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<p>To identify risk factors, mechanisms, and develop a validated predictive model for platelet transfusion refractoriness (PTR) in pediatric oncology to optimize transfusion outcomes. This retrospective cohort analyzed pediatric oncology patients receiving platelet transfusions, stratified by response (effective vs. refractory). Statistically significant indicators from t-tests underwent multivariable logistic regression to determine independent PTR predictors [platelet count (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer (D-D)]. A combined predictor derived via logistic regression was evaluated using ROC analysis. A Nomogram integrating PLT, hemoglobin (Hb), PT, APTT, and D-D was developed, with calibration (calibration curves) and clinical utility (decision curve analysis, DCA) rigorously validated. Multivariable logistic regression revealed an inverse association between elevated PLT and PTR risk (OR = 1.396), while elevated PT, APTT, and D-D were directly associated with increased PTR risk (OR: 0.794, 0.943, and &lt; 0.001, respectively). The combined predictor demonstrated excellent discriminatory power (AUC = 0.962). The nomogram model exhibited high calibration accuracy in both training and validation sets, as evidenced by well-fitted calibration curves. DCA confirmed superior net benefit compared to alternative strategies. High AUC values (training set: 0.9723; validation set: 0.9639) indicated robust discrimination of PTR risk. PLT, PT, APTT, and D-dimer are key modulators of PTR risk in pediatric oncology. The developed nomogram model demonstrates favorable calibration, strong discriminatory ability, and significant clinical utility. It enables quantitative PTR risk stratification and guides pathway-targeted interventions, providing a precision tool for optimizing platelet transfusion management in this vulnerable population.</p>

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Influencing Factor Analysis and Predictive Model Development for Platelet Transfusion Refractoriness in Pediatric Oncology Patients

  • 期刊论文

  • 发表日期：

    2025年10月16日

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  • Kelu Huang,
  • Zheng Luo,
  • Haiyan Peng

摘要

To identify risk factors, mechanisms, and develop a validated predictive model for platelet transfusion refractoriness (PTR) in pediatric oncology to optimize transfusion outcomes. This retrospective cohort analyzed pediatric oncology patients receiving platelet transfusions, stratified by response (effective vs. refractory). Statistically significant indicators from t-tests underwent multivariable logistic regression to determine independent PTR predictors [platelet count (PLT), prothrombin time (PT), activated partial thromboplastin time (APTT), D-dimer (D-D)]. A combined predictor derived via logistic regression was evaluated using ROC analysis. A Nomogram integrating PLT, hemoglobin (Hb), PT, APTT, and D-D was developed, with calibration (calibration curves) and clinical utility (decision curve analysis, DCA) rigorously validated. Multivariable logistic regression revealed an inverse association between elevated PLT and PTR risk (OR = 1.396), while elevated PT, APTT, and D-D were directly associated with increased PTR risk (OR: 0.794, 0.943, and < 0.001, respectively). The combined predictor demonstrated excellent discriminatory power (AUC = 0.962). The nomogram model exhibited high calibration accuracy in both training and validation sets, as evidenced by well-fitted calibration curves. DCA confirmed superior net benefit compared to alternative strategies. High AUC values (training set: 0.9723; validation set: 0.9639) indicated robust discrimination of PTR risk. PLT, PT, APTT, and D-dimer are key modulators of PTR risk in pediatric oncology. The developed nomogram model demonstrates favorable calibration, strong discriminatory ability, and significant clinical utility. It enables quantitative PTR risk stratification and guides pathway-targeted interventions, providing a precision tool for optimizing platelet transfusion management in this vulnerable population.