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围生期血栓预防风险评估模型的性能。

Performance of Risk Assessment Models for Peripartum Thromboprophylaxis.

机构信息

1 School of Medicine, The University of Texas Medical Branch, Galveston, TX, USA.

2 Department of Obstetrics and Gynecology, The University of Texas Medical Branch, Galveston, TX, USA.

出版信息

Reprod Sci. 2019 Sep;26(9):1243-1248. doi: 10.1177/1933719118813197. Epub 2018 Nov 28.

DOI:10.1177/1933719118813197
PMID:30486735
Abstract

OBJECTIVE

There is no consensus on which risk stratification approach to use for thromboprophylaxis in pregnancy, and most available risk assessment models (RAMs) for venous thromboembolism (VTE) events have not been validated in pregnancy. Our objective was to compare the performance of some of the most commonly used VTEs RAMs in our patient population in the peripartum period.

STUDY DESIGN

This is a retrospective cohort of women who delivered at our institution in 2015 and 2016. We excluded patients with history of prior or current VTEs or those already receiving anticoagulants. Antepartum, intrapartum, and postpartum records were reviewed. Data were collected on known risk factors for VTEs in order to calculate scores for 3 RAMs on admission for delivery: Padua, Caprini, and Royal College of Obstetricians and Gynaecologists (RCOG). The primary objective was to the estimate the performance of the various RAMs in preventing postpartum VTEs. We calculated the proportion of women who would have been high risk, determined the number of VTEs cases within high- and low-risk categories, as well as calculated the number needed to treat (NNT) for each RAM. We performed analyses using different RAM scores cutoffs, VTEs outcome rates, and effectiveness of anticoagulation to prevent VTEs. The value <.05 was considered statistically significant.

RESULTS

A total of 6094 women were included. Three women had VTEs for an overall rate of 0.04% (N = 3; 95% confidence interval [CI]: 0.01-0.15). The proportion of women categorized as high risk for VTEs were 62% (95% CI: 61-64) for RCOG, 0.8% (95% CI: 0.6-1.0) for Padua, and 94% (95% CI: 94-95) for Caprini. Of the 3 VTEs, the RCOG model classified 1 woman as high risk and Padua model classified 3 women as high risk; whereas the Caprini did not identify any women as high risk. Assuming 100% effectiveness of thromboprophylaxis, the observed NNT was 3838 using RCOG, not able to calculate using Padua (no VTEs cases occurred in the high-risk group, thus none were prevented), and 1927 using Caprini.

CONCLUSION

The rates of VTEs in pregnancy are very low and the available RAMs do not identify most of them. The RCOG and Caprini RAMs would categorize a large proportion of women as high risk and are associated with high NNTs. The Padua RAM appears to have the lowest NNT but missed all the VTEs in our cohort.

摘要

目的

目前对于妊娠期间的血栓预防,尚无共识推荐使用哪种风险分层方法,且大多数现有的静脉血栓栓塞症(VTE)风险评估模型(RAM)并未在妊娠期间得到验证。我们的目的是比较一些最常用于围产期的 VTEs RAM 在我们患者人群中的表现。

研究设计

这是一项对 2015 年和 2016 年在我院分娩的女性进行的回顾性队列研究。我们排除了有 VTE 病史或当前正在接受抗凝治疗的患者。回顾了产前、产时和产后的记录。为了计算 3 种 RAM(Padua、Caprini 和皇家妇产科医师学院(RCOG))入院分娩时的评分,我们收集了 VTE 已知的危险因素数据。主要目的是评估各种 RAM 在预防产后 VTE 方面的表现。我们计算了高危女性的比例,确定了高危和低危人群中的 VTE 病例数,以及计算了每种 RAM 的需要治疗人数(NNT)。我们使用不同的 RAM 评分截断值、VTE 发生率和抗凝预防 VTE 的效果进行了分析。P 值<.05 被认为具有统计学意义。

结果

共有 6094 名女性入组。共有 3 名女性发生 VTE,总发生率为 0.04%(95%置信区间[CI]:0.01-0.15)。RCOG 模型将 62%(95%CI:61-64)的女性归类为 VTE 高危,Padua 模型将 0.8%(95%CI:0.6-1.0)的女性归类为高危,Caprini 模型将 94%(95%CI:94-95)的女性归类为高危。在这 3 例 VTE 中,RCOG 模型将 1 名女性归类为高危,Padua 模型将 3 名女性归类为高危;而 Caprini 模型未将任何女性归类为高危。假设 100%的血栓预防效果,使用 RCOG 的观察 NNT 为 3838,使用 Padua 则无法计算(高危组未发生 VTE 病例,因此无病例被预防),使用 Caprini 则为 1927。

结论

妊娠期间 VTE 的发生率非常低,现有的 RAM 无法识别大多数 VTE。RCOG 和 Caprini RAM 将大量女性归类为高危人群,且与较高的 NNT 相关。Padua RAM 的 NNT 似乎最低,但在我们的队列中错过了所有 VTE 病例。

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引用本文的文献

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Values of Caprini Risk Assessment Scale and D-Dimer for Predicting Venous Thromboembolism During Puerperium.Caprini风险评估量表及D-二聚体在预测产褥期静脉血栓栓塞中的价值。
Int J Womens Health. 2024 Jan 12;16:47-53. doi: 10.2147/IJWH.S443245. eCollection 2024.
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Risk assessment models for venous thromboembolism in pregnancy and in the puerperium: a systematic review.
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BMJ Open. 2022 Oct 12;12(10):e065892. doi: 10.1136/bmjopen-2022-065892.