Objective: To investigate the factors influencing adverse reactions to blood transfusion in patients with acute aplastic anemia and construct a prediction model to provide the best assessment tool for clinical practice and to formulate timely and accurate prevention and treatment measures. Methods: A total of 388 patients with acute aplastic anemia in Wuzhou Red Cross Hospital, Wuzhou Worker's Hospital and Guidong People's Hospital of Guangxi Zhuang Autonomous Region from June 2021 to June 2022 were selected as study subjects, randomly divided into modeling population(n=271) and validation population(n=117) according to the ratio of 7:3. Transfusion adverse reactions, demographic characteristics, coagulation indexes, thromboelastography were collected.Logistic regression equation was used to analyze the factors influencing transfusion adverse reactions. We constructed a prediction model for the risk of transfusion adverse reactions by column line graphs,receiver operating characteristic(ROC) curve was used to analyze model discrimination, calibration curves was used to analyze model accuracy, and decision curve analysis(DCA) was used to evaluate model validity. Results: (1) The incidence of adverse transfusion reactions in patients with acute aplastic anemia was 9.02%(35/388); (2) Logistic regression equation showed that transfusion history, allergy history, platelet storage time,fibrinogen(FIB), activated partial thromboplastin time(APTT),(prothrombin time)PT, and maxium amplitude(MA)were factors affecting adverse transfusion reactions in patients with acute aplastic anemia(P<0.05).(3) Based on the Logistic regression equation, the area under the ROC curve(AUC) prediction model for adverse blood transfusion reactions in patients with acute aplastic anemia was established. The AUC values were 0.914 and 0.914 respectively in the modeling population and the verification population. There was a good correlation with the actual observation results, and the net benefit rate was good.(4) Based on the median score of the model(4 points), the patients in the modeling population and the validation population were classified into high-risk population(≥ 4 points) and low-risk population(<4 points), and the incidence of adverse blood transfusion reactions was higher in the high-risk population(17.39%) than in the low-risk population(4.97%, P<0.05). Conclusion: The neagram model based on blood transfusion history, allergy history, platelet storage time, FIB, APTT, PT and MA can effectively predict the risk of transfusion adverse reactions in patients with acute aplastic anemia, and help medical staff realize risk warning advance and take individualized and precise intervention.
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