Liu Shun, Zhou Rong, Xia Xing-Qiu, Ren He, Wang Le-Ye, Sang Rui-Rui, Jiang Mi, Yang Chun-Chen, Liu Huan, Wei Lai, Rong Rui-Ming
Department of Cardiovascular Surgery, Zhongshan Hospital, Shanghai Cardiovascular Institution, Fudan University, Shanghai, China.
Department of Transfusion, Zhongshan Hospital, Fudan University, Shanghai, China.
Ann Transl Med. 2021 Apr;9(7):530. doi: 10.21037/atm-20-7375.
Red blood cell (RBC) transfusion therapy has been widely used in surgery, and has yielded excellent treatment outcomes. However, in some instances, the demand for RBC transfusion is assessed by doctors based on their experience. In this study, we use machine learning models to predict the need for RBC transfusion during mitral valve surgery to guide the surgeon's assessment of the patient's need for intraoperative blood transfusion.
We retrospectively reviewed 698 cases of isolated mitral valve surgery with and without combined tricuspid valve operation. Seventy percent of the database was used as the training set and the remainder as the testing set for 13 machine learning algorithms to build a model to predict the need for intraoperative RBC transfusion. According to the characteristic value of model mining, we analyzed the risk-related factors to determine the main effects of variables influencing the outcome.
A total of 166 patients of the cases considered had undergone intraoperative RBC transfusion (24.52%). Of the 13 machine learning algorithms, CatBoost delivered the best performance, with an AUC of 0.888 (95% CI: 0.845-0.909) in testing set. Further analysis using the CatBoost model revealed that hematocrit (<37.81%), age (>64 y), body weight (<59.92 kg), body mass index (BMI) (<22.56 kg/m), hemoglobin (<122.6 g/L), type of surgery (median thoracotomy surgery), height (<160.61 cm), platelet (>194.12×10/L), RBC (<4.08×10/L), and gender (female) were the main risk-related factors for RBC transfusion. A total of 204 patients were tested, 177 of whom were predicted accurately (86.8%).
Machine learning models can be used to accurately predict the outcomes of RBC transfusion, and should be used to guide surgeons in clinical practice.
红细胞（RBC）输血疗法已在外科手术中广泛应用，并取得了良好的治疗效果。然而，在某些情况下，医生根据经验评估RBC输血需求。在本研究中，我们使用机器学习模型预测二尖瓣手术期间的RBC输血需求，以指导外科医生评估患者术中输血需求。
我们回顾性分析了698例单纯二尖瓣手术病例，其中部分病例合并三尖瓣手术。将数据库的70%用作训练集，其余用作测试集，用于13种机器学习算法构建预测术中RBC输血需求的模型。根据模型挖掘的特征值，我们分析了风险相关因素，以确定影响结果的变量的主要作用。
在所考虑的病例中，共有166例患者接受了术中RBC输血（24.52%）。在13种机器学习算法中，CatBoost表现最佳，测试集中的AUC为0.888（95%CI：0.845 - 0.909）。使用CatBoost模型的进一步分析表明，血细胞比容（<37.81%）、年龄（>64岁）、体重（<59.92 kg）、体重指数（BMI）（<22.56 kg/m²）、血红蛋白（<122.6 g/L）、手术类型（正中开胸手术）、身高（<160.61 cm）、血小板（>194.12×10⁹/L）、红细胞（<4.08×10¹²/L）和性别（女性）是RBC输血的主要风险相关因素。共对204例患者进行了测试，其中177例预测准确（86.8%）。
机器学习模型可用于准确预测RBC输血结果，并应用于临床实践中指导外科医生。
