (GBM) was used to predict AKI. A tenfold‐stratified area under the curve (AUC) served as the metric for internal validation. Calibration was performed via isotonic regression and evaluated using a calibration plot. End‐stage renal disease (ESRD) was followed up for an average of 41.7 months. ResultsAKI develops in up to 10% of patients undergoing TKA, increasing the risk of progression to ESRD. The ESRD odds ratio of AKI patients (compared to non‐AKI patients) was 9.8 (95% confidence interval 4.3–22.4). Six key predictors of postoperative AKI were selected: higher preoperative levels of creatinine in serum, the use of general anesthesia, male sex, a higher ASA class (> 3), use of a renin–angiotensin–aldosterone system inhibitor, and no use of tranexamic acid (all p < 0.001). The predictive performance of our model was good (area under the curve 0.78 [95% CI 0.74–0.81] in the developmental cohort and improved in the external validation cohort (0.89). Our model can be accessed at https://safetka.net. ConclusionsA web‐based predictive model for AKI after TKA was developed using a machine‐learning algorithm featuring six preoperative variables. The model is simple and has been validated to improve both short‐ and long‐term prognoses of TKA patients. Postoperative AKI may lead to ESRD, which surgeons should strive to avoid. Level of evidenceDiagnostic level II.

PurposeWe aimed to evaluate the nationwide incidence and risk factors for symptomatic deep vein thrombosis (DVT) after major lower limb orthopedic surgeries.Materials and MethodsThe Korean Health Insurance Review and Assessment Service database was used to retrospectively identify International Classification of Disease-10 codes for DVT and operation codes representing hip arthroplasty, knee arthroplasty, and hip fracture surgeries. The age- and gender-adjusted annual incidence of DVT, rates of major lower limb orthopedic surgeries, and the postoperative incidence of DVT according to the surgical procedure were assessed.ResultsThe age- and gender-adjusted annual incidence of DVT was 70.67 per 100000 persons/year. Compared to patients aged <49 years, the relative risk of DVT was five times higher in patients aged 50-69 and 10 times higher in patients aged >70 years (p<0.001). Females showed a greater relative risk for DVT than males (1.08; p<0.001). The incidence of postoperative DVT, according to the type of surgery, was significantly greater for knee replacement arthroplasty than for other forms of surgery (p<0.002). The relative risk of postoperative DVT was higher in females in knee replacement arthroplasty (1.47) and hip fracture surgery (2.25) groups, although relatively lower in those who underwent hip replacement arthroplasty (0.97).ConclusionAmong major lower limb surgeries, advanced age, female gender, and undergoing a knee replacement arthroplasty were found to be risk factors for developing postoperative DVT. These findings further emphasize the need for orthopedic surgeons to consider the development of DVT after surgery in high-risk patients.