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Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes
INTRODUCTION: Gestational diabetes (GDM) is a common yet highly heterogeneous condition. The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with GDM would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing wom...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646337/ https://www.ncbi.nlm.nih.gov/pubmed/33154055 http://dx.doi.org/10.1136/bmjopen-2020-038845 |
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author | Cooray, Shamil D. Boyle, Jacqueline A. Soldatos, Georgia Zamora, Javier Fernández Félix, Borja M. Allotey, John Thangaratinam, Shakila Teede, Helena J. |
author_facet | Cooray, Shamil D. Boyle, Jacqueline A. Soldatos, Georgia Zamora, Javier Fernández Félix, Borja M. Allotey, John Thangaratinam, Shakila Teede, Helena J. |
author_sort | Cooray, Shamil D. |
collection | PubMed |
description | INTRODUCTION: Gestational diabetes (GDM) is a common yet highly heterogeneous condition. The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with GDM would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. The Prediction for Risk-Stratified care for women with GDM (PeRSonal GDM) study will develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM. METHODS AND ANALYSIS: We undertook formative research to conceptualise and design the prediction model. Informed by these findings, we will conduct a model development and validation study using a retrospective cohort design with participant data collected as part of routine clinical care across three hospitals. The study will include all pregnancies resulting in births from 1 July 2017 to 31 December 2018 coded for a diagnosis of GDM (estimated sample size 2430 pregnancies). We will use a temporal split-sample development and validation strategy. A multivariable logistic regression model will be fitted. The performance of this model will be assessed, and the validated model will also be evaluated using decision curve analysis. Finally, we will explore modes of model presentation suited to clinical use, including electronic risk calculators. ETHICS AND DISSEMINATION: This study was approved by the Human Research Ethics Committee of Monash Health (RES-19–0000713 L). We will disseminate results via presentations at scientific meetings and publication in peer-reviewed journals. TRIAL REGISTRATION DETAILS: Systematic review proceeding this work was registered on PROSPERO (CRD42019115223) and the study was registered on the Australian and New Zealand Clinical Trials Registry (ACTRN12620000915954); Pre-results. |
format | Online Article Text |
id | pubmed-7646337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-76463372020-11-10 Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes Cooray, Shamil D. Boyle, Jacqueline A. Soldatos, Georgia Zamora, Javier Fernández Félix, Borja M. Allotey, John Thangaratinam, Shakila Teede, Helena J. BMJ Open Diabetes and Endocrinology INTRODUCTION: Gestational diabetes (GDM) is a common yet highly heterogeneous condition. The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with GDM would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. The Prediction for Risk-Stratified care for women with GDM (PeRSonal GDM) study will develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM. METHODS AND ANALYSIS: We undertook formative research to conceptualise and design the prediction model. Informed by these findings, we will conduct a model development and validation study using a retrospective cohort design with participant data collected as part of routine clinical care across three hospitals. The study will include all pregnancies resulting in births from 1 July 2017 to 31 December 2018 coded for a diagnosis of GDM (estimated sample size 2430 pregnancies). We will use a temporal split-sample development and validation strategy. A multivariable logistic regression model will be fitted. The performance of this model will be assessed, and the validated model will also be evaluated using decision curve analysis. Finally, we will explore modes of model presentation suited to clinical use, including electronic risk calculators. ETHICS AND DISSEMINATION: This study was approved by the Human Research Ethics Committee of Monash Health (RES-19–0000713 L). We will disseminate results via presentations at scientific meetings and publication in peer-reviewed journals. TRIAL REGISTRATION DETAILS: Systematic review proceeding this work was registered on PROSPERO (CRD42019115223) and the study was registered on the Australian and New Zealand Clinical Trials Registry (ACTRN12620000915954); Pre-results. BMJ Publishing Group 2020-11-05 /pmc/articles/PMC7646337/ /pubmed/33154055 http://dx.doi.org/10.1136/bmjopen-2020-038845 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Diabetes and Endocrinology Cooray, Shamil D. Boyle, Jacqueline A. Soldatos, Georgia Zamora, Javier Fernández Félix, Borja M. Allotey, John Thangaratinam, Shakila Teede, Helena J. Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes |
title | Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes |
title_full | Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes |
title_fullStr | Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes |
title_full_unstemmed | Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes |
title_short | Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes |
title_sort | protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes |
topic | Diabetes and Endocrinology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646337/ https://www.ncbi.nlm.nih.gov/pubmed/33154055 http://dx.doi.org/10.1136/bmjopen-2020-038845 |
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