Development of a Nomogram for Predicting Blood Transfusion Risk After Hemiarthroplasty for Femoral Neck Fractures in Elderly Patients

Abstract

The aim of this study was to determine the risk factors and develop a nomogram for blood transfusions after hemiarthroplasty (HA) in patients with femoral neck fractures (FNFs). Material/Methods:We performed a retrospective study including consecutive elderly FNF patients treated by HA between January 2015 and December 2017. Perioperative information was obtained retrospectively, uni-and multivariate regression analyses were conducted to determine risk factors for blood transfusion, and a nomogram model was constructed to predict the risk of blood transfusion. The predictive performance and consistency of the model were evaluated by the consistency coefficient (C-index) and the calibration curve, respectively. Results:Of 178 patients, 151 were finally enrolled in the study and 21 received blood transfusion. Binary logistic regression analysis showed the low preoperative hemoglobin (Hb), longer time to surgery, general anesthesia, longer surgery duration, and higher intraoperative blood loss (IBL) were risk factors for blood transfusion. The accuracy of the contour map for predicting transfusion risk was 0.940. Conclusions:We found a correlation between blood transfusion requirement and low preoperative Hb, longer time to surgery, general anesthesia, longer surgery duration, and higher IBL, and we then developed a nomogram. Our nomogram model can be used to evaluate the transfusion risk for FNF patients after HA, and provides better guidance for clinicians to intervene perioperatively, so as to reduce the incidence of blood transfusion.

Allogeneic Red Blood Cell Transfusion Rate and Risk Factors in Elderly Patients With Femoral Neck Fracture After Hemiarthroplasty

“…In this study, the preoperative Hb of the blood transfusion group was (113.9 ± 14.0) g/L, which was signi cantly lower than the preoperative Hb (128.2 ± 12.8) g/L of the non-transfusion group (p < 0.05). This result is consistent with many previous studies [7,9,13] . Adunsky A et al [14] found that patients with preoperative Hb lower than 120 g/L have a 5-fold increase in the risk of postoperative blood transfusion.…”

“…The IBL of the transfusion group was (231.8 ± 97.0) ml, which was signi cantly higher than that of the non-transfusion group (190.5 ± 89.9) ml (p < 0.05). It is consistent with the results of Wang JQ et al [9] . Under normal circumstances, the amount of intraoperative blood loss increases the risk of blood transfusion.…”

“…Then what are the reasons? In the study of Wang JQ et al [9] , the average age of the blood transfusion group was (78.95 ± 5.26) years old, the non-transfusion group was (80.82 ± 5.23) years old, while in this study, the blood transfusion group was (83.4 ± 7.0) years old, and the non-transfusion group was (80.8 ± 6.5). It can be seen that the patients in this study are older, and advanced age itself is one of the risk factors for postoperative ABT.…”

Allogeneic Red Blood Cell Transfusion Rate and Risk Factors in Elderly Patients with Femoral Neck Fracture after Hemiarthroplasty

“…In this study, the preoperative Hb of the blood transfusion group was (113.18 ± 14.69) g/L, which was signi cantly lower than the preoperative Hb (128.80 ± 12.41) g/L of the nontransfusion group (p < 0.05). This result is consistent with many previous studies [7,9,13] . Adunsky A et al [14] found that patients with preoperative Hb lower than 120 g/L have a 5-fold increase in the risk of postoperative blood transfusion.…”

Machine Learning for the Prediction of Red Blood Cell Transfusion in Patients During or After Liver Transplantation Surgery

“…Meanwhile, data on the transfusion of RBCs before surgery and the clinical significance of intraoperative and postoperative risk factors such as operation time, intraoperative blood loss, and postoperative laboratory indicators are limited. Studies have been conducted to predict blood transfusion in joint surgery, craniofacial surgery, and obstetric surgery by developing clinical prediction models combined with patients' preoperative risk factors ( 9 – 11 ).…”