Pan Zhongmian, Charoenkwan Kittipat
Department of Obstetrics and Gynecology, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand.
Department of Obstetrics and Gynecology, Faculty of Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, China.
Diagnostics (Basel). 2024 Sep 12;14(18):2018. doi: 10.3390/diagnostics14182018.
This systematic review aimed to evaluate prediction models for perioperative blood transfusion in patients undergoing gynecologic surgery. Given the inherent risks associated with blood transfusion and the critical need for accurate prediction, this study identified and assessed models based on their development, validation, and predictive performance. The review included five studies encompassing various surgical procedures and approaches. Predicting factors commonly used across these models included preoperative hematocrit, race, surgical route, and uterine fibroid characteristics. However, the review highlighted significant variability in the definition of perioperative periods, a lack of standardization in transfusion criteria, and a high risk of bias in most models due to methodological issues, such as a low number of events per variable, inappropriate handling of continuous and categorical predictors, inappropriate handling of missing data, improper methods of predictor selection, inappropriate measurement methods for model performance, and inadequate evaluations of model overfitting and optimism in model performance. Despite some models demonstrating good discrimination and calibration, the overall quality and external validation of these models were limited. Consequently, there is a clear need for more robust and externally validated models to improve clinical decision-making and patient outcomes in gynecologic surgery. Future research should focus on refining these models, incorporating rigorous validation, and adhering to standardized reporting practices.
本系统评价旨在评估妇科手术患者围手术期输血的预测模型。鉴于输血存在的固有风险以及准确预测的迫切需求，本研究根据模型的开发、验证和预测性能对其进行了识别和评估。该评价纳入了五项涵盖各种手术程序和方法的研究。这些模型常用的预测因素包括术前血细胞比容、种族、手术途径和子宫肌瘤特征。然而，该评价强调围手术期定义存在显著差异，输血标准缺乏标准化，并且由于方法学问题，大多数模型存在较高的偏倚风险，如每个变量的事件数量少、对连续和分类预测变量处理不当、对缺失数据处理不当、预测变量选择方法不当、模型性能测量方法不当以及对模型过度拟合和模型性能乐观性评估不足。尽管一些模型显示出良好的区分度和校准度，但这些模型的整体质量和外部验证有限。因此，显然需要更强大且经过外部验证的模型，以改善妇科手术中的临床决策和患者预后。未来的研究应专注于完善这些模型，纳入严格的验证，并遵循标准化报告规范。
