Background
Total hip arthroplasty (THA) has become the first-choice treatment for elderly patients with end-stage hip disease. The high amount of hidden blood loss (HBL) in overweight and obese patients after THA not only affects rapid recovery, but also results in a greater economic burden. We aimed to identify risk factors that contribute to elevated HBL in overweight and obese patients after THA by retrospective analysis, and establish a nomogram prediction model for massive HBL in overweight and obese patients after THA.
Methods
A total of 505 overweight and obese patients treated with THA were included and randomly divided into modeling and validation sets according to a 7:3 ratio. The demographic and relevant clinical data of the patients were collected. The independent risk factors affecting HBL after THA in overweight and obese patients were obtained by Pearson, independent sample
T
-test, and multiple linear regression analyses. R software was used to establish a nomogram prediction model for postoperative HBL, as well as a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
Results
HBL was 911±438 mL, accounting for 79.5±13.1% of the total perioperative blood loss (1104±468 mL). A multiple linear regression analysis showed that HBL was associated with necrosis of the femoral head, absence of hypertension, longer operative time, higher preoperative erythrocytes, and higher preoperative D-dimer levels. The areas under the ROC curve (AUC) for the modeling and validation sets were 0.751 and 0.736, respectively, while the slope of the calibration curve was close to 1. The DCA curve demonstrated a better net benefit at a risk of HBL ≥1000 ml in both the training and validation groups.
Conclusion
HBL was an important component of total blood loss (TBL) after THA in overweight and obese patients. Necrosis of the femoral head, absence of hypertension, longer operative time, higher preoperative erythrocytes, and higher preoperative D-dimer levels were independent risk factors for postoperative HBL in these patients. The predictive model constructed based these data had better discriminatory power and accuracy, and could result in better net benefit for patients.