ArticleSAGE open medicine2025
Prediction model for cytomegalovirus infection following hematopoietic stem cell transplantation in patients with β-thalassemia major.
Article in SAGE open medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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16 authors.
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Abstract
Background: Cytomegalovirus infection is a common complication following hematopoietic stem cell transplantation that significantly influences clinical outcomes. Objectives: To develop and validate a predictive model for cytomegalovirus infection risk in patients with β-thalassemia major undergoing hematopoietic stem cell transplantation. Design: Retrospective cohort study. Methods: Clinical data from 291 β-thalassemia major patients undergoing hematopoietic stem cell transplantation were retrospectively analyzed. Independent risk factors identified via univariate and multivariate logistic regression analyses formed the basis of a predictive nomogram. The model's performance was evaluated by the concordance index (C-index), receiver operating characteristic curves, calibration plots, and decision curve analysis. Internal validation was performed using bootstrap resampling, and external validation was conducted with an independent cohort of 84 patients from another center. Results: Three independent predictors of cytomegalovirus infection were identified: serum albumin levels, donor type, and grade III-IV acute graft-versus-host disease. A nomogram incorporating these predictors was established, demonstrating good discriminative ability (C-index: 0.745; 95% CI: 0.684-0.807). Internal and external validations yielded C-indices of 0.746 and 0.649, respectively. Receiver operating characteristic analysis showed an area under the curve of 0.745 in the training cohort and 0.649 in the validation cohort. Conclusion: We developed and validated a reliable predictive model for assessing cytomegalovirus infection risk after hematopoietic stem cell transplantation in β-thalassemia major patients. This scoring system offers clinicians a practical tool for early risk stratification and intervention.
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