Objective: To explore the influencing factors of blood transfusion volume in obstetric patients based on the national health development strategy, and to provide evidence for clinical transfusion management and maternal and infant safety. Methods: A total of 1 203 obstetric patients who received blood transfusion in a tertiary hospital in Beijing from 2013 to 2023 were enrolled retrospectively. Seventeen indicators including age, body mass index (BMI), hemoglobin and prothrombin time were selected as research variables. Descriptive analysis was conducted firstly. Scatter plot matrix was used to analyze the correlation between continuous variables and transfusion volume, and box plots were used to analyze the relationship between categorical variables and transfusion volume. Linear regression, logistic regression and random forest models were established for empirical analysis, and ROC curves and AUC values were compared to evaluate model performance. Results: Descriptive analysis showed that the distribution of obstetric blood transfusion volume was right-skewed, and multiple indicators were significantly correlated with transfusion volume. Linear regression indicated that 9 variables such as age, BMI and hemoglobin had linear correlation with transfusion volume. Logistic regression confirmed that 7 variables including BMI and hemoglobin were statistically correlated with the probability of massive blood transfusion. Random forest model screened the core influencing factors, which were ranked by importance: hemoglobin, prothrombin time, hematocrit, BMI, activated partial thromboplastin time, platelet count and age. The AUC values of logistic regression and random forest were 0.78 and 0.81 respectively, and the latter had better predictive performance. Conclusion: Prenatal anemia, coagulation disorders, advanced age, obesity, gestational hypertension, placental abnormalities and organ damage can increase the risk of massive blood transfusion in obstetric patients. The random forest model has better analytical and predictive ability in this study. The results can provide reference for accurate blood transfusion and risk prediction in obstetrics.

Objective: To investigate the molecular mechanism of ABO blood type gene c. 899G>A leading to the occurrence of subtype A. Methods: Serological methods were used to identify ABO blood types. Genotyping was performed using polymerase chain reaction sequence specific (PCR-SSP) kits, and Sanger sequencing was used to analyze the ABO gene exons 1-7. PacBio third-generation sequencing was employed for ABO gene full-length haplotype analysis. AlphaFold3 was used to predict protein structure, and ChimeraX software was used to analyze the impact of amino acid changes caused by base mutations on protein structure. Results: The patient exhibited group O of forward typing and group A of reverse typing. Weak A. antigen was detected by absorption-elution test. Molecular biology detection revealed three mutation sites, c. 261delG, c. 297A>G, and c. 899G>A. Third-generation sequencing showed that one haplotype was recombinant ABO O.01.01 and the other haplotype was recombinant ABO B.01-O.01.01, with c. 297A>G and c. 899G>A located on this recombinant haplotype. Protein structure prediction indicated that c. 899G>A resulted in premature termination codons and translation interruption of ABO blood group genes, leading to a 55 amino acid deletion at the C-terminus. Conclusion: The c. 899G>A mutation in the ABO gene of the patient resulted in reduced A antigen expression and an A subtype phenotype.