Zou Yu, Jiang Tianhua, Fan Yue, Liang Simin, Lin Long, Zheng Mao
Department of Blood Transfusion, Deyang People's Hospital, Deyang, China.
Department of Clinical Laboratory, Deyang People's Hospital, Deyang, China.
Front Med (Lausanne). 2025 Aug 7;12:1632042. doi: 10.3389/fmed.2025.1632042. eCollection 2025.
This study aimed to systematically analyze the independent risk factors for platelet transfusion refractoriness (PTR) in hematological patients, and to develop and validate a nomogram prediction model, thereby providing scientific evidence for personalized platelet transfusion strategies in clinical practice.
A retrospective cohort study was conducted involving 363 platelet transfusion episodes in hematological patients who received platelet transfusions at Deyang People's Hospital between January 2023 and August 2023. Comprehensive clinical data and laboratory parameters were collected. Potential PTR-related factors were initially identified through univariate analysis, followed by multivariate logistic regression to determine independent risk factors. Using Rstudio software, a nomogram prediction model was constructed based on the identified factors. The model's performance was rigorously evaluated through receiver operating characteristic (ROC) curve analysis, calibration curves, and internal validation using bootstrap resampling (1,000 repetitions) to assess discrimination, calibration, and clinical applicability.
This study retrospectively analyzed 363 platelet transfusion episodes involving 131 hematological patients, the incidence of PTR was 30.85% (112/363). Multivariate logistic regression analysis revealed four independent risk factors for PTR: female gender (OR = 1.876, 95% CI: 1.147-3.067), transfusion frequency ≥ 10 times (OR = 2.552, 95% CI: 1.089-5.981), splenomegaly (OR = 3.170, 95% CI: 1.334-7.534), and antibiotic usage (OR = 2.177, 95% CI: 1.078-4.396) (all < 0.05). The predictive model demonstrated an area under the ROC curve of 0.673 (95% CI: 0.611-0.735), with specificity of 78.1%, sensitivity of 55.4%, Youden's index of 0.335, and an optimal cutoff value of 0.320. Internal validation confirmed good consistency between predicted probabilities and actual observations.
We successfully developed and validated a PTR prediction model incorporating gender, transfusion frequency, splenomegaly, and antibiotic usage as key risk factors. This model exhibits promising clinical utility and can serve as an objective tool for optimizing individualized platelet transfusion protocols in hematological patients.
本研究旨在系统分析血液病患者血小板输注无效（PTR）的独立危险因素，并开发和验证列线图预测模型，从而为临床实践中个性化血小板输注策略提供科学依据。
进行一项回顾性队列研究，纳入2023年1月至2023年8月在德阳市人民医院接受血小板输注的血液病患者的363次血小板输注事件。收集综合临床数据和实验室参数。通过单因素分析初步确定潜在的PTR相关因素，随后进行多因素逻辑回归以确定独立危险因素。使用Rstudio软件，基于确定的因素构建列线图预测模型。通过受试者工作特征（ROC）曲线分析、校准曲线以及使用自抽样重采样（1000次重复）进行内部验证来严格评估模型性能，以评估区分度、校准度和临床适用性。
本研究回顾性分析了涉及131例血液病患者的363次血小板输注事件，PTR发生率为30.85%（112/363）。多因素逻辑回归分析显示PTR的四个独立危险因素：女性（OR = 1.876，95%CI：1.147 - 3.067）、输血频率≥10次（OR = 2.552，95%CI：1.089 - 5.981）、脾肿大（OR = 3.170，95%CI：1.334 - 7.534）和使用抗生素（OR = 2.177，95%CI：1.078 - 4.396）（均P < 0.05）。预测模型的ROC曲线下面积为0.673（95%CI：0.611 - 0.735），特异性为78.1%，灵敏度为55.4%，约登指数为0.335，最佳截断值为0.320。内部验证证实预测概率与实际观察结果之间具有良好的一致性。
我们成功开发并验证了一个将性别、输血频率、脾肿大和抗生素使用作为关键危险因素的PTR预测模型。该模型具有良好的临床实用性，可作为优化血液病患者个体化血小板输注方案的客观工具。
