School of Population and Global Health, The University of Western Australia, Perth, Australia.
Data and Digital Innovation, East Metropolitan Health Service, Perth, Australia.
Blood Transfus. 2023 Jan;21(1):42-49. doi: 10.2450/2022.0295-21. Epub 2022 Mar 11.
Predicting red cell transfusion may assist in identifying those most likely to benefit from patient blood management strategies. Our objective was to identify a simple statistical model to predict transfusion in elective surgery from routinely available data.
Our final multicentre cohort consisted of 42,546 patients and contained the following potential predictors of red cell transfusion known prior to admission: patient age, sex, pre-admission hemoglobin, surgical procedure, and comorbidities. Missing data were handled by multiple imputation methods. The outcome measure of interest was administration of a red cell transfusion. We used multivariable logistic regression models to predict transfusion, and evaluated the performance by applying a 10-fold cross-validation. Model accuracy was assessed by comparing the area under the receiver operating characteristics curve. After applying an optimal probability cut-off we measured model accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.
7.0% (n=2,993) of the study population received a red cell transfusion. Our most simple model predicted red cell transfusion based on admission hemoglobin and surgical procedure with a multiply imputed estimated area under the curve of 0.862 (0.856, 0.864). The estimated accuracy, sensitivity, specificity, positive predictive, and negative predictive values at the probability cut-off of 0.4 were 0.934, 0.257, 0.986, 0.573, and 0.946 respectively.
A small number of variables available prior to admission can predict red cell transfusion with very good accuracy. Our model can be used to flag high-risk patients most likely to benefit from pre-operative patient blood management measures.
预测红细胞输血可能有助于确定最有可能从患者血液管理策略中受益的人群。我们的目标是确定一种简单的统计模型，以便从常规可用数据中预测择期手术中的输血。
我们的最终多中心队列包括 42546 名患者，其中包含已知的输血前预测因素，包括患者年龄、性别、入院前血红蛋白、手术程序和合并症。采用多重插补方法处理缺失数据。感兴趣的结局指标为红细胞输血的输注。我们使用多变量逻辑回归模型预测输血，并通过应用 10 折交叉验证来评估模型性能。通过比较接受者操作特征曲线下的面积来评估模型准确性。应用最佳概率截断值后，我们测量了模型的准确性、敏感性、特异性、阳性预测值和阴性预测值。
研究人群中有 7.0%（n=2993）接受了红细胞输血。我们最简化的模型基于入院时的血红蛋白和手术程序预测红细胞输血，插补后估计的曲线下面积为 0.862（0.856，0.864）。概率截断值为 0.4 时，估计的准确性、敏感性、特异性、阳性预测值和阴性预测值分别为 0.934、0.257、0.986、0.573 和 0.946。
入院前可用的少数变量可以非常准确地预测红细胞输血。我们的模型可用于标记高风险患者，这些患者最有可能从术前患者血液管理措施中受益。
