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Development and internal validation of a model predicting severe maternal morbidity using variables available pre-conception and in early pregnancy: a population-based study
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  • Natalie Dayan,
  • Gabriel Shapirio,
  • Jin Luo,
  • Jun Guan,
  • Deshayne Fell,
  • Carl Laskin,
  • Olga Basso,
  • Alison Park,
  • Joel Ray
Natalie Dayan
McGill University Health Centre
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Gabriel Shapirio
McGill University
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Jin Luo
Institute for Clinical Evaluative Sciences
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Jun Guan
ICES
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Deshayne Fell
University of Ottawa
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Carl Laskin
University of Toronto
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Olga Basso
McGill University
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Alison Park
ICES
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Joel Ray
Departments of Medicine and Obstetrics and Gynaecology, St. Michael’s Hospital, University of Toronto, Ontario, Canada
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Abstract

Objective: To improve the prediction of maternal end-organ injury or death using routinely-collected variables from the pre-pregnancy and the early pregnancy period. Design: Population-based cohort study using linked administrative health data. Setting: Ontario, Canada, April 1, 2006 to March 31, 2014. Sample: Women aged 18-60 years with a livebirth or stillbirth, of which one birth was randomly selected per woman. Methods and main outcome measures: We constructed a CPM 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 CPM included variables collected from 12 months before estimated conception until 19 weeks’ gestation. We developed a separate CPM 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 CPM 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 of this model would enable clinical use.