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中国循证儿科杂志

中国循证儿科杂志 ›› 2023, Vol. 18 ›› Issue (6): 435-441.DOI: 10.3969/j.issn.1673-5501.2023.06.005

• 论著 • 上一篇    下一篇

儿童急性淋巴细胞白血病血小板输注影响因素及疗效预测模型的构建与验证

何柏霖1,2，郭玉霞1,3，温普生1，杨媛淇1，彭雪松1，朱静2   

  1. 重庆医科大学附属儿童医院，儿童发育疾病研究教育部重点实验室，国家儿童健康与疾病临床医学研究中心，儿童发育重大疾病国家国际科技合作基地，儿科学重庆市重点实验室重庆，400014；1 输血科；2 儿科研究所；3 血液肿瘤科

  • 收稿日期:2023-11-09 修回日期:2024-01-07 出版日期:2023-12-25 发布日期:2024-01-22
  • 通讯作者: 朱静

Influencing factors and efficacy prediction model of platelet transfusion for children with acute lymphoblastic leukemia

HE Bolin1,2, GUO Yuxia1,3, WEN Pusheng1, YANG Yuanqi1, PENG Xuesong1, ZHU Jing2   

  1. Children's Hospital of Chongqing Medical University, Key Laboratory of the Ministry of Education for Research on Child Developmental Diseases, National Clinical Medical Research Center for Child Health and Diseases, National International Science and Technology Cooperation Base for Major Child Developmental Diseases, Chongqing Key Laboratory of Pediatrics,  Chongqing 400014, China; 1 Department of Blood Transfusion, 2 Pediatric Research Institute, 3 Department of Hematology and Oncology
  • Received:2023-11-09 Revised:2024-01-07 Online:2023-12-25 Published:2024-01-22
  • Contact: ZHU Jing, email: jingzhu@cqmu.edu.cn

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摘要： 背景：血小板输注疗效影响临床诊疗效果及临床决策，目前缺乏针对急性淋巴细胞白血病(ALL)患儿的血小板输注后疗效的预测模型。 目的：构建及验证ALL患儿输注血小板后血小板校正计数增量(CCI)预测模型。 设计：回顾性队列研究。 方法：纳入2022年1月至2023年3月在重庆医科大学附属儿童医院血液肿瘤科初诊、初治的ALL住院患儿，以2022年12月31日为入院时间节点划分为建模组和验证组。采集可能影响血小板输注疗效的变量(性别、年龄、脾大或脾亢、出血等级、贫血程度、血小板储存天数、输血前使用药物、ALL形态学、危险度分组和治疗阶段等)进行单因素COX回归和Lasso回归分析，再行多因素COX回归确定最终预测因子，构建列线图预测模型，并对模型进行区分度和校准度的预测效能检测。 主要结局指标：输注血小板疗效CCI列线图模型的预测效能。 结果：研究期间133例247例次输注血小板病例纳入本研究，建模组198例次，验证组49例次，两组变量的基线差异均无统计学意义。最终纳入性别、年龄、ALL危险度分组、输注前白细胞数、脾大或脾亢、感染情况六个因子构建列线图预测模型。在建模组中，CCI为11.4（P25）、18.5（P50）和28.2（P75）时ROC曲线下面积（AUC）分别为0.783、0.695和0.654，在验证组中分别为0.765、0.714和0.580；校准曲线显示在建模组和验证组中CCI为11.4、18.5和28.2时模型预测发生率和实际发生率基本一致。 结论：成功构建ALL患儿血小板CCI预测模型；利用性别、年龄、输注前白细胞数、感染情况、ALL危险度分组、脾大或脾亢建立的列线图预测模型对ALL患儿血小板输注疗效具有较好的预测效能。

关键词: 儿童急性淋巴细胞白血病, 血小板输注, 血小板校正计数增量, 预测模型

Abstract: Background: Platelet transfusion efficacy affects clinical outcomes and clinical decision making, and there is a lack of specific efficacy prediction models for platelet transfusion in children with acute lymphoblastic leukemia (ALL). Objective: To develop and validate a predictive model for platelet corrected count increment (CCI) in pediatric ALL after platelet transfusion. Design: A retrospective cohort study. Methods: Hospitalized ALL children who had been transfused with platelets in the Department of Hematology and Oncology at Affiliated Children's Hospital of Chongqing Medical University from January 2022 to March 2023 for initial diagnosis and treatment were included, and were divided into the modeling group (before December 31, 2022 ) and the validation group (after December 31, 2022). The history data that might affect the efficacy of platelet transfusion were collected including gender, age, splenomegaly or hyper-splenism, hemorrhagic grade, degree of anemia, platelet storage days, medications, morphology, risk grouping, and treatment stage. Univariable COX regression and Lasso regression analysis were performed, followed by multivariable COX regression to determine the final predictors. The column-line graph prediction model was established built on the results. To detect the predictive efficacy, the model was then evaluated for its differentiation and calibration. Main outcome measures: Predictive efficacy of the CCI line graph model for platelet transfusion efficacy. Results: A total of 133 cases with 247 platelet transfusions were included in this study during the study period. There were a total of 198 transfusions in the modeling group and 49 transfusions in the validation group, and the baseline differences in the variables between the two groups were not statistically significant. Six factors, namely, gender, age, ALL risk grouping, pre-transfusion leukocyte count, splenomegaly or hypersplenism, and infection status, were finally incorporated to construct a column-line graph prediction model. When CCI was 11.4 (P25), 18.5 (P50), and 28.2 (P75), the area under curve (AUC) values were respectively 0.783, 0.695, and 0.654 in the modeling group, and 0.765, 0.714, and 0.580 in the validation group. The calibration curves showed that the model-predicted incidence rates and the actual incidence rates were basically the same in the modeling and validation group when CCI was 11.4, 18.5, and 28.2. Conclusion: A predictive model for platelets CCI after platelet transfusion in children with ALL was established. Good predictive efficacy for platelet transfusion efficacy in children with ALL was confirmed by a column-line graph predictive model using influencing factors of gender, age, pre-transfusion leukemia count, infection, ALL risk grouping, and splenomegaly or hypersplenism.

Key words: Pediatric acute lymphoblastic leukemia, Platelet transfusion, CCI, Predictive model