Establishment of a predictive model for blood transfusion after femoral head replacement in elderly patients.

ABSTRACT

OBJECTIVES: The study aimed to establish a nomogram predictive model for blood transfusion after artificial femoral head replacement surgery in elderly patients with intertrochanteric fractures. PATIENTS AND METHODS: Two hundred five elderly patients (55 males, 150 females; mean age 82.1±6.6 years; range, 63 to 103 years) with intertrochanteric femoral fractures who underwent artificial femoral head replacement surgery between January 2015 and May 2023 were retrospectively analyzed. The patients were randomly divided into two groups the training group (n=143) and the validation group (n=62). Within the training group, patients were further categorized into the nontransfused (n=86) and transfused (n=57) groups. Perioperative data were collected for logistic regression analysis to identify risk factors for postoperative blood transfusion. A nomogram model was developed to predict the need for blood transfusion, with assessments including the C-index, receiver operating characteristic curve, decision curve analysis, and clinical impact curve. RESULTS: Logistic regression analysis showed that low preoperative hemoglobin levels, high intraoperative bleeding volume, high drainage volume, the use of wire reinforcement, and history of cerebral infarction were the independent risk factors for transfusion after femoral head replacement. Both decision curve analysis and clinical impact curves indicated that the prediction model could be used as a good prediction tool for blood transfusion after artificial femoral head replacement for intertrochanteric femoral fractures in the elderly. CONCLUSION: A nomogram prediction model that effectively assesses the risk of blood transfusion in elderly patients undergoing femoral head replacement for intertrochanteric femoral fractures was established in this study. This model demonstrated high predictive accuracy and consistency, providing a valuable tool for clinicians to identify high-risk patients and implement early interventions to reduce the need for postoperative blood transfusions.