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Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study

OBJECTIVE: To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women. DESIGN AND SETTING: A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008–13 in...

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Autores principales: Sandström, Anna, Snowden, Jonathan M., Höijer, Jonas, Bottai, Matteo, Wikström, Anna-Karin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6881002/
https://www.ncbi.nlm.nih.gov/pubmed/31774875
http://dx.doi.org/10.1371/journal.pone.0225716
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author Sandström, Anna
Snowden, Jonathan M.
Höijer, Jonas
Bottai, Matteo
Wikström, Anna-Karin
author_facet Sandström, Anna
Snowden, Jonathan M.
Höijer, Jonas
Bottai, Matteo
Wikström, Anna-Karin
author_sort Sandström, Anna
collection PubMed
description OBJECTIVE: To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women. DESIGN AND SETTING: A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008–13 in the Stockholm-Gotland Counties in Sweden. METHODS: Maternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemoglobin and capillary glucose levels) routinely collected at the first visit in antenatal care, constitute the predictive variables. Predictive models for preeclampsia were created by three methods; logistic regression models using 1) pre-specified variables (similar to the Fetal Medicine Foundation model including maternal factors and mean arterial pressure), 2) backward selection starting from the full suite of variables, and 3) a Random forest model using the same candidate variables. The performance of the British National Institute for Health and Care Excellence (NICE) binary risk classification guidelines for preeclampsia was also evaluated. The outcome measures were diagnosis of preeclampsia with delivery <34, <37, and ≥37 weeks’ gestation. RESULTS: A total of 2 773 (4.4%) nulliparous women subsequently developed preeclampsia. The pre-specified variables model was superior the other two models, regarding prediction of preeclampsia with delivery <34 and <37 weeks, both with areas under the curve of 0.68, and sensitivity of 30.6% (95% CI 24.5–37.2) and 29.2% (95% CI 25.2–33.4) at a 10% false positive rate, respectively. The performance of these customizable multivariable models at the chosen false positive rate, was significantly better than the binary NICE-guidelines for preeclampsia with delivery <37 and ≥37 weeks’ gestation. CONCLUSION: Multivariable models in early pregnancy had a modest performance, although providing advantages over the NICE-guidelines, in predicting preeclampsia in nulliparous women. Use of a machine learning algorithm (Random forest) did not result in superior prediction.
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spelling pubmed-68810022019-12-08 Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study Sandström, Anna Snowden, Jonathan M. Höijer, Jonas Bottai, Matteo Wikström, Anna-Karin PLoS One Research Article OBJECTIVE: To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women. DESIGN AND SETTING: A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008–13 in the Stockholm-Gotland Counties in Sweden. METHODS: Maternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemoglobin and capillary glucose levels) routinely collected at the first visit in antenatal care, constitute the predictive variables. Predictive models for preeclampsia were created by three methods; logistic regression models using 1) pre-specified variables (similar to the Fetal Medicine Foundation model including maternal factors and mean arterial pressure), 2) backward selection starting from the full suite of variables, and 3) a Random forest model using the same candidate variables. The performance of the British National Institute for Health and Care Excellence (NICE) binary risk classification guidelines for preeclampsia was also evaluated. The outcome measures were diagnosis of preeclampsia with delivery <34, <37, and ≥37 weeks’ gestation. RESULTS: A total of 2 773 (4.4%) nulliparous women subsequently developed preeclampsia. The pre-specified variables model was superior the other two models, regarding prediction of preeclampsia with delivery <34 and <37 weeks, both with areas under the curve of 0.68, and sensitivity of 30.6% (95% CI 24.5–37.2) and 29.2% (95% CI 25.2–33.4) at a 10% false positive rate, respectively. The performance of these customizable multivariable models at the chosen false positive rate, was significantly better than the binary NICE-guidelines for preeclampsia with delivery <37 and ≥37 weeks’ gestation. CONCLUSION: Multivariable models in early pregnancy had a modest performance, although providing advantages over the NICE-guidelines, in predicting preeclampsia in nulliparous women. Use of a machine learning algorithm (Random forest) did not result in superior prediction. Public Library of Science 2019-11-27 /pmc/articles/PMC6881002/ /pubmed/31774875 http://dx.doi.org/10.1371/journal.pone.0225716 Text en © 2019 Sandström et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sandström, Anna
Snowden, Jonathan M.
Höijer, Jonas
Bottai, Matteo
Wikström, Anna-Karin
Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study
title Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study
title_full Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study
title_fullStr Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study
title_full_unstemmed Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study
title_short Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study
title_sort clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: a population based cohort study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6881002/
https://www.ncbi.nlm.nih.gov/pubmed/31774875
http://dx.doi.org/10.1371/journal.pone.0225716
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