METHODS: In this study, we retrospectively analyzed the clinical data of elderly PHF patients undergoing ORIF from January 2020 to December 2021. We have established a multivariate regression model and nomograph. The prediction performance and consistency of the model were evaluated by the consistency coefficient and calibration curve, respectively.
RESULTS: 162 patients met our inclusion criteria and were included in the final study. The following factors are related to the increased risk of transfusion after ORIF: time to surgery, fibrinogen levels, intraoperative blood loss, and surgical duration.
CONCLUSIONS: Our patient-specific transfusion risk calculator uses a robust multivariable model to predict transfusion risk.The resulting nomogram can be used as a screening tool to identify patients with high transfusion risk and provide necessary interventions for these patients (such as preoperative red blood cell mobilization, intraoperative autologous blood transfusion, etc.).
方法:在本研究中,我们回顾性分析了2020年1月至2021年12月接受ORIF的老年PHF患者的临床资料.我们建立了多元回归模型和列线图。通过一致性系数和校正曲线评价模型的预测性能和一致性,分别。
结果:162例患者符合我们的纳入标准,被纳入最终研究。以下因素与ORIF后输血风险增加有关:手术时间,纤维蛋白原水平,术中失血,和手术持续时间。
结论:我们的患者特异性输血风险计算器使用稳健的多变量模型来预测输血风险。由此产生的列线图可作为筛查工具,用于识别输血风险高的患者,并为这些患者提供必要的干预措施(如术前红细胞动员,术中自体输血,等。).