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Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors

Steven Wambua, Francesca L. Crowe, Shakila Thangaratinam, Dermot O’Reilly, Colin McCowan, Sinead Brophy Orcid Logo, Christopher Yau, Krishnarajah Nirantharakumar, Richard D. Riley, Kym I. E. Snell, (the MuM-PreDiCT Group)

BMC Medicine, Volume: 23, Issue: 1

Swansea University Author: Sinead Brophy Orcid Logo

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DOI (Published version): 10.1186/s12916-025-04229-1

Abstract

BackgroundEach year, over 700,000 pregnancies occur in the UK, with up to 10% affected by complications such as hypertensive disorders of pregnancy and gestational diabetes mellitus. Pregnancy-related complications and reproductive factors are associated with an increased risk of cardiovascular dise...

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Published in: BMC Medicine
ISSN: 1741-7015
Published: Springer Science and Business Media LLC 2025
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Our aim was to determine whether adding pregnancy factors to a prediction model with established CVD risk factors improves 10-year risk prediction of CVD in postpartum women, using QRISK&#xAE;-3 as a benchmark model.MethodsWe used a population-based retrospective cohort of women aged 15 to 49 who had been pregnant from the Clinical Practice Research Datalink (CPRD) primary care database. Women who were CVD-free were followed from 6 months postpartum. We evaluated the performance of QRISK&#xAE;-3 and updated the risk prediction model using established risk factors for CVD from QRISK&#xAE;-3 and additional risk factors specific to pregnancy. Models were developed using Cox-proportional hazards regression for CVD within 10 years. Models were evaluated and compared using measures of overall model fit, calibration, discrimination and clinical utility.ResultsAmong 567,667 eligible women, 2175 (0.38%) experienced a CVD event within 10 years. The median follow-up was 4 years. Of the additional pregnancy factors, gestational hypertension, preeclampsia, miscarriage, stillbirth, postnatal depression, gravidity, endometriosis and polycystic ovary syndrome remained associated with CVD after adjusting for other established risk factors of CVD. Adding pregnancy factors to those from QRISK&#xAE;-3 led to marginal improvements in model performance (QRISK&#xAE;-3 C-statistic: 0.703 (95% CI 0.687 to 0.718), updated model C-statistic: 0.726 (95% CI 0.711 to 0.740) Although calibration did not improve overall, subgroup analysis showed better calibration in women with a history of pre-eclampsia, postnatal depression and preterm birth using the updated model. The clinical utility was improved for updated models.ConclusionsThe updated risk prediction models resulted in marginal improvement in discrimination and calibration compared to QRISK&#xAE;-3 in postpartum women. This could be due to the known association of pregnancy-related complications with established risk factors of CVD. Although the overall predictive performance and calibration of the updated models was similar, the updated model resulted in better clinical utility.</abstract><type>Journal Article</type><journal>BMC Medicine</journal><volume>23</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>1741-7015</issnElectronic><keywords>Risk prediction; Pregnancy complications;; QRISK&#xAE;-3; Cardiovascular disease</keywords><publishedDay>29</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-08-29</publishedDate><doi>10.1186/s12916-025-04229-1</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>This work is funded by the Strategic Priority Fund &#x201C;Tackling multimorbidity at scale&#x201D; programme (grant number MR/W014432/1) delivered by the Medical Research Council and the National Institute for Health Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council. SW PhD studentship is funded by the British Heart Foundation (BHF) Data Science Centre (BHF grant number SP/19/3/34678, awarded to Health Data Research (HDR)). His PhD is also supported through the HDR-UK-Turing Wellcome PhD Programme. RR and KS are supported by funding from the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham.</funders><projectreference/><lastEdited>2025-11-17T14:47:23.2660986</lastEdited><Created>2025-10-02T19:33:27.1264879</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Steven</firstname><surname>Wambua</surname><order>1</order></author><author><firstname>Francesca L.