Objective: To investigate the factors influencing intraoperative blood transfusion in patients with hip fractures and to develop a machine learning (ML) model for predicting this risk. Methods: A total of 424 patients with hip fractures who underwent surgical treatment between November 2022 and March 2025 in our hospital were selected. Key feature variables of intraoperative blood transfusion risk were identified using the Boruta algorithm. Four different ML algorithms—support vector machine (SVM), linear discriminant analysis (LDA), mixed discriminant analysis (MDA), and extreme gradient boosting (XGBoost)—were used to develop predictive models for intraoperative blood transfusion risk. The predictive performance of the four ML models were evaluated using accuracy, precision, receiver operating characteristic (ROC) curves, precision-recall curves (PRC), precision-recall gain curves (PRGC), and F1 scores. Shapley additive interpretation (SHAP) was used to interpret the final model. Results: Among the 424 patients, 77(18.2%) received intraoperative blood transfusion. The Boruta algorithm identified albumin (ALB), activated partial thromboplastin time (APTT), types of anesthesia, types of fracture, and hemoglobin (Hb) as key feature variables for predicting intraoperative blood transfusion risk. In model evaluation, the SVM model outperforms the other three models across multiple metrics, including the area under the receiver operating characteristic curve (AUC), recall, recall gain, accuracy, precision, F1 score, and the area under the precision-recall curve (PRC-AUC). The SVM model, interpreted and visualized based on SHAP values, effectively predicted intraoperative blood transfusion risk in patients with hip fracture. A visual online application was developed based on the SVM model (https://pbo-nomogram.shinyapps.io/blood/). Conclusion: Preoperative low ALB and Hb levels, prolonged APTT, general anesthesia, and intertrochanteric fractures are risk factors for intraoperative blood transfusion in hip fracture patients. The risk prediction model for intraoperative blood transfusion constructed based on the SVM algorithm has optimal performance, which provides new ideas and methods for the clinical early identification of hip fracture patients with high transfusion risk and the implementation of targeted interventions.

Objective To study a nucleic acid extraction method suitable for detecting methicillin‐resistant Staphylococcus aureus (MRSA) by PCR method .Methods Under different incubation conditions ,MRSA was cracked by lysozyme ,lysostaphin or chel‐ex100R resin for obtaining DNA ,then the target gene was detected by using the PCR method .Results DNA was obtained by sim‐ultaneously using lysozyme ,lysostaphin and chelex100R resin solution ,the obtained Ct value was significantly lower than that of the other components of schizolysis solutions when PCR was used to detect mecA gene of obtained DNA .There was no statistically sig‐nificant difference between adopting the 56 ℃ one‐step method and the 37 ℃ and 56 ℃ two‐step method for conducting MRSA schizolysis(P> 0 .05) ,but the steps were simplified .Conclusion Incubating MRSA in solution containing lysozyme ,lysostaphin , chelex100R resin for 30 min at 56 ℃ is the convenient and efficient schizolysis method to extract DNA ,which can be used immedi‐ately for the next step of PCR and lays the foundation for PCR rapid detection of clinical MRSA infection .