Qi Jing, Chen Yinchu, Jin Xiaoke, Wang Ran, Wang Nana, Yan Jiawei, Huang Chen, Huang Jun, Wei Yuanfeng, Xie Faqin, Yu Zhengzhi, Huang Dongping
Department of Hematology, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.
J Int Med Res. 2024 Dec;52(12):3000605241305360. doi: 10.1177/03000605241305360.
Mobilization and collection of peripheral blood stem cells (PBSCs) are time-intensive and costly. Excessive apheresis sessions can cause physical discomfort for donors and increase the costs associated with collection. Therefore, it is essential to identify key predictive factors for successful harvests to minimize the need for multiple apheresis procedures.
We retrospectively analyzed 88 PBSC donations at our hospital. Mobilization involved disease-specific chemotherapy plus human recombinant granulocyte-colony-stimulating factor (G-CSF; lenograstim) or G-CSF alone for 5 days, followed by apheresis on day 5. The baseline characteristics of donors, pre-apheresis complete blood counts, and CD34+ cells were evaluated. Univariate logistic regression, the eXtreme Gradient Boosting algorithm, and multivariate logistic regression were applied to select significant predictive variables. The multivariate logistic regression results were integrated into various machine learning models to assess predictive accuracy.
The percentage of pre-collection monocytes (Mono%), age, and CD34+ cell percentage (CD34+ cell%) were identified as significant independent factors that could accurately predict the success of an initial PBSC harvest.
We used machine learning methods to identify and validate Mono%, age, and CD34+ cell% as significant factors predictive of successful PBSC harvest on the first attempt, offering important insight to guide the clinical harvesting of PBSCs.
外周血干细胞（PBSCs）的动员和采集耗时且成本高昂。过多的单采程序会给供者带来身体不适，并增加采集相关成本。因此，识别成功采集的关键预测因素以尽量减少多次单采程序的必要性至关重要。
我们回顾性分析了我院88例PBSC捐献情况。动员包括针对特定疾病的化疗加重组人粒细胞集落刺激因子（G-CSF；来格司亭）或仅使用G-CSF 5天，随后在第5天进行单采。评估了供者的基线特征、单采前全血细胞计数和CD34+细胞。应用单因素逻辑回归、极端梯度提升算法和多因素逻辑回归来选择显著的预测变量。将多因素逻辑回归结果整合到各种机器学习模型中以评估预测准确性。
采集前单核细胞百分比（Mono%）、年龄和CD34+细胞百分比（CD34+ cell%）被确定为能够准确预测首次PBSC采集成功的显著独立因素。
我们使用机器学习方法识别并验证了Mono%、年龄和CD34+ cell%是首次PBSC采集成功的显著预测因素，为指导PBSC的临床采集提供了重要见解。
