[Objective] To investigate the implementation of emergency transfusion strategy of uncrossmatched type O suspended RBCs based on the single-center clinical practice, which is "emergency transfusion is initiated by the authorized doctor of the emergency department, and no more than 4 U of type O uncrossmatched suspended RBCs are issued within 15 minutes in the transfusion department"(referred as the Practice), so as to provide reference for blood management. [Methods] A retrospective analysis of the information of patients who received uncrossmatched type O suspended RBCs in West China Hospital of Sichuan University from August 2019 to April 2024 was conducted. The analysis included reasons for emergency blood transfusion, time of receiving transfusion application and blood distribution, total bilirubin, indirect bilirubin, lactate dehydrogenase before and after transfusion, blood group of patients, and disease outcome. [Results] From August 2019 to April 2024, 39 cases applied for emergency transfusion of type O suspended RBCs, and a total of 90 U uncrossmatched suspended RBCs were transfused. All patients were Rh(D) positive, including 14 cases of blood group A, 6 cases of blood group B, 16 cases of blood group O, 2 cases of blood group AB, one case of undetermined blood group, and 2 cases with positive antibody screening. The main cause of emergency transfusion of type O suspended RBCs was traffic accident, accounting for 46% (18/39), with a mortality rate at 51.28% (20/39). The cause of death was primary injury, and no adverse reactions were reported. There was no significant difference in total bilirubin (TBIL), indirect bilirubin (IBIL) and lactate dehydrogenase (LDH) before and after blood transfusion (P>0.05). The median duration from admission to receiving transfusion application was 30.20 minutes, and 5.30 minutes from receipt of the application to blood distribution. [Conclusion] The single-center based Practice is safe, but there is room for optimization before the link of blood transfusion application sent to the transfusion department when applying for emergency transfusion of type O suspended RBCs.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

【Objective】 To investigate the factors influencing the production of platelet antibody and its effect on clinical platelet transfusion. 【Methods】 This is a single-center prospective observational study. The research subjects were patients with hematological diseases in West China Hospital of Sichuan University from October 1, 2018 to September 30, 2019, and their plasma were collected before platelet transfusion to detect platelet antibodies using solid-phase agglutination method. According to the results of platelet antibody screening, the patients were divided into platelet antibody positive group and negative group. The t test and nonparametric Mann-Whitney U test were used to compare the transfusion efficacy of two groups. Patients’ demographic and clinical information, including age, gender, diagnosis, the units of platelets and RBC transfused, were collected via HIS6.2.0 and whole process management system of blood in clinical (version 3.0) to analyze the influence of age, gender and the disease on the positive rate of platelet antibodies, as well as the profile of platelet antibodies in patients with different diseases, the correlation between the positive rate of platelet antibodies and the history of blood/platelets transfusion. In additional, the platelet transfusion process was observed on site. 【Results】 A total of 316 patients with hematologic diseases were included in this study, mainly with acute myeloid leukemia(188/316, 59.5%). All patients were transfused 1671 U platelet [1~17(5.3±3.1)U each person] and 1896 U RBC products [0~38(7.8±4.6)U each person] during the treatment. Out of the 316 patients, platelet antibodies were found in 85 (26.9%) of them. No significant differences in the positive rates of platelet antibody after transfusion were notice by genders or ages(P>0.05). The incidence of platelet antibody was related to diseases (P<0.05), with MDS as the highest (57.1%), followed by aplastic anemia (36.4%) and myeloid leukemia (27.7%). In additional, the positive rate of platelet antibody increased with the number of previous platelet transfusions(P<0.05). The 316 patients were divided into positive group and negative group according to the results of platelet antibody screening. The corrected count of increment (5.2×10⁹/L vs 11.5×10⁹/L, P<0.01) and absolute platelet increase(8×10⁹/L vs 17×10⁹/L, P<0.01)in positive group were lower than those in negative group. The positive group were transfused more units of platelets(1.7 U vs 1.2 U, P<0.01)and red blood cells(1.5 U vs 1.1 U, P<0.05)per week than negative group. The platelet transfusion interval was shorter in positive group than negative group (3.1 days vs 3.6 days, P<0.05), but there was no significant difference in red blood cell transfusion interval (3.1 days vs 3.8 days, P>0.05) between two groups. The minimum PLT count(5×10⁹/L vs 9×10⁹/L, P<0.01), average PLT count(27×10⁹/L vs 40×10⁹/L, P<0.01)and average Hb(71 g/L vs 77 g/L, P<0.05)in positive group were lower than those in negative group during hospitalization, but there was no significant difference in the minimum Hb(56 g/L vs 59 g/L, P>0.05)between two groups. According to transfusion events on site, the incidence of acute adverse reactions to transfusion was 13% (169/1 291). 【Conclusions】 The positive rates of platelet antibodies in patients with hematologic diseases were relatively high. In addition, the efficacy of platelet transfusion in positive group were worse than that in the negative group. It is recommended that platelet antibody testing should be routinely performed before transfusion in hematologic disease patients to select crossmatch-compatible platelets in order to improve the effectiveness of platelet transfusion.

