Reducing severe adverse events for women during pregnancy through prediction modelling

产妇特定的早期预警评分(MOEWS)是一种简单的工具，旨在使用静态生理标记物预测妇女恶化的可能性。然而，有证据表明，传统的风险评估可能低估了与怀孕相关的恶化的速度和深度，而对这些措施的依赖导致了一些迹象被忽视的情况。随着NHS转向使用电子健康记录，这是开发能够及早预测产妇健康恶化并降低产妇发病率和死亡率的可靠模型的理想机会。尽管存在产科预测模型，但很少有独立于数据进行评估的产科预测模型(外部验证)，更少的是在关键的临床亚组中进行评估。建议在开发新的预测模型之前，对现有模型进行外部验证并考虑更新。尽管存在关于如何在开发期间处理丢失数据的指导方针，但在如何在模型实现中处理个人的丢失数据方面存在着相当大的差异。这项研究的目的是确定目前可用的MOEWS在预测孕产妇死亡和孕产妇发病率方面的有效性和适宜性，并调查如何改进现有的方法。

Maternal-specific Early Warning Scores (MOEWSs) are simple tools designed to predict the likelihood of a women deteriorating using static physiological markers. However, evidence has shown the rapidity and depth of deterioration in relation to pregnancy can be under-predicted by traditional risk assessment, and reliance on these measures has led to instances where signs were ignored. As the NHS moves to the use of electronic health records, this is the ideal opportunity to develop robust models that can predict deteriorating maternal health early and reduce maternal morbidity and mortality.Although obstetric prediction models exist, very few have been evaluated on data independent of that on which they were developed (external validation), fewer have been assessed in key clinical subgroups. It is recommended to externally validate and consider updating an existing model before developing a new prediction model. Although guidelines exist on how to handle missing data during development, there is considerable variation in how to handle an individual's missing data at model implementation. The aim of this study is to determine the effectiveness and suitability of currently available MOEWSs at predicting maternal death and maternal morbidity and to investigate how current methods can be improved.