Background: Data on the relationship between the triglyceride glucose (TyG) index and coronary artery calcification (CAC) progression is limited. This longitudinal study evaluated the association of TyG index with CAC progression in asymptomatic adults. Methods:We enrolled 12,326 asymptomatic Korean adults who had at least two CAC evaluations. The TyG index was determined using ln (fasting triglycerides [mg/dL] × fasting glucose [mg/dL]/2). CAC progression was defined as a difference ≥ 2.5 between the square roots (√) of the baseline and follow-up coronary artery calcium score (CACS) (Δ√transformed CACS). Annualized Δ√transformed CACS was defined as Δ√transformed CACS divided by the interscan period.Results: During a mean 3.3 years, the overall incidence of CAC progression was 30.6%. The incidence of CAC progression (group I [lowest]: 22.7% versus [vs.] group II: 31.7% vs. group III [highest]: 37.5%, P < 0.001) and annualized Δ√transformed CACS (group I: 0.46 ± 1.44 vs. group II: 0.71 ± 2.02 vs. group III: 0.87 ± 1.75, P < 0.001) were markedly elevated with increasing TyG index tertiles. Multivariate linear regression analysis showed that TyG index was associated with annualized Δ√transformed CACS (β = 0.066, P = 0.036). In multivariate logistic regression analysis, the TyG index was significantly associated with CAC progression in baseline CACS ≤ 100. Conclusion:The TyG index is an independent predictor of CAC progression, especially in adults without heavy baseline CAC.

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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.

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