Abstract

BackgroundImprovement 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.MethodsWe 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).ResultsOf 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.ConclusionsA 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.

Highlights

  • Improvement in the prediction and prevention of severe maternal morbidity (SMM) - a range of lifethreatening conditions during pregnancy, at delivery or within 42 days postpartum - is a public health priority

  • As the focus in industrialized countries such as Canada has shifted towards ‘near miss’ events as a means to improving the health and quality of care for pregnant women [5], prediction of SMM has been identified as a critical research gap in obstetrics [4]

  • Population and data sources All women with a pregnancy lasting beyond 20 weeks’ gestation, and who delivered within an Ontario hospital between April 1, 2006 and March 31, 2014, were identified within the Better Outcomes Registry & Network (BORN) databases [16]

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Summary

Introduction

Improvement in the prediction and prevention of severe maternal morbidity (SMM) - a range of lifethreatening conditions during pregnancy, at delivery or within 42 days postpartum - is a public health priority. Severe maternal morbidity (SMM) covers a range of conditions along the continuum to maternal death during pregnancy or within 42 days after delivery [1]. Maternal morbidity is a substantial public health concern [2] whose incidence is rising in Canada and the US [3] These patterns are driven by multiple risk factors including delaying childbearing, use of assisted reproductive technologies, rising rates of obesity, and Caesarean delivery [4]. A combination of such factors may reliably predict its onset, enabling evidencebased and rational early triage of high-risk women for enhanced surveillance and subspecialty-based care

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