Postoperative serum albumin drop predicts complications in primary total knee arthroplasty: a retrospective cohort...

Perioperative complications that occur after TKA are still challenging to manage. While preoperative hypoalbuminemia has been proved to be a risk-factor indicator, the role of ΔAlb is yet to be determined.

In this retrospective study with a cohort analysis, an XGBoost machine learning model was trained with data collected from 758 TKA patients (2018-2022), to identify predictors that aid in the prediction of joint infections after TKA. The predictors to be entered into the model include 28 variables such as ΔAlb (preoperative values - postoperative values)/preoperative values \* 100%, peak C-reactive protein (CRP) level on postoperative day 2, and erythrocyte sedimentation rate (ESR) dynamics. Validation criteria include AUC-ROC, integrated calibration index (IC), and decision curve analysis (DCA).

The XGBoost model provided better predictive accuracy (AUC = 0.947, 95% CI, 0.923 - 0.968), performing better than the logistic regression model (AUC = 0.752) and other ensemble methods (random forest, AUC = 0.835; LightGBM, AUC = 0.905). For the variables, the model that had the highest clinical relevance was a percentage decrease of Alb > 15%, which independently raised the risk of developing complications fivefold (OR, 5.8, P < 0.001), with progressively increasing hazard ratios for a percentage decrease below the threshold. Calibration reliability was high (ICI, 0.101), and the model provided informative net benefit within the range of clinical decision-making (10% - 60%). An important interaction existed between the variables: patients with percentage decreases of Alb > 15% and CRP > 60 mg/L were at 3.2 times higher risk of venous thromboembolism. Complications occurred in 9.63% of patients, of which 52.1% were venous thromboembolisms.

ΔAlb is an excellent dynamic marker useful in the determination of post-TKA complications. Using ΔAlb values and monitoring the level of CRP provides clinicians with precision risk estimation superior to that presented by current models encompassing patient comorbidity. Based on the presented XGBoost model, clinicians are in possession of actionable risk thresholds useful in interventions such as replenishing intravenous albumin during situations wherein ΔAlb surpasses 15%, in an attempt to potentially preventing over 60% of avoidable complications in high-risk cohorts.