Development and validation of a risk prediction model and management strategy for red blood cell irregular antibodies a retrospective cohort study of 521 patients

This study aimed to systematically identify independent risk factors for difficult cross-matching and to construct a predictive model for early clinical risk warning and stratified management. A derivation cohort of 521 patients with positive red blood cell irregular antibody screening results was retrospectively enrolled. Based on the screening results, patients were categorized into a 1-2 positive wells group (n = 428) and a 3 positive wells group (n = 93). Univariate and multivariate logistic regression analyses were performed to identify independent predictors of difficult cross-matching. A simplified clinical risk scoring system was developed based on the regression coefficients, and the predictive performance of the model was evaluated. Multivariate analysis identified six independent influencing factors: 3 positive wells (OR = 2.34, 95% CI: 1.18-4.65), diagnosis of multiple myeloma (OR = 5.05, 95% CI: 2.54-10.05), hospitalization in the hematology department (OR = 2.25, 95% CI: 1.27-3.98), age ≥ 60 years (OR = 1.97, 95% CI: 1.12-3.48), pregnancy-related diseases (protective factor; OR = 0.24, 95% CI: 0.08-0.73), and difficult blood typing (OR = 4.57, 95% CI: 2.30-9.08). The risk scoring model incorporating these factors demonstrated good predictive performance, with an area under the curve (AUC) of 0.819 (95% CI: 0.766-0.872), a sensitivity of 74.4%, and a specificity of 76.5%. Patients were stratified into three risk tiers (low, medium, and high) based on their scores, with corresponding differentiated clinical management pathways and resource allocation plans established. This study successfully identified and quantified key clinical and laboratory predictors for difficult cross-matching. The established risk scoring model demonstrates good predictive accuracy, providing a reliable tool for precise pre-transfusion risk assessment. The stratified management system based on this model facilitates a shift from reactive to proactive intervention in transfusion workflows, offering significant clinical translational value for optimizing blood bank resource allocation and ensuring transfusion safety for high-risk patients.