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利用孕前和孕早期变量预测严重孕产妇发病率的模型的建立和内部验证:加拿大安大略省的一项基于人群的研究。

Development and internal validation of a model predicting severe maternal morbidity using pre-conception and early pregnancy variables: a population-based study in Ontario, Canada.

机构信息

Department of Medicine and Research Institute, McGill University Health Centre, 5252 de Maisonneuve West, 2B.40, Montreal, QC, H4A 3S5, Canada.

Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Purvis Hall, 1020 Pine Ave West, Montreal, QC, H3A 1A2, Canada.

出版信息

BMC Pregnancy Childbirth. 2021 Oct 6;21(1):679. doi: 10.1186/s12884-021-04132-6.

Abstract

BACKGROUND

Improvement in the prediction and prevention of severe maternal morbidity (SMM) - a range of life-threatening conditions during pregnancy, at delivery or within 42 days postpartum - is a public health priority. Reduction of SMM at a population level would be facilitated by early identification and prediction. We sought to develop and internally validate a model to predict maternal end-organ injury or death using variables routinely collected during pre-pregnancy and the early pregnancy period.

METHODS

We performed a population-based cohort study using linked administrative health data in Ontario, Canada, from April 1, 2006 to March 31, 2014. We included women aged 18-60 years with a livebirth or stillbirth, of which one birth was randomly selected per woman. We constructed a clinical prediction model for the primary composite outcome of any maternal end-organ injury or death, arising between 20 weeks' gestation and 42 days after the birth hospital discharge date. Our model included variables collected from 12 months before estimated conception until 19 weeks' gestation. We developed a separate model for parous women to allow for the inclusion of factors from previous pregnancy(ies).

RESULTS

Of 634,290 women, 1969 experienced the primary composite outcome (3.1 per 1000). Predictive factors in the main model included maternal world region of origin, chronic medical conditions, parity, and obstetrical/perinatal issues - with moderate model discrimination (C-statistic 0.68, 95% CI 0.66-0.69). Among 333,435 parous women, the C-statistic was 0.71 (0.69-0.73) in the model using variables from the current (index) pregnancy as well as pre-pregnancy predictors and variables from any previous pregnancy.

CONCLUSIONS

A combination of factors ascertained early in pregnancy through a basic medical history help to identify women at risk for severe morbidity, who may benefit from targeted preventive and surveillance strategies including appropriate specialty-based antenatal care pathways. Further refinement and external validation of this model are warranted and can support evidence-based improvements in clinical practice.

摘要

背景

改善严重孕产妇发病率(SMM)的预测和预防——这是一系列在妊娠、分娩或产后 42 天内危及生命的情况——是公共卫生的重点。通过早期识别和预测,可以促进人群水平上的 SMM 减少。我们试图开发并内部验证一种使用在妊娠前和妊娠早期常规收集的变量来预测母体终末器官损伤或死亡的模型。

方法

我们在加拿大安大略省进行了一项基于人群的队列研究,使用 2006 年 4 月 1 日至 2014 年 3 月 31 日期间的链接行政健康数据。我们纳入了年龄在 18-60 岁之间、活产或死产的女性,每位女性随机选择一次分娩。我们构建了一个用于主要复合结局(任何母体终末器官损伤或死亡,发生在 20 周妊娠至分娩后 42 天之间)的临床预测模型。我们的模型包括从估计受孕前 12 个月到 19 周妊娠期间收集的变量。我们为经产妇建立了一个单独的模型,以允许纳入以前妊娠的因素。

结果

在 634290 名女性中,有 1969 名经历了主要复合结局(每 1000 名 3.1 名)。主要模型中的预测因素包括产妇的世界原籍地区、慢性疾病、产次和产科/围产期问题——具有中等的模型区分度(C 统计量 0.68,95%CI 0.66-0.69)。在 333435 名经产妇中,使用当前(指数)妊娠以及妊娠前预测因素和任何以前妊娠的变量的模型的 C 统计量为 0.71(0.69-0.73)。

结论

通过基本病史在妊娠早期确定的一系列因素有助于识别有严重发病风险的女性,她们可能受益于有针对性的预防和监测策略,包括适当的专科产前护理途径。需要进一步改进和外部验证该模型,并为基于证据的临床实践改进提供支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcd1/8496026/2a6c5e467c86/12884_2021_4132_Fig1_HTML.jpg

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