Objective: Blood transfusions, crucial in managing anemia and coagulopathy in ICU settings, require accurate prediction for effective resource allocation and patient risk assessment. However, existing clinical decision support systems have primarily targeted a particular patient demographic with unique medical conditions and focused on a single type of blood transfusion. This study aims to develop an advanced machine learning-based model to predict the probability of transfusion necessity over the next 24 hours for a diverse range of non-traumatic ICU patients. Methods: We conducted a retrospective cohort study on 72,072 adult non-traumatic ICU patients admitted to a high-volume US metropolitan academic hospital between 2016 and 2020. We developed a meta-learner and various machine learning models to serve as predictors, training them annually with four-year data and evaluating on the fifth, unseen year, iteratively over five years. Results: The experimental results revealed that the meta-model surpasses the other models in different development scenarios. It achieved notable performance metrics, including an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.97, an accuracy rate of 0.93, and an F1-score of 0.89 in the best scenario. Conclusion: This study pioneers the use of machine learning models for predicting blood transfusion needs in a diverse cohort of critically ill patients. The findings of this evaluation confirm that our model not only predicts transfusion requirements effectively but also identifies key biomarkers for making transfusion decisions.

翻译：目的：输血在重症监护病房（ICU）中用于管理贫血和凝血障碍至关重要，精准预测输血需求有助于实现资源有效配置和患者风险评估。然而，现有临床决策支持系统主要针对具有特定医疗条件的特定患者群体，且仅关注单一类型输血。本研究旨在开发一种基于先进机器学习的方法，用于预测各类非创伤性ICU患者在随后24小时内输血需求的概率。方法：我们开展了一项回顾性队列研究，纳入2016年至2020年间入住美国某大型城市学术医院的重症监护室的72,072名非创伤性成人ICU患者。我们构建了一个元学习器及多种机器学习模型作为预测器，采用四年数据逐年训练模型，并在第五年（未见过数据）上进行评估，逐次迭代五年。结果：实验结果表明，在不同开发场景下，元模型优于其他模型。在最佳场景中，其取得了若干显著性能指标，包括受试者工作特征曲线下面积（AUROC）为0.97，准确率为0.93，F1分数为0.89。结论：本研究首次将机器学习模型用于预测广泛危重患者群体的输血需求。评估结果证实，我们的模型不仅能有效预测输血需求，还能识别出用于输血决策的关键生物标志物。