What question did this study set out to answer?

To identify risk factors for difficult cross-matching and to develop a predictive model for risk assessment.

April 3, 2026Clinical and Experimental Medicine0 citationsOpen Access

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

Key Points

To identify risk factors for difficult cross-matching and to develop a predictive model for risk assessment.
Retrospective cohort study of 521 patients with positive antibody screening results
Categorization into groups based on positive well results
Univariate and multivariate logistic regression analyses to identify predictors
Development of a clinical risk scoring system from regression coefficients
Evaluation of model performance using area under the curve (AUC) metrics.
Identified six independent risk factors for difficult cross-matching
Model demonstrated an AUC of 0.819, sensitivity of 74.4%, and specificity of 76.5%
Patients categorized into low, medium, and high-risk tiers for transfusion
Established a management strategy tailored to each risk category
Promoted proactive interventions in blood transfusion workflows.

Abstract

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.

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