Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK.
J Clin Epidemiol. 2023 Jul;159:10-30. doi: 10.1016/j.jclinepi.2023.05.002. Epub 2023 May 6.
Blood transfusion can be a lifesaving intervention after perioperative blood loss. Many prediction models have been developed to identify patients most likely to require blood transfusion during elective surgery, but it is unclear whether any are suitable for clinical practice.
We conducted a systematic review, searching MEDLINE, Embase, PubMed, The Cochrane Library, Transfusion Evidence Library, Scopus, and Web of Science databases for studies reporting the development or validation of a blood transfusion prediction model in elective surgery patients between January 1, 2000 and June 30, 2021. We extracted study characteristics, discrimination performance (c-statistics) of final models, and data, which we used to perform risk of bias assessment using the Prediction model risk of bias assessment tool (PROBAST).
We reviewed 66 studies (72 developed and 48 externally validated models). Pooled c-statistics of externally validated models ranged from 0.67 to 0.78. Most developed and validated models were at high risk of bias due to handling of predictors, validation methods, and too small sample sizes.
Most blood transfusion prediction models are at high risk of bias and suffer from poor reporting and methodological quality, which must be addressed before they can be safely used in clinical practice.
围手术期失血后，输血可以是一种救命的干预措施。已经开发了许多预测模型来识别择期手术中最有可能需要输血的患者，但尚不清楚是否有任何模型适合临床实践。
我们进行了系统评价，检索了 MEDLINE、Embase、PubMed、The Cochrane Library、输血证据库、Scopus 和 Web of Science 数据库，以查找 2000 年 1 月 1 日至 2021 年 6 月 30 日期间报告在择期手术患者中开发或验证输血预测模型的研究。我们提取了研究特征、最终模型的区分性能（c 统计量）和数据，我们使用预测模型风险偏倚评估工具（PROBAST）来进行风险评估。
我们回顾了 66 项研究（72 项开发和 48 项外部验证模型）。外部验证模型的汇总 c 统计量范围为 0.67 至 0.78。由于处理预测因子、验证方法和样本量过小，大多数开发和验证模型的偏倚风险较高。
大多数输血预测模型存在较高的偏倚风险，并且存在报告和方法学质量差的问题，在安全地将其用于临床实践之前，必须解决这些问题。
