Abstract：
Low-dose aspirin treatment reduces the risk of preeclampsia among high-risk pregnant women. Internationally, several first-trimester risk-calculation methods are applied.
This study aimed to assess the costs and benefits of different first-trimester preeclampsia risk estimation algorithms: EXPECT (an algorithmic prediction model based on maternal characteristics), National Institute for Health and Care Excellence (a checklist of risk factors), and the Fetal Medicine Foundation (a prediction model using additional uterine artery Doppler measurement and laboratory testing) models, coupled with low-dose aspirin treatment, in comparison with no risk assessment.
We constructed a decision analytical model estimating the number of cases of preeclampsia with each strategy and the costs of risk assessment for preeclampsia and early aspirin treatment, expressed in euros (€) in a hypothetical population of 100,000 women. We performed 1-way sensitivity analyses to assess the impact of adherence rates on model outcomes.
Application of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models results in respectively 1.98%, 2.55%, and 1.90% of the women developing preeclampsia, as opposed to 3.00% of women in the case of no risk assessment. Overall, the net financial benefits of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models relative to no risk assessment are €144, €43, and €38 per patient, respectively. The respective percentages of women receiving aspirin treatment are 18.6%, 10.2%, and 6.0% for the 3 risk assessment methods.
The EXPECT and Fetal Medicine Foundation model are comparable with regard to numbers of prevented preeclampsia cases, and both are superior to the National Institute for Health and Care Excellence model and to no risk assessment. EXPECT is less resource-demanding and results in the highest cost savings, but also requires the highest number of women to be treated with aspirin. When deciding which strategy is preferable, cost savings and easier use have to be weighed against the degree of overtreatment, although low-dose aspirin has no clear disadvantages during pregnancy.
This study aimed to assess the costs and benefits of different first-trimester preeclampsia risk estimation algorithms: EXPECT (an algorithmic prediction model based on maternal characteristics), National Institute for Health and Care Excellence (a checklist of risk factors), and the Fetal Medicine Foundation (a prediction model using additional uterine artery Doppler measurement and laboratory testing) models, coupled with low-dose aspirin treatment, in comparison with no risk assessment.
We constructed a decision analytical model estimating the number of cases of preeclampsia with each strategy and the costs of risk assessment for preeclampsia and early aspirin treatment, expressed in euros (€) in a hypothetical population of 100,000 women. We performed 1-way sensitivity analyses to assess the impact of adherence rates on model outcomes.
Application of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models results in respectively 1.98%, 2.55%, and 1.90% of the women developing preeclampsia, as opposed to 3.00% of women in the case of no risk assessment. Overall, the net financial benefits of the EXPECT, National Institute for Health and Care Excellence, and Fetal Medicine Foundation models relative to no risk assessment are €144, €43, and €38 per patient, respectively. The respective percentages of women receiving aspirin treatment are 18.6%, 10.2%, and 6.0% for the 3 risk assessment methods.
The EXPECT and Fetal Medicine Foundation model are comparable with regard to numbers of prevented preeclampsia cases, and both are superior to the National Institute for Health and Care Excellence model and to no risk assessment. EXPECT is less resource-demanding and results in the highest cost savings, but also requires the highest number of women to be treated with aspirin. When deciding which strategy is preferable, cost savings and easier use have to be weighed against the degree of overtreatment, although low-dose aspirin has no clear disadvantages during pregnancy.
摘要：
背景：低剂量阿司匹林治疗可降低高危孕妇先兆子痫（PE）的风险。国际上,几个前三个月的风险计算评估是可用的.
目的：评估不同的妊娠早期PE风险估计算法的成本和收益：EXPECT（基于母体特征的算法预测模型），NICE（风险因素清单）和胎儿医学基金会（使用额外的子宫动脉多普勒测量和实验室测试的预测模型），与低剂量阿司匹林治疗相比，没有风险评估。
方法：我们构建了一个决策分析模型，估计每个策略中的PE病例数以及PE和早期阿司匹林治疗的风险评估成本，以欧元（€）表示，在假设的100.000名女性人群中。我们进行了单向敏感性分析，以评估依从率对模型结果的影响。
结果：预期的应用，NICE和FMF将分别导致1.98%，2.55%和1.90%的女性发展PE，相比之下，3.00%的女性在没有风险评估的情况下。总体而言，预期的净财务收益，NICE和FMF与无风险评估相比，每位患者分别为144欧元，43欧元和38欧元，分别。接受阿司匹林治疗的女性比例为18.6%，三种风险评估方法分别为10.2%和6.0%。
结论：EXPECT和FMF在预防的PE数量方面具有可比性，并且均优于NICE或无风险评估。预计对资源的要求较低，并节省了最高的成本，但也需要最多的女性接受阿司匹林治疗。当决定哪种策略更可取时，成本节约和更容易使用必须权衡过度治疗的程度，虽然低剂量阿司匹林在怀孕期间没有明显的缺点。
目的：评估不同的妊娠早期PE风险估计算法的成本和收益：EXPECT（基于母体特征的算法预测模型），NICE（风险因素清单）和胎儿医学基金会（使用额外的子宫动脉多普勒测量和实验室测试的预测模型），与低剂量阿司匹林治疗相比，没有风险评估。
方法：我们构建了一个决策分析模型，估计每个策略中的PE病例数以及PE和早期阿司匹林治疗的风险评估成本，以欧元（€）表示，在假设的100.000名女性人群中。我们进行了单向敏感性分析，以评估依从率对模型结果的影响。
结果：预期的应用，NICE和FMF将分别导致1.98%，2.55%和1.90%的女性发展PE，相比之下，3.00%的女性在没有风险评估的情况下。总体而言，预期的净财务收益，NICE和FMF与无风险评估相比，每位患者分别为144欧元，43欧元和38欧元，分别。接受阿司匹林治疗的女性比例为18.6%，三种风险评估方法分别为10.2%和6.0%。
结论：EXPECT和FMF在预防的PE数量方面具有可比性，并且均优于NICE或无风险评估。预计对资源的要求较低，并节省了最高的成本，但也需要最多的女性接受阿司匹林治疗。当决定哪种策略更可取时，成本节约和更容易使用必须权衡过度治疗的程度，虽然低剂量阿司匹林在怀孕期间没有明显的缺点。
