Bruno Ann M, Sandoval Grecio J, Hughes Brenna L, Grobman William A, Saade George R, Manuck Tracy A, Longo Monica, Simhan Hyagriv N, Rouse Dwight J, Mendez-Figueroa Hector, Gyamfi-Bannerman Cynthia, Bailit Jennifer L, Costantine Maged M, Sehdev Harish M, Tita Alan T N
University of Utah Health Sciences Center, Salt Lake City, Utah; the George Washington University Biostatistics Center, Washington, DC; the University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Northwestern University, Chicago, Illinois; the University of Texas Medical Branch, Galveston, and the University of Texas Health Science Center at Houston, Children's Memorial Hermann Hospital, Houston, Texas; the Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland; the University of Pittsburgh, Pittsburgh, and the University of Pennsylvania, Philadelphia, Pennsylvania; Brown University, Women and Infants Hospital of Rhode Island, Providence, Rhode Island; Columbia University, New York, New York; Case Western Reserve University, Cleveland, and The Ohio State University, Columbus, Ohio; and the University of Alabama at Birmingham, Birmingham, Alabama.
O G Open. 2025 Apr;2(2). doi: 10.1097/og9.0000000000000078. Epub 2025 Apr 24.
To develop and internally validate a practical and data-driven risk-scoring system to predict blood transfusion during hospitalization for delivery in a contemporary U.S. cohort.
This was a secondary analysis of a multicenter cohort of patients who delivered on randomly selected days at 17 U.S. hospitals (2019-2020). Patients with placenta accreta spectrum were excluded. The primary outcome was any blood transfusion during hospitalization for delivery. Candidate risk factors for transfusion were selected based on relevant literature. A multivariable logistic regression model was developed and internally validated using stratified k-fold (k=5) cross validation with stepwise backward elimination that used significance level of 0.05. Each risk factor included in the final model was assigned a point value by dividing the log of the odds ratio (OR) by the log of the OR of the factor with the lowest value. The summed points for an individual generate a numeric risk score predictive of transfusion. Performance of the risk score to predict transfusion was assessed using the area under the receiver operating curve (AUC).
Of 21,780 included individuals, 2.5% (n=545) received a blood transfusion. Factors associated with the highest risk for transfusion in the final model included thrombocytopenia, and placental abruption or significant antepartum bleeding. Risk score outputs among patients in the cohort ranged from 0 to 17 (maximum possible 26) with a corresponding predicted risk for transfusion from 1.0% to 84.4%. The AUC for prediction of transfusion in the validation subsample was 0.81 (95% CI, 0.76-0.85).
We developed a clinically applicable numeric risk score to predict blood transfusion during hospitalization for delivery. Future work should externally validate this risk-scoring system.
开发并在内部验证一个实用的、数据驱动的风险评分系统，以预测当代美国队列中分娩住院期间的输血情况。
这是一项对在美国17家医院随机选择的日子分娩的患者进行的多中心队列的二次分析（2019 - 2020年）。排除胎盘植入谱系疾病患者。主要结局是分娩住院期间的任何输血情况。根据相关文献选择输血的候选风险因素。使用分层k折（k = 5）交叉验证和逐步向后消除法（显著性水平为0.05）开发并在内部验证多变量逻辑回归模型。通过将比值比（OR）的对数除以具有最低值的因素的OR的对数，为最终模型中包含的每个风险因素分配一个分值。个体的总分值生成一个预测输血的数字风险评分。使用受试者操作特征曲线下面积（AUC）评估风险评分预测输血的性能。
在纳入的21,780名个体中，2.5%（n = 545）接受了输血。最终模型中与输血风险最高相关的因素包括血小板减少症、胎盘早剥或产前大量出血。队列中患者的风险评分输出范围为0至17（最大可能为26），相应的输血预测风险为1.0%至84.4%。验证子样本中预测输血的AUC为0.81（95%CI，0.76 - 0.85）。
我们开发了一个临床适用的数字风险评分来预测分娩住院期间的输血情况。未来的工作应在外部验证这个风险评分系统。
