Wang Wenting, Wang He, Liu Jia, Jin Yu, Ji Bingyang, Liu Jinping
Department of Anesthesiology, The Second Affiliated Hospital of Hainan Medical University, Haikou, China.
Department of Cardiopulmonary Bypass, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, No.167, North Lishi Road, Xicheng District, Beijing, 100037, China.
BMC Anesthesiol. 2025 Jan 31;25(1):49. doi: 10.1186/s12871-025-02917-2.
Timely recognition of perioperative red blood cell transfusion (PRT) risk is crucial for developing personalized blood management strategies in pediatric patients. In this study, we sought to construct a prediction model for PRT risk in pediatric patients undergoing cardiac surgery with cardiopulmonary bypass (CPB).
From September 2014 to December 2021, 23,884 pediatric patients under the age of 14 were randomly divided into training and testing cohorts at a 7:3 ratio. Variable selection was performed using univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was then used to identify predictors, and a nomogram was developed to predict PRT risk. The model's performance was evaluated based on discrimination, calibration, and clinical utility in both cohorts.
After multiple rounds of variable selection, eight predictors of PRT risk were identified: age, weight, preoperative hemoglobin levels, presence of cyanotic congenital heart disease, CPB duration, minimum rectal temperature during CPB, CPB priming volume, and the use of a small incision. The predictive model incorporating these variables demonstrated strong performance, with an area under the curve (AUC) of 0.886 (95% CI: 0.880-0.891) in the training cohort and 0.883 (95% CI: 0.875-0.892) in the testing cohort. The calibration plot closely aligned with the ideal diagonal line, and decision curve analysis indicated that the model provided a net clinical benefit.
Our predictive model exhibits good performance in assessing PRT risk in pediatric patients undergoing cardiac surgery with CPB, providing clinicians a practical tool to optimize individualized perioperative blood management strategies for this vulnerable population.
及时识别围手术期红细胞输血（PRT）风险对于制定儿科患者个性化血液管理策略至关重要。在本研究中，我们试图构建一个预测模型，用于预测接受体外循环（CPB）心脏手术的儿科患者的PRT风险。
2014年9月至2021年12月，23884名14岁以下儿科患者按7:3的比例随机分为训练组和测试组。使用单因素逻辑回归和最小绝对收缩和选择算子（LASSO）回归进行变量选择。然后使用多因素逻辑回归确定预测因素，并绘制列线图以预测PRT风险。基于两个队列中的区分度、校准度和临床实用性对模型性能进行评估。
经过多轮变量选择，确定了8个PRT风险预测因素：年龄、体重、术前血红蛋白水平、存在青紫型先天性心脏病、CPB持续时间、CPB期间最低直肠温度、CPB预充量和小切口的使用。纳入这些变量的预测模型表现出强大的性能，训练组的曲线下面积（AUC）为0.886（95%CI：0.880-0.891），测试组为0.883（95%CI：0.875-0.892）。校准图与理想对角线紧密对齐，决策曲线分析表明该模型提供了净临床益处。
我们的预测模型在评估接受CPB心脏手术的儿科患者的PRT风险方面表现良好，为临床医生提供了一个实用工具，以优化这一脆弱人群的个体化围手术期血液管理策略。