</firstname><surname>Crowe</surname><order>2</order></author><author><firstname>Shakila</firstname><surname>Thangaratinam</surname><order>3</order></author><author><firstname>Dermot</firstname><surname>O&#x2019;Reilly</surname><order>4</order></author><author><firstname>Colin</firstname><surname>McCowan</surname><order>5</order></author><author><firstname>Sinead</firstname><surname>Brophy</surname><orcid>0000-0001-7417-2858</orcid><order>6</order></author><author><firstname>Christopher</firstname><surname>Yau</surname><order>7</order></author><author><firstname>Krishnarajah</firstname><surname>Nirantharakumar</surname><order>8</order></author><author><firstname>Richard D.</firstname><surname>Riley</surname><order>9</order></author><author><firstname>Kym I. E.</firstname><surname>Snell</surname><order>10</order></author><author><firstname>(the MuM-PreDiCT</firstname><surname>Group)</surname><order>11</order></author></authors><documents><document><filename>70563__35650__f5d5da9f656742b9bac6e5c446847715.pdf</filename><originalFilename>70563.VoR.pdf</originalFilename><uploaded>2025-11-17T14:45:15.6698390</uploaded><type>Output</type><contentLength>1285952</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-11-17T14:47:23.2660986 v2 70563 2025-10-02 Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors 84f5661b35a729f55047f9e793d8798b 0000-0001-7417-2858 Sinead Brophy Sinead Brophy true false 2025-10-02 MEDS BackgroundEach year, over 700,000 pregnancies occur in the UK, with up to 10% affected by complications such as hypertensive disorders of pregnancy and gestational diabetes mellitus. Pregnancy-related complications and reproductive factors are associated with an increased risk of cardiovascular disease (CVD) later in life. Our aim was to determine whether adding pregnancy factors to a prediction model with established CVD risk factors improves 10-year risk prediction of CVD in postpartum women, using QRISK®-3 as a benchmark model.MethodsWe used a population-based retrospective cohort of women aged 15 to 49 who had been pregnant from the Clinical Practice Research Datalink (CPRD) primary care database. Women who were CVD-free were followed from 6 months postpartum. We evaluated the performance of QRISK®-3 and updated the risk prediction model using established risk factors for CVD from QRISK®-3 and additional risk factors specific to pregnancy. Models were developed using Cox-proportional hazards regression for CVD within 10 years. Models were evaluated and compared using measures of overall model fit, calibration, discrimination and clinical utility.ResultsAmong 567,667 eligible women, 2175 (0.38%) experienced a CVD event within 10 years. The median follow-up was 4 years. Of the additional pregnancy factors, gestational hypertension, preeclampsia, miscarriage, stillbirth, postnatal depression, gravidity, endometriosis and polycystic ovary syndrome remained associated with CVD after adjusting for other established risk factors of CVD. Adding pregnancy factors to those from QRISK®-3 led to marginal improvements in model performance (QRISK®-3 C-statistic: 0.703 (95% CI 0.687 to 0.718), updated model C-statistic: 0.726 (95% CI 0.711 to 0.740) Although calibration did not improve overall, subgroup analysis showed better calibration in women with a history of pre-eclampsia, postnatal depression and preterm birth using the updated model. The clinical utility was improved for updated models.ConclusionsThe updated risk prediction models resulted in marginal improvement in discrimination and calibration compared to QRISK®-3 in postpartum women. This could be due to the known association of pregnancy-related complications with established risk factors of CVD. Although the overall predictive performance and calibration of the updated models was similar, the updated model resulted in better clinical utility. Journal Article BMC Medicine 23 1 Springer Science and Business Media LLC 1741-7015 Risk prediction; Pregnancy complications;; QRISK®-3; Cardiovascular disease 29 8 2025 2025-08-29 10.1186/s12916-025-04229-1 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Another institution paid the OA fee This work is funded by the Strategic Priority Fund “Tackling multimorbidity at scale” programme (grant number MR/W014432/1) delivered by the Medical Research Council and the National Institute for Health Research in partnership with the Economic and Social Research Council and in collaboration with the Engineering and Physical Sciences Research Council. SW PhD studentship is funded by the British Heart Foundation (BHF) Data Science Centre (BHF grant number SP/19/3/34678, awarded to Health Data Research (HDR)). His PhD is also supported through the HDR-UK-Turing Wellcome PhD Programme. RR and KS are supported by funding from the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham. 2025-11-17T14:47:23.2660986 2025-10-02T19:33:27.1264879 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Steven Wambua 1 Francesca L. Crowe 2 Shakila Thangaratinam 3 Dermot O’Reilly 4 Colin McCowan 5 Sinead Brophy 0000-0001-7417-2858 6 Christopher Yau 7 Krishnarajah Nirantharakumar 8 Richard D. Riley 9 Kym I. E. Snell 10 (the MuM-PreDiCT Group) 11 70563__35650__f5d5da9f656742b9bac6e5c446847715.pdf 70563.VoR.pdf 2025-11-17T14:45:15.6698390 Output 1285952 application/pdf Version of Record true © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License. true eng http://creativecommons.org/licenses/by/4.0/
title Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors
spellingShingle Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors
Sinead Brophy
title_short Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors
title_full Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors
title_fullStr Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors
title_full_unstemmed Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors
title_sort Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors
author_id_str_mv 84f5661b35a729f55047f9e793d8798b
author_id_fullname_str_mv 84f5661b35a729f55047f9e793d8798b_***_Sinead Brophy
author Sinead Brophy
author2 Steven Wambua
Francesca L. Crowe
Shakila Thangaratinam
Dermot O’Reilly
Colin McCowan
Sinead Brophy
Christopher Yau
Krishnarajah Nirantharakumar
Richard D. Riley
Kym I. E. Snell
(the MuM-PreDiCT Group)
format Journal article
container_title BMC Medicine
container_volume 23
container_issue 1
publishDate 2025
institution Swansea University
issn 1741-7015
doi_str_mv 10.1186/s12916-025-04229-1
publisher Springer Science and Business Media LLC
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
document_store_str 1
active_str 0
description BackgroundEach year, over 700,000 pregnancies occur in the UK, with up to 10% affected by complications such as hypertensive disorders of pregnancy and gestational diabetes mellitus. Pregnancy-related complications and reproductive factors are associated with an increased risk of cardiovascular disease (CVD) later in life. Our aim was to determine whether adding pregnancy factors to a prediction model with established CVD risk factors improves 10-year risk prediction of CVD in postpartum women, using QRISK®-3 as a benchmark model.MethodsWe used a population-based retrospective cohort of women aged 15 to 49 who had been pregnant from the Clinical Practice Research Datalink (CPRD) primary care database. Women who were CVD-free were followed from 6 months postpartum. We evaluated the performance of QRISK®-3 and updated the risk prediction model using established risk factors for CVD from QRISK®-3 and additional risk factors specific to pregnancy. Models were developed using Cox-proportional hazards regression for CVD within 10 years. Models were evaluated and compared using measures of overall model fit, calibration, discrimination and clinical utility.ResultsAmong 567,667 eligible women, 2175 (0.38%) experienced a CVD event within 10 years. The median follow-up was 4 years. Of the additional pregnancy factors, gestational hypertension, preeclampsia, miscarriage, stillbirth, postnatal depression, gravidity, endometriosis and polycystic ovary syndrome remained associated with CVD after adjusting for other established risk factors of CVD. Adding pregnancy factors to those from QRISK®-3 led to marginal improvements in model performance (QRISK®-3 C-statistic: 0.703 (95% CI 0.687 to 0.718), updated model C-statistic: 0.726 (95% CI 0.711 to 0.740) Although calibration did not improve overall, subgroup analysis showed better calibration in women with a history of pre-eclampsia, postnatal depression and preterm birth using the updated model. The clinical utility was improved for updated models.ConclusionsThe updated risk prediction models resulted in marginal improvement in discrimination and calibration compared to QRISK®-3 in postpartum women. This could be due to the known association of pregnancy-related complications with established risk factors of CVD. Although the overall predictive performance and calibration of the updated models was similar, the updated model resulted in better clinical utility.
published_date 2025-08-29T05:31:09Z
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score 11.089572

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