【Objective】 To develop a prediction model of allogenic blood transfusion in elective patients based on machine learning, so as to guide clinicians to prepare blood for perioperative patients more reasonably. 【Methods】 Relevant data of all surgical patients from 2012 to 2018 were extracted from the big data integration platform of our hospital, to construct the surgical blood database based on Python V3.8.0. All data were analyzed using Excel and SAS, and the prediction model was developed based on SPSS Modeler 18.0. 【Results】 1) There was a negative correlation between preoperative Hb and BMI and intraoperative blood transfusion rate, with Pearson correlation coefficient (R) as -0.168 and -0.046, respectively. The transfusion rate of patients under 1 year old was the highest, up to 15.63%. The transfusion rate of female patients was higher than that of male patients (P>0.05), as cardiac surgery rated at the highest 11.38%, but their per capita blood transfusion was lower than that of males (P<0.01). 2) The AUC range corresponding to the prediction model for transfusion probability was 0.67~0.88, and when the AUC reached the highest, the hit ratio, coverage rate and specificity of Model 9 was 10.7%, 85.76% and 75.4%, respectively. 3) The main factors contributing to the prediction model for transfusion volume in surgery were weight, Hb, total protein(TP), etc. 【Conclusion】 The prediction efficiency of the successfully constructed prediction model for perioperative blood use was better than that of MSBOS.

【Objective】 To retrospectively analyze the situation of surgical blood ordering in our hospital and explore the value of optimizing preoperative blood ordering. 【Methods】 Surgical blood ordering and utilization data of West China Hospital of Sichuan University from 2012 to 2018 were gathered to evaluate the rationality of preoperative blood ordering by calculating the indicators including transfusion rate, transfusion probability, transfusion index etc. and recommend preoperative blood ordering guided by transfusion index ≥ 0.3, the transfusion rate ≥ 5%, and the transfusion index ≥ 0.5 respectively to calculate the cost saved. 【Results】 1) The preoperative blood ordering of Department of Cardiac Surgery and Burn Plastic Surgery were relatively rational, while other Surgery Departments was excessive, especially the Thoracic Surgery; 2) Among the top fifteen surgeries ranked by blood ordering rate, the blood ordering was rational for mitral valve replacement, ventricular septum (repair/occlusion), and aortic valve replacement, while excessive for other 12 surgeries, especially for lung resection surgery; 3) The surgical blood ordering guided by the three indicators can reduce 19% ~80% theoretically, saving 0.39~1.28 million yuan per year. 【Conclusion】 Preoperative blood ordering of the Department of Cardiac Surgery and Burn Plastic Surgery in our hospital is relatively rational. While excessive blood ordering exists in other surgical departments, especially for thoracic surgery. The establishment of Maximum Surgical Blood Order Schedule can reduce unnecessary blood ordering and improve blood utilization, and save manpower and material resources, and reduce the costs of patients.