Development and Validation of a Nomogram for Prediction of the Risk of Positive Hidden Blood Loss in the Perioperative Period of Thoracolumbar Burst Fracture

Abstract

Background: This study aimed to develop and validate an individualized nomogram to predict the risk of positive hidden blood loss (HBL) in patients with thoracolumbar burst fracture (TBF) during the perioperative period.Methods: We conducted a retrospective investigation including 161 consecutive patients with TBL, and the corresponding patient data was extracted from March 2013 to March 2019. The independent risk factors for positive HBL were screened using univariate and multivariate logistic regression analyses. According to published literature and clinical experience, a series of variables were selected to develop a nomogram prediction model for positive HBL. The area under the receiver operating characteristic curves (AUC), C-index, calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the prediction model. Bootstrapping validation was performed to evaluate the performance of the model.Results: Among the 161 consecutive patients, 62 patients were negative for HBL (14.13%). The Multivariate logistic regression analysis showed that the six risk factors of age, length of surgical incision, duration of operation, percentage of vertebral height restoration (P1%), preoperative total cholesterol, and preoperative fibrinogen were independent risk factors of positive HBL. The C-index was 0.862 (95% CI 0.788–0.903) and 0.8884 in bootstrapping validation, respectively. The calibration curve showed that the predicted probability of the model was consistent with the actual probability. Decision curve analysis (DCA) showed that the nomogram had clinical utility.Conclusion: Overall, we explored the relationship between the positive HBL requirement and predictors: age, duration from admission to surgery, duration of operation, percentages of vertebral height restoration (P1%), preoperative total cholesterol, and preoperative fibrinogen. The individualized prediction model for patients with TBF can accurately assess the risk of positive HBL and facilitate clinical decision making. However, external validation will be needed in the future.