Homcha Brittany E, Mets Elbert J, Goldenberg Michael D F, Kong Lan, Vaida Sonia J
From the Department of Anesthesiology and Perioperative Medicine, Penn State College of Medicine (B.E.H., M.D.F.G.), Hershey, PA; Cornell University (E.J.M.), Ithaca, NY; Division of Biostatistics and Bioinformatics (L.K.), Penn State College of Medicine Public Health Sciences; and Department of Anesthesiology & Perioperative Medicine (S.J.V.), Penn State Milton S. Hershey Medical Center, Hershey, PA.
Simul Healthc. 2017 Oct;12(5):314-318. doi: 10.1097/SIH.0000000000000246.
Estimated blood loss for surgical procedures performed via visual estimation is known to be an inaccurate method. Blood loss estimation is further complicated during cesarean delivery (CD) by a large volume loss for a short period as well as the presence of amniotic fluid. We hypothesized that a pictorial guide depicting premeasured blood volumes and materials commonly used in a CD would improve clinician accuracy in estimated blood loss.
A simulated CD scene was used to assess the ability of clinicians to estimate the amount of blood lost by a CD patient. Accuracy of the estimates was assessed before and after they had access to the pictorial guide created for the study.
Before the intervention, 52% of participants estimated more than 25% above or below actual blood loss volume. With use of the guide, clinicians became more accurate at blood loss estimation. After the intervention, the number of participants estimating within 5% of the actual volume increased from 7% before to 24% (P = 0.033).
An institution-specific pictorial guide is effective at improving the accuracy of visual blood loss estimation in a simulation and may help improve clinical care in CD.
通过视觉估计来确定外科手术的失血量是一种不准确的方法。剖宫产（CD）过程中的失血估计更为复杂，因为短时间内失血量很大，而且还有羊水存在。我们假设，一份描绘预先测量的血量以及剖宫产中常用材料的图片指南将提高临床医生估计失血量的准确性。
使用一个模拟的剖宫产场景来评估临床医生估计剖宫产患者失血量的能力。在他们使用为该研究创建的图片指南之前和之后，评估估计的准确性。
在干预之前，52%的参与者估计的失血量比实际失血量多25%以上或少25%以上。使用该指南后，临床医生在估计失血量方面变得更加准确。干预后，估计失血量在实际量的5%以内的参与者人数从之前的7%增加到24%（P = 0.033）。
一份针对特定机构的图片指南在模拟中能有效提高视觉失血量估计的准确性，可能有助于改善剖宫产的临床护理。
