OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) undergoing total knee arthroplasty (TKA). METHODS: A retrospective cohort study was conducted including RA patients who underwent unilateral TKA between January 2019 and December 2024. Inpatient and 30-day postoperative outpatient follow-up data were collected. Six machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, extreme gradient boosting, and light gradient boosting machine, were used to construct predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F1-score, accuracy, precision, and recall. SHapley Additive exPlanations (SHAP) values were employed to interpret and rank the importance of individual variables. RESULTS: According to the inclusion criteria, a total of 1 548 patients were enrolled. Ultimately, 18 preoperative indicators were identified as effective predictive features, and 8 postoperative complications were defined as prediction labels for inclusion in the study. Within 30 days after surgery, 453 patients (29.2%) developed one or more complications. Considering overall accuracy, precision, recall, and F1-score, the random forest model [AUC=0.930, 95% CI (0.910, 0.950)] and the extreme gradient boosting model [AUC=0.909, 95% CI (0.880, 0.938)] demonstrated the best predictive performance. SHAP analysis revealed that anti-cyclic citrullinated peptide antibody, C-reactive protein, rheumatoid factor, interleukin-6, body mass index, age, and smoking status made significant contributions to the overall prediction of postoperative complications. CONCLUSION Machine learning-based models enable accurate prediction of postoperative complication risks among RA patients undergoing TKA. Inflammatory and immune-related blood biomarkers, such as anti-cyclic citrullinated peptide antibody, C-reactive protein, and rheumatoid factor, interleukin-6, play key predictive roles, highlighting their potential value in perioperative risk stratification and individualized management.
Humans ; Arthroplasty, Replacement, Knee/adverse effects* ; Arthritis, Rheumatoid/blood* ; Machine Learning ; Postoperative Complications/blood* ; Female ; Male ; Retrospective Studies ; Middle Aged ; Aged ; Risk Factors ; Preoperative Period ; C-Reactive Protein/analysis* ; Risk Assessment

OBJECTIVE: To explore serological and molecular characteristics of a family trio with weak B phenotype. METHODS: ABO blood type of the family was determined with a serological method. Genotypes of the ABO gene were determined with PCR-sequence specific primer (PCR-SSP). Sequences of exons 6 and 7 of the ABO gene were analyzed by PCR sequence-based testing. RESULTS: Serologically, the proband and her father were determined as B subtype (Bw), while her mother was of O group. The genotypes of the proband and her father were Bw12/O, while her mother was O01/O01. Sequencing of exons 6 and 7 of the ABO gene confirmed that the proband and her father were Bw12/O01. Compared with B101/O01, the Bw12 subtype carried a missense mutation (278C>T) in exon 6 of the ABO gene. CONCLUSION The 278C>T mutation probably underlies the Bw phenotype and can be transmitted stably.
ABO Blood-Group System ; genetics ; Alleles ; Base Sequence ; Exons ; Female ; Genotype ; Humans ; Male ; Mutation, Missense ; Phenotype ; Sequence Analysis, DNA

Objective To investigate the testing capabilities of clinical transfusion laboratories in medical institutions in Beijing for the regulatory authorities to formulate administrative policies in this regard.Methods Experts assigned by Beijing Clinical Transfusion Quality Control Center made on-site inspections at the transfusion laboratories in medical institutions using quality control products.They recorded the complete testing process of the operators as well as the instruments,detection reagents in use and the testing results,with statistics and analysis made to the data so collected.Results The pass rate of these on-site inspections was lower than that of the external quality assessment.Some laboratories failed to complete the testing of the quality control products in time and the actual operations in some laboratories were inconsistent to the guidelines.55.9％ of level Ⅰ hospitals and 25.6％ of level Ⅱ hospitals were found with insufficient and inadequate instruments and process layout to meet the needs of clinical blood transfusion.Some of the technicians were found without sufficient trainings in their professional knowledge and basic skills,resulting in their poor competence against emergency cases and weakness in independent problem solving.In addition,the records of detection process and results were found to be substandard.Conclusions Transfusion laboratories in Beijing need to improve their testing capabilities in general.