Department of Medical Ultrasonics, The First Affiliated Hospital of Wenzhou Medical University, Shangcai Village Ouhai District, Wenzhou, 325000, Zhejiang, China.
Department of Orthopaedics, The Second Affiliated Hospital and Yuying, Children's Hospital of Wenzhou Medical University, 109# Xue Yuan Xi Road, Wenzhou, 325000, Zhejiang, China.
BMC Musculoskelet Disord. 2024 Jul 11;25(1):530. doi: 10.1186/s12891-024-07661-1.
Few studies have focused on the risk factors leading to postoperative blood transfusion after open reduction and internal fixation (ORIF) of proximal humeral fractures (PHFs) in the elderly. Therefore, we designed this study to explore potential risk factors of blood transfusion after ORIF for PHFs. We have also established a nomogram model to integrate and quantify our research results and give feedback.
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.
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.
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.).
鲜有研究关注导致老年肱骨近端骨折（PHF）切开复位内固定（ORIF）术后输血的危险因素。因此，我们设计了这项研究来探讨 PHF 行 ORIF 术后输血的潜在危险因素。我们还建立了列线图模型来整合和量化我们的研究结果并提供反馈。
本研究回顾性分析了 2020 年 1 月至 2021 年 12 月接受 ORIF 的老年 PHF 患者的临床资料。我们建立了多变量回归模型和列线图。通过一致性系数和校准曲线分别评估模型的预测性能和一致性。
162 例患者符合纳入标准并纳入最终研究。与 ORIF 后输血风险增加相关的因素有：手术时间、纤维蛋白原水平、术中失血量和手术持续时间。
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选工具，以识别具有高输血风险的患者，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）。
