Abstract
BACKGROUND: The adverse events and complications caused by blood loss after spinal surgery have attracted increasing amounts of attention. This retrospective study aimed to identify risk factors for postoperative bleeding and develop predictive models. METHODS: This was a retrospective analysis of patients who underwent spinal fusion surgery between November 2018 and December 2019. Preoperative data were collected, and LASSO regression and multifactor regression analyses were performed to identify risk factors for increased postoperative blood loss. Using the obtained risk factors, a nomogram model was established. The nomogram was evaluated based on the concordance index (C-index), receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve analysis. RESULTS: A total of 434 patients who underwent spinal surgery were included in the study. LASSO regression analysis and multivariate logistic regression analysis revealed that age, cardiovascular disease status, fusion level, use of neuromuscular blockers, peak pressure of mechanical ventilation, and intraoperative blood loss were related to the amount of postoperative drainage. Based on these six risk factors, we developed a nomogram with a C-index of 0.807. The areas under the curve (AUCs) in the training group and the verification group were 0.8155 and 0.7529, respectively, indicating that the established model had good predictive performance. There was good agreement in the calibration curve, and the clinical decision curve showed a significant net benefit after the intervention. CONCLUSIONS: We developed a nomogram model through retrospective analysis, enabling clinicians to predict the likelihood of postoperative blood loss based on risk factors. The risk factors for increased drainage volume after spinal fusion surgery include age, cardiovascular disease status, fusion level, use of neuromuscular blockers, peak pressure of mechanical ventilation, and intraoperative blood loss.