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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
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 identifi...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496026/ https://www.ncbi.nlm.nih.gov/pubmed/34615477 http://dx.doi.org/10.1186/s12884-021-04132-6 |
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author | Dayan, Natalie Shapiro, Gabriel D. Luo, Jin Guan, Jun Fell, Deshayne B. Laskin, Carl A. Basso, Olga Park, Alison L. Ray, Joel G. |
author_facet | Dayan, Natalie Shapiro, Gabriel D. Luo, Jin Guan, Jun Fell, Deshayne B. Laskin, Carl A. Basso, Olga Park, Alison L. Ray, Joel G. |
author_sort | Dayan, Natalie |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-021-04132-6. |
format | Online Article Text |
id | pubmed-8496026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84960262021-10-07 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 Dayan, Natalie Shapiro, Gabriel D. Luo, Jin Guan, Jun Fell, Deshayne B. Laskin, Carl A. Basso, Olga Park, Alison L. Ray, Joel G. BMC Pregnancy Childbirth Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-021-04132-6. BioMed Central 2021-10-06 /pmc/articles/PMC8496026/ /pubmed/34615477 http://dx.doi.org/10.1186/s12884-021-04132-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Dayan, Natalie Shapiro, Gabriel D. Luo, Jin Guan, Jun Fell, Deshayne B. Laskin, Carl A. Basso, Olga Park, Alison L. Ray, Joel G. 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496026/ https://www.ncbi.nlm.nih.gov/pubmed/34615477 http://dx.doi.org/10.1186/s12884-021-04132-6 |
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