妇科手术后预测输血风险模型的构建
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A model to predict risk of blood transfusion after gynecologic surgery
背景与目的
预测患者输血风险的模型可能有助于选择性术前检查及更有效的围术期血液管理及利用。我们试图构建和验证一种模型，预测患者妇科手术后需要输血的风险。
方 法
分析了2010年1月至2014年6月间共116名外科医生在一个卫生系统中的10个机构进行妇科手术的18,319名妇女。数据集分为2010年1月至2012年12月期间进行的12,219例手术的模拟训练队列，以及从2013年1月至2014年6月进行的6100例手术的单独验证队列。总共收集了47个候选输血危险因素。多个回归分析模型用于于训练队列，以预测手术后30天内的输血。使用“逐减法”删除变量，找到最好的简要模型。使用一致指数测量模型分辨率。该模型使用1000个自举样本进行内部验证，并通过测试模型在验证队列中的性能进行临时验证。绘制校准和决策曲线，以便临床医师了解预测概率的准确性，以及该模型是否在作出决策时增加了临床意义。
结 果
训练队列输血率为2％（95％CI：1.72-2.22）。该模型在组内验证（偏差校正的一致指数：0.906; 95％CI：0.890-0.928）中具有明显的区别和校准，并使用单独的验证队列（一致性指数：0.915; 95％CI： 0.872-0.954）确定临时校验精确性。校准曲线表明该模型准确度高达40％，然后开始过度预测风险。当临床决策阈值在0-50％的预测风险之间时，该模型有较好的意义。
结 论
该模型准确预测了妇科手术后患者的输血风险，便于选择性术前检查和更有效的围手术期血液管理及利用。
原始文献摘要
Stanhiser J, Chagin K, Jelovsek J E；American Journal of Obstetrics & Gynecology, 2017；；A Model to Predict Risk of Blood Transfusion After Gynecologic Surgery.
BACKGROUND: A model that predicts a patient’s risk of receiving a blood transfusion may facilitate selective preoperative testing and more
efficient perioperative blood management utilization.
OBJECTIVE: We sought to construct and validate a model that predicts a patient’s risk of receiving a blood transfusion after gynecologic surgery.
STUDY DESIGN: In all, 18,319 women who underwent gynecologic surgery at 10 institutions in a single health system by 116 surgeons from January 2010 through June 2014 were analyzed. The data set was split into a model training cohort of 12,219 surgeries performed from January 2010 through December 2012 and a separate validation cohort of 6100 surgeries performed from January 2013 through June 2014. In all, 47 candidate risk factors for transfusion were collected. Multiple logistic models were fit onto the training cohort to predict transfusion within 30 days of surgery. Variables were removed using stepwise backward reduction to find the best parsimonious model. Model discrimination was measured using the
concordance index. The model was internally validated using 1000 bootstrapped samples and temporally validated by testing the model’s performance in the validation cohort. Calibration and decision curves were plotted to inform clinicians about the accuracy of predicted probabilities and whether the model adds clinical benefit when
making decisions.
RESULTS: The transfusion rate in the training cohort was 2% (95% confidence interval, 1.72e2.22). The model had excellent discrimination and calibration during internal validation (bias-corrected concordance index, 0.906; 95% confidence interval, 0.890e0.928) and maintained accuracy during temporal validation using the separate validation cohort (concordance index, 0.915; 95% confidence interval, 0.872e0.954).Calibration curves demonstrated the model was accurate up to 40% then it began to overpredict risk. The model provides superior net benefit when clinical decision thresholds are between 0-50% predicted risk.
CONCLUSION: This model accurately predicts a patient’s risk of transfusion after gynecologic surgery facilitating selective preoperative testing and more efficient perioperative blood management utilization.
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