Wei Donglei, Jiang Yage, Long Xingcan, Huang Nanchang, Xiang Jianhui, Cheng Jianwen, Zhao Jinmin
The First Affiliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning, Guangxi, China.
Guangxi Key Laboratory of Regenerative Medicine, Orthopaedic Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, People's Republic of China.
Biomed Eng Online. 2025 Jul 1;24(1):80. doi: 10.1186/s12938-025-01419-z.
Proximal femoral antirotation nailing (PFNA) for treating elderly patients with intertrochanteric fractures (EIFs) is often associated with substantial hidden blood loss. Perioperative blood transfusion to restore the lost blood has no effect on postoperative mortality and it increases the risk of postoperative infection. The goal of this study was to develop and validate a nomogram for predicting the risk of perioperative transfusion and intervening ahead of time to reduce the risk in EIF patients receiving PFNA.
This study retrospectively examined and collected risk factors associated with transfusion in EIF patients treated with PFNA. Random forest with least absolute shrinkage and selection operator (LASSO) regression analysis was used to select characteristic variables and construct nomograms with the screening variables. The predictive model's discriminatory efficacy and calibration efficacy were assessed by receiver operating characteristic (ROC) curves, C-index, and calibration curves, respectively. Clinical usefulness was assessed by decision curve analysis (DCA).
The final nomogram consisted of five predictor variables: lower preoperative haemoglobin (HGB), age, preoperative urea, preoperative albumin, and surgical position. The nomogram showed good discriminatory and calibration efficacy with an area under the curve (AUC) value of 0.865 and a calibration curve highly approximating the ideal curve. In internal validation, the C-index of the model was calculated to be 0.823, indicating that the model exhibited superior predictive power.
The nomogram constructed from preoperative HGB, age, urea, albumin, and surgical position can be used to predict more accurately the risk of perioperative transfusion in EIF patients treated with PFNA. Validation of the accuracy of this predictive model requires multicenter, prospective, and larger populations.
股骨近端抗旋髓内钉（PFNA）治疗老年股骨转子间骨折（EIF）患者时常常伴有大量隐性失血。围手术期输血以补充失血对术后死亡率并无影响，反而会增加术后感染风险。本研究的目的是开发并验证一种列线图，用于预测围手术期输血风险，并提前进行干预以降低接受PFNA治疗的EIF患者的输血风险。
本研究回顾性检查并收集了接受PFNA治疗的EIF患者与输血相关的危险因素。采用带有最小绝对收缩和选择算子（LASSO）回归分析的随机森林来选择特征变量，并使用筛选变量构建列线图。分别通过受试者操作特征（ROC）曲线、C指数和校准曲线评估预测模型的辨别效能和校准效能。通过决策曲线分析（DCA）评估临床实用性。
最终的列线图由五个预测变量组成：术前血红蛋白（HGB）水平较低、年龄、术前尿素、术前白蛋白和手术体位。该列线图显示出良好的辨别和校准效能，曲线下面积（AUC）值为0.865，校准曲线高度接近理想曲线。在内部验证中，模型的C指数计算为0.823，表明该模型具有卓越的预测能力。
由术前HGB、年龄、尿素、白蛋白和手术体位构建的列线图可更准确地预测接受PFNA治疗的EIF患者围手术期输血风险。该预测模型准确性的验证需要多中心、前瞻性且更大规模的人群研究。
