From the Centre for Trauma Sciences (W.O., M.M.), Blizard Institute, Queen Mary University of London; and Barts Health NHS Trust (S.T., Z.P.), London, United Kingdom.
J Trauma Acute Care Surg. 2024 Oct 1;97(4):651-659. doi: 10.1097/TA.0000000000004385. Epub 2024 May 9.
Hemorrhage is a leading cause of preventable death in trauma. Accurately predicting a patient's blood transfusion requirement is essential but can be difficult. Machine learning (ML) is a field of artificial intelligence that is emerging within medicine for accurate prediction modeling. This systematic review aimed to identify and evaluate all ML models that predict blood transfusion in trauma.
This systematic review was registered on the International Prospective register of Systematic Reviews (CRD4202237110). MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials were systematically searched. Publications reporting an ML model that predicted blood transfusion in injured adult patients were included. Data extraction and risk of bias assessment were performed using validated frameworks. Data were synthesized narratively because of significant heterogeneity.
Twenty-five ML models for blood transfusion prediction in trauma were identified. Models incorporated diverse predictors and varied ML methodologies. Predictive performance was variable, but eight models achieved excellent discrimination (area under the receiver operating characteristic curve, >0.9) and nine models achieved good discrimination (area under the receiver operating characteristic curve, >0.8) in internal validation. Only two models reported measures of calibration. Four models have been externally validated in prospective cohorts: the Bleeding Risk Index, Compensatory Reserve Index, the Marsden model, and the Mina model. All studies were considered at high risk of bias often because of retrospective data sets, small sample size, and lack of external validation.
This review identified 25 ML models developed to predict blood transfusion requirement after injury. Seventeen ML models demonstrated good to excellent performance in silico, but only four models were externally validated. To date, ML models demonstrate the potential for early and individualized blood transfusion prediction, but further research is critically required to narrow the gap between ML model development and clinical application.
Systematic Review Without Meta-analysis; Level IV.
出血是创伤导致可预防死亡的主要原因。准确预测患者的输血需求至关重要，但可能较为困难。机器学习（ML）是人工智能领域的一个分支，正在医学领域崭露头角，可用于准确的预测建模。本系统评价旨在确定和评估所有用于预测创伤患者输血的 ML 模型。
本系统评价已在国际前瞻性系统评价登记处（CRD4202237110）注册。系统地检索了 MEDLINE、Embase 和 Cochrane 对照试验中心注册库。纳入报告用于预测成年创伤患者输血的 ML 模型的出版物。使用经过验证的框架进行数据提取和偏倚风险评估。由于存在显著的异质性，因此采用叙述性方法对数据进行综合。
确定了 25 个用于预测创伤输血的 ML 模型。模型纳入了多样化的预测因素和不同的 ML 方法学。预测性能存在差异，但 8 个模型在内部验证中实现了优秀的区分度（接受者操作特征曲线下面积>0.9），9 个模型实现了良好的区分度（接受者操作特征曲线下面积>0.8）。仅有 2 个模型报告了校准措施。4 个模型在前瞻性队列中进行了外部验证：出血风险指数、代偿储备指数、Marsden 模型和 Mina 模型。所有研究均被认为存在较高的偏倚风险，通常是因为数据来自回顾性研究、样本量较小以及缺乏外部验证。
本评价确定了 25 个用于预测受伤后输血需求的 ML 模型。17 个 ML 模型在计算机模拟中表现出良好到优秀的性能，但仅有 4 个模型进行了外部验证。迄今为止，ML 模型展示了用于早期和个体化输血预测的潜力，但迫切需要进一步研究以缩小 ML 模型开发与临床应用之间的差距。
无荟萃分析的系统评价；IV 级。
