Abstract
OBJECTIVES: To identify independent risk factors for perioperative hidden blood loss (HBL) in intertrochanteric femoral fractures (ITFs) and to develop a predictive model. METHODS: We enrolled 231 patients with ITFs who underwent proximal femoral nail antirotation (PFNA) surgery at the Orthopedics Department of Northern Jiangsu People's Hospital, Jiangsu Province, China, from January 2021 to December 2023. Hidden blood loss was calculated using the OSTEO formula, and independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. A nomogram prediction model was subsequently constructed based on multivariate logistic regression. RESULTS: The LASSO regression identified eight key predictive factors: sex, body mass index (BMI), Admission serum calcium (mmol/L), American Society of Anesthesiologists (ASA) physical status classification, fracture type (Evans), hypertension, preoperative blood transfusion, and preoperative hemoglobin (HGB, g/L). The nomogram model demonstrated excellent predictive performance in both the training and validation sets, with area under the curve (AUC) values of 0.947 and 0.902, respectively. Calibration curves and decision curve analyses further confirmed the strong agreement between model predictions and actual observations, as well as the net clinical benefit. CONCLUSIONS: The nomogram model facilitates an intuitive and quantitative assessment of the risk of perioperative hidden blood loss in patients with ITFs, providing robust support for clinical decision-making